Bio


Dr. Xing is currently the Jacob Haimson & Sarah S. Donaldson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. He also holds affiliate faculty positions in Department of Electrical engineering, Bio-X and Molecular Imaging Program at Stanford. Dr. Xing’s research has been focused on artificial intelligence in medicine, medical imaging, treatment planning, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. He has made unique and significant contributions to each of the above areas. Dr. Xing is an author on more than 400 peer reviewed publications, a co-inventor on many issued and pending patents, and a co- investigator or principal investigator on numerous NIH, NSF, DOD, RSNA, ACS and many corporate grants. He is a fellow of AAPM (American Association of Physicists in Medicine), ASTRO (American Society for Radiation Oncology), and AIMBE (American Institute for Medical and Biological Engineering). He has received numerous awards, such as Google Faculty Scholar Award and E. H. Quimby Lifetime Achievement Awards from AAPM.

Administrative Appointments


  • Associate Editor, Medical Physics Journal (2022 - Present)
  • Associate Editor, Medical Physics Journal (2003 - 2008)
  • Member of international advisory board, Physics in Medicine and Bilogy (2008 - Present)
  • Member of Clinical Research and Cancer Epidemiology (CCE) Committee, American Cancer Society (2006 - Present)
  • Member of ZRG1 (Quick Trials on Imaging and Image-Guided Intervention) section, National Institute of Health (2008 - Present)
  • Director of Medical Physics Division, Department of Radiation Oncology, Stanford University (2009 - Present)
  • Director of Radiation Physics Division, Department of Radiation Oncology, Stanford University (2010 - Present)
  • Member of Senior Editorial Board, American Journal of Nuclear Medicine and Molecular Imaging (2010 - Present)
  • Member of Editorial Board, Journal of Gastrointestinal Oncology (2010 - Present)

Honors & Awards


  • Best of Medical Physics in Imaging, AAPM (2017)
  • Basic Science Award, ASTRO (2013)
  • Fellow, American Association of Physicists in Medicine (AAPM) (2012)
  • Best of Physics, American Association of Physicists in Medicine (2015)
  • Fellow, American Institute for Medical and Biological Engineering (AIMBE) (2016)
  • Google Faculty Research Award, Google Inc. (2016)
  • Concept Award for Breast Cancer Research, Department of Defense (2001)
  • Research Scholar Award, American Cancer Society (2001)
  • Basic Science Investigator Award, American Society of Therapeutic Radiology (ASTRO). (2002)
  • Research Scholar of 2005, American Cancer Society (2005)

Professional Education


  • PhD, Johns Hopkins University, Physics (1992)

Community and International Work


  • Clinical implementation of intensity modulated radiation therapy

    Partnering Organization(s)

    American Association of Physicists in Medicine (AAPM)

    Ongoing Project

    Yes

    Opportunities for Student Involvement

    Yes

Current Research and Scholarly Interests


Artificial intelligence in medicine
Medical imaging (instrumentation, image reconstruction and clinical applications)
Biologically conformal radiation therapy (BCRT)
Metabolic imaging (MRSI, PET/CT) for tumor delineation and assessment of therapeutic response;
Treatment planning and clinical decision-making
Radiobiology study using molecular imaging (small animal PET, CT, MRI, optical);

Clinical Trials


  • Cervical Nodal Mets in Squamous Cell Carcinoma of H&N - MRI, FDG-PET, & Histopathologic Correlation Not Recruiting

    The purpose of this study is to determine the value of novel non-invasive medical imaging methods for detecting the spread of head and neck squamous cell carcinoma to the lymph nodes in the neck by comparing their results to findings at the time of surgery.

    Stanford is currently not accepting patients for this trial. For more information, please contact Quynh-Thu Le, (650) 498 - 6184.

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  • Endoscopic Capillary Oximetry for Tumor Diagnosis in Head and Neck Cancer Not Recruiting

    Endoscopy is a standard part of the evaluation of patients with head and neck cancer used for determining the extent of tumor involvement. However, not all areas involved by tumor are apparent visually. Preliminary results indicate that compared with normal tissues, tumors have abnormal levels of capillary oxygenation. The purpose of this study is to determine the ability of non-pulsatile visible light tissue oxygen monitoring to differentiate normal and tumor tissue based on capillary oxygenation during endoscopy Should this be possible, this method could be used to mark tumor extent and invasion, even when that invasion is up to 5mm blow the tissue surface.

    Stanford is currently not accepting patients for this trial. For more information, please contact Peter Maxim, (650) 724 - 3018.

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  • Indirect Magnetic Resonance Lymphangiography of the Head and Neck Region Using Conventional Gadolinium-based Contrast Not Recruiting

    To determine the ability of magnetic resonance lymphangiography using conventional gadolinium injected directly into the tumor site and PET scan in detecting microscopic nodal metastasis in patients with newly diagnosed H\&N cancers

    Stanford is currently not accepting patients for this trial. For more information, please contact Bill Loo, (650) 736 - 7143.

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  • Phase I Dose Escalation of Stereotactic Radiosurgical Boost for Locally Advanced Esophageal Cancer Not Recruiting

    To study the safety and feasibility of stereotactic radiation dose escalation following neoadjuvant chemotherapy with concurrent conventionally fractionated radiation, by evaluating the acute and late toxicity of treatment.

    Stanford is currently not accepting patients for this trial. For more information, please contact Laurie Ann Columbo, (650) 736 - 0792.

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  • Real-Time kV Imaging vs. Real-Time 3D Patient Surface Tracking for Head & Neck Cancer Not Recruiting

    To determine if a new optical system that can track a patient's movement during treatment can be used to measure motion and allow for motion adjustments in order to decrease the amount of healthy tissue that receives radiation without limiting our ability to cure cancers using radiation.

    Stanford is currently not accepting patients for this trial. For more information, please contact Brian Khong, (650) 725 - 4777.

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  • Real-Time MV/kV Image Guided Radiation Therapy Not Recruiting

    In current radiation therapy, imaging (typically, cone beam CT imaging or two orthogonal X-ray projection imaging) is done for patient setup before radiation dose delivery. Dose delivery typically takes 2 to 5 minutes depending on the delivery technique used for treatment. A tumor target may change its position during the dose delivery process. The goal of this project is develop a real-time imaging strategy to monitor the tumor position during dose delivery and evaluate its potential clinical impact.

    Stanford is currently not accepting patients for this trial. For more information, please contact Lei Xing, 650-498-7896.

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2024-25 Courses


Stanford Advisees


Graduate and Fellowship Programs


  • Biomedical Data Science (Phd Program)
  • Biomedical Data Science (Masters Program)

All Publications


  • Interpretable discovery of patterns in tabular data via spatially semantic topographic maps. Nature biomedical engineering Yan, R., Islam, M. T., Xing, L. 2024

    Abstract

    Tabular data-rows of samples and columns of sample features-are ubiquitously used across disciplines. Yet the tabular representation makes it difficult to discover underlying associations in the data and thus hinders their analysis and the discovery of useful patterns. Here we report a broadly applicable strategy for unravelling intertwined relationships in tabular data by reconfiguring each data sample into a spatially semantic 2D topographic map, which we refer to as TabMap. A TabMap preserves the original feature values as pixel intensities, with the relationships among the features spatially encoded in the map (the strength of two inter-related features correlates with their distance on the map). TabMap makes it possible to apply 2D convolutional neural networks to extract association patterns in the data to aid data analysis, and offers interpretability by ranking features according to importance. We show the superior predictive performance of TabMap by applying it to 12 datasets across a wide range of biomedical applications, including disease diagnosis, human activity recognition, microbial identification and the analysis of quantitative structure-activity relationships.

    View details for DOI 10.1038/s41551-024-01268-6

    View details for PubMedID 39407015

    View details for PubMedCentralID 6443823

  • Localization and recognition of human action in 3D using transformers. Communications engineering Sun, J., Huang, L., Wang, H., Zheng, C., Qiu, J., Islam, M. T., Xie, E., Zhou, B., Xing, L., Chandrasekaran, A., Black, M. J. 2024; 3 (1): 125

    Abstract

    Understanding a person's behavior from their 3D motion sequence is a fundamental problem in computer vision with many applications. An important component of this problem is 3D action localization, which involves recognizing what actions a person is performing, and when the actions occur in the sequence. To promote the progress of the 3D action localization community, we introduce a new, challenging, and more complex benchmark dataset, BABEL-TAL (BT), for 3D action localization. Important baselines and evaluating metrics, as well as human evaluations, are carefully established on this benchmark. We also propose a strong baseline model, i.e., Localizing Actions with Transformers (LocATe), that jointly localizes and recognizes actions in a 3D sequence. The proposed LocATe shows superior performance on BABEL-TAL as well as on the large-scale PKU-MMD dataset, achieving state-of-the-art performance by using only 10% of the labeled training data. Our research could advance the development of more accurate and efficient systems for human behavior analysis, with potential applications in areas such as human-computer interaction and healthcare.

    View details for DOI 10.1038/s44172-024-00272-7

    View details for PubMedID 39227676

    View details for PubMedCentralID PMC11372174

  • Virtual multiplexed immunofluorescence staining from non-antibody-stained fluorescence imaging for gastric cancer prognosis. EBioMedicine Zhou, Z., Jiang, Y., Sun, Z., Zhang, T., Feng, W., Li, G., Li, R., Xing, L. 2024; 107: 105287

    Abstract

    Multiplexed immunofluorescence (mIF) staining, such as CODEX and MIBI, holds significant clinical value for various fields, such as disease diagnosis, biological research, and drug development. However, these techniques are often hindered by high time and cost requirements.Here we present a Multimodal-Attention-based virtual mIF Staining (MAS) system that utilises a deep learning model to extract potential antibody-related features from dual-modal non-antibody-stained fluorescence imaging, specifically autofluorescence (AF) and DAPI imaging. The MAS system simultaneously generates predictions of mIF with multiple survival-associated biomarkers in gastric cancer using self- and multi-attention learning mechanisms.Experimental results with 180 pathological slides from 94 patients with gastric cancer demonstrate the efficiency and consistent performance of the MAS system in both cancer and noncancer gastric tissues. Furthermore, we showcase the prognostic accuracy of the virtual mIF images of seven gastric cancer related biomarkers, including CD3, CD20, FOXP3, PD1, CD8, CD163, and PD-L1, which is comparable to those obtained from the standard mIF staining.The MAS system rapidly generates reliable multiplexed staining, greatly reducing the cost of mIF and improving clinical workflow.Stanford 2022 HAI Seed Grant; National Institutes of Health 1R01CA256890.

    View details for DOI 10.1016/j.ebiom.2024.105287

    View details for PubMedID 39154539

  • Auto-delineation of treatment target volume for radiation therapy using large language model-aided multimodal learning. International journal of radiation oncology, biology, physics Rajendran, P., Chen, Y., Qiu, L., Niedermayr, T., Liu, W., Buyyounouski, M., Bagshaw, H., Han, B., Yang, Y., Kovalchuk, N., Gu, X., Hancock, S., Xing, L., Dai, X. 2024

    Abstract

    Artificial intelligence (AI)-aided methods have made significant progress in the auto-delineation of normal tissues. However, these approaches struggle with the auto-contouring of radiotherapy target volume. Our goal is to model the delineation of target volume as a clinical decision-making problem, resolved by leveraging large language model-aided multimodal learning approaches.A vision-language model, termed Medformer, has been developed, employing the hierarchical vision transformer as its backbone, and incorporating large language models to extract text-rich features. The contextually embedded linguistic features are seamlessly integrated into visual features for language-aware visual encoding through the visual language attention module. Metrics, including Dice similarity coefficient (DSC), intersection over union (IOU), and 95th percentile Hausdorff distance (HD95), were used to quantitatively evaluate the performance of our model. The evaluation was conducted on an in-house prostate cancer dataset and a public oropharyngeal carcinoma (OPC) dataset, totaling 668 subjects.Our Medformer achieved a DSC of 0.81 ± 0.10 versus 0.72 ± 0.10, IOU of 0.73 ± 0.12 versus 0.65 ± 0.09, and HD95 of 9.86 ± 9.77 mm versus 19.13 ± 12.96 mm for delineation of gross tumor volume (GTV) on the prostate cancer dataset. Similarly, on the OPC dataset, it achieved a DSC of 0.77 ± 0.11 versus 0.72 ± 0.09, IOU of 0.70 ± 0.09 versus 0.65 ± 0.07, and HD95 of 7.52 ± 4.8 mm versus 13.63 ± 7.13 mm, representing significant improvements (p < 0.05). For delineating the clinical target volume (CTV), Medformer achieved a DSC of 0.91 ± 0.04, IOU of 0.85 ± 0.05, and HD95 of 2.98 ± 1.60 mm, comparable to other state-of-the-art algorithms.Auto-delineation of the treatment target based on multimodal learning outperforms conventional approaches that rely purely on visual features. Our method could be adopted into routine practice to rapidly contour CTV/GTV.

    View details for DOI 10.1016/j.ijrobp.2024.07.2149

    View details for PubMedID 39117164

  • Deciphering the Feature Representation of Deep Neural Networks for High-Performance AI IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Islam, M., Xing, L. 2024; 46 (8): 5273-5287

    Abstract

    AI driven by deep learning is transforming many aspects of science and technology. The enormous success of deep learning stems from its unique capability of extracting essential features from Big Data for decision-making. However, the feature extraction and hidden representations in deep neural networks (DNNs) remain inexplicable, primarily because of lack of technical tools to comprehend and interrogate the feature space data. The main hurdle here is that the feature data are often noisy in nature, complex in structure, and huge in size and dimensionality, making it intractable for existing techniques to analyze the data reliably. In this work, we develop a computational framework named contrastive feature analysis (CFA) to facilitate the exploration of the DNN feature space and improve the performance of AI. By utilizing the interaction relations among the features and incorporating a novel data-driven kernel formation strategy into the feature analysis pipeline, CFA mitigates the limitations of traditional approaches and provides an urgently needed solution for the analysis of feature space data. The technique allows feature data exploration in unsupervised, semi-supervised and supervised formats to address different needs of downstream applications. The potential of CFA and its applications for pruning of neural network architectures are demonstrated using several state-of-the-art networks and well-annotated datasets across different disciplines.

    View details for DOI 10.1109/TPAMI.2024.3363642

    View details for Web of Science ID 001262841000005

    View details for PubMedID 38373137

  • TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers. Medical image analysis Chen, J., Mei, J., Li, X., Lu, Y., Yu, Q., Wei, Q., Luo, X., Xie, Y., Adeli, E., Wang, Y., Lungren, M. P., Zhang, S., Xing, L., Lu, L., Yuille, A., Zhou, Y. 2024; 97: 103280

    Abstract

    Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-Net face limitations in modeling long-range dependencies. To address this, Transformers designed for sequence-to-sequence predictions have been integrated into medical image segmentation. However, a comprehensive understanding of Transformers' self-attention in U-Net components is lacking. TransUNet, first introduced in 2021, is widely recognized as one of the first models to integrate Transformer into medical image analysis. In this study, we present the versatile framework of TransUNet that encapsulates Transformers' self-attention into two key modules: (1) a Transformer encoder tokenizing image patches from a convolution neural network (CNN) feature map, facilitating global context extraction, and (2) a Transformer decoder refining candidate regions through cross-attention between proposals and U-Net features. These modules can be flexibly inserted into the U-Net backbone, resulting in three configurations: Encoder-only, Decoder-only, and Encoder+Decoder. TransUNet provides a library encompassing both 2D and 3D implementations, enabling users to easily tailor the chosen architecture. Our findings highlight the encoder's efficacy in modeling interactions among multiple abdominal organs and the decoder's strength in handling small targets like tumors. It excels in diverse medical applications, such as multi-organ segmentation, pancreatic tumor segmentation, and hepatic vessel segmentation. Notably, our TransUNet achieves a significant average Dice improvement of 1.06% and 4.30% for multi-organ segmentation and pancreatic tumor segmentation, respectively, when compared to the highly competitive nn-UNet, and surpasses the top-1 solution in the BrasTS2021 challenge. 2D/3D Code and models are available at https://github.com/Beckschen/TransUNet and https://github.com/Beckschen/TransUNet-3D, respectively.

    View details for DOI 10.1016/j.media.2024.103280

    View details for PubMedID 39096845

  • Large Language Model-Augmented Auto-Delineation of Treatment Target Volume in Radiation Therapy. ArXiv Rajendran, P., Yang, Y., Niedermayr, T. R., Gensheimer, M., Beadle, B., Le, Q. T., Xing, L., Dai, X. 2024

    Abstract

    Radiation therapy (RT) is one of the most effective treatments for cancer, and its success relies on the accurate delineation of targets. However, target delineation is a comprehensive medical decision that currently relies purely on manual processes by human experts. Manual delineation is time-consuming, laborious, and subject to interobserver variations. Although the advancements in artificial intelligence (AI) techniques have significantly enhanced the auto-contouring of normal tissues, accurate delineation of RT target volumes remains a challenge. In this study, we propose a visual language model-based RT target volume auto-delineation network termed Radformer. The Radformer utilizes a hierarchical vision transformer as the backbone and incorporates large language models to extract text-rich features from clinical data. We introduce a visual language attention module (VLAM) for integrating visual and linguistic features for language-aware visual encoding (LAVE). The Radformer has been evaluated on a dataset comprising 2985 patients with head-and-neck cancer who underwent RT. Metrics, including the Dice similarity coefficient (DSC), intersection over union (IOU), and 95th percentile Hausdorff distance (HD95), were used to evaluate the performance of the model quantitatively. Our results demonstrate that the Radformer has superior segmentation performance compared to other state-of-the-art models, validating its potential for adoption in RT practice.

    View details for PubMedID 39040646

    View details for PubMedCentralID PMC11261986

  • Optimizing cystoscopy and TURBT: enhanced imaging and artificial intelligence. Nature reviews. Urology Shkolyar, E., Zhou, S. R., Carlson, C. J., Chang, S., Laurie, M. A., Xing, L., Bowden, A. K., Liao, J. C. 2024

    Abstract

    Diagnostic cystoscopy in combination with transurethral resection of the bladder tumour are the standard for the diagnosis, surgical treatment and surveillance of bladder cancer. The ability to inspect the bladder in its current form stems from a long chain of advances in imaging science and endoscopy. Despite these advances, bladder cancer recurrence and progression rates remain high after endoscopic resection. This stagnation is a result of the heterogeneity of cancer biology as well as limitations in surgical techniques and tools, as incomplete resection and provider-specific differences affect cancer persistence and early recurrence. An unmet clinical need remains for solutions that can improve tumour delineation and resection. Translational advances in enhanced cystoscopy technologies and artificial intelligence offer promising avenues to overcoming the progress plateau.

    View details for DOI 10.1038/s41585-024-00904-9

    View details for PubMedID 38982304

    View details for PubMedCentralID 6889816

  • Commissioning of a novel PET-Linac for biology-guided radiotherapy (BgRT). Medical physics Surucu, M., Ashraf, M. R., Romero, I. O., Zalavari, L. T., Pham, D., Vitzthum, L. K., Gensheimer, M. F., Yang, Y., Xing, L., Kovalchuk, N., Han, B. 2024

    Abstract

    Biology-guided radiotherapy (BgRT) is a novel radiotherapy delivery technique that utilizes the tumor itself to guide dynamic delivery of treatment dose to the tumor. The RefleXion X1 system is the first radiotherapy system developed to deliver SCINTIX® BgRT. The X1 is characterized by its split arc design, employing two 90-degree positron emission tomography (PET) arcs to guide therapeutic radiation beams in real time, currently cleared by FDA to treat bone and lung tumors.This study aims to comprehensively evaluate the capabilities of the SCINTIX radiotherapy delivery system by evaluating its sensitivity to changes in PET contrast, its adaptability in the context of patient motion, and its performance across a spectrum of prescription doses.A series of experimental scenarios, both static and dynamic, were designed to assess the SCINTIX BgRT system's performance, including an end-to-end test. These experiments involved a range of factors, including changes in PET contrast, motion, and prescription doses. Measurements were performed using a custom-made ArcCHECK insert which included a 2.2 cm spherical target and a c-shape structure that can be filled with a PET tracer with varying concentrations. Sinusoidal and cosine4 motion patterns, simulating patient breathing, was used to test the SCINTIX system's ability to deliver BgRT during motion-induced challenges. Each experiment was evaluated against specific metrics, including Activity Concentration (AC), Normalized Target Signal (NTS), and Biology Tracking Zone (BTZ) bounded dose-volume histogram (bDVH) pass rates. The accuracy of the delivered BgRT doses on ArcCHECK and EBT-XD film were evaluated using gamma 3%/2 mm and 3%/3 mm analysis.In static scenarios, the X1 system consistently demonstrated precision and robustness in SCINTIX dose delivery. The end-to-end delivery to the spherical target yielded good results, with AC and NTS values surpassing the critical thresholds of 5 kBq/mL and 2, respectively. Furthermore, bDVH analysis consistently confirmed 100% pass rates. These results were reaffirmed in scenarios involving changes in PET contrast, emphasizing the system's ability to adapt to varying PET avidities. Gamma analysis with 3%/2 mm (10% dose threshold) criteria consistently achieved pass rates > 91.5% for the static tests. In dynamic SCINTIX delivery scenarios, the X1 system exhibited adaptability under conditions of motion. Sinusoidal and cosine4 motion patterns resulted in 3%/3 mm gamma pass rates > 87%. Moreover, the comparison with gated stereotactic body radiotherapy (SBRT) delivery on a conventional c-arm Linac resulted in 93.9% gamma pass rates and used as comparison to evaluate the interplay effect. The 1 cm step shift tests showed low overall gamma pass rates of 60.3% in ArcCHECK measurements, while the doses in the PTV agreed with the plan with 99.9% for 3%/3 mm measured with film.The comprehensive evaluation of the X1 radiotherapy delivery system for SCINTIX BgRT demonstrated good agreement for the static tests. The system consistently achieved critical metrics and delivered the BgRT doses per plan. The motion tests demonstrated its ability to co-localize the dose where the PET signal is and deliver acceptable BgRT dose distributions.

    View details for DOI 10.1002/mp.17114

    View details for PubMedID 38703397

  • Automated contouring, treatment planning, and quality assurance for VMAT craniospinal irradiation (VMAT-CSI). Frontiers in oncology Simiele, E., Romero, I. O., Wang, J. Y., Chen, Y., Lozko, Y., Severyn, Y., Skinner, L., Yang, Y., Xing, L., Gibbs, I., Hiniker, S. M., Kovalchuk, N. 2024; 14: 1378449

    Abstract

    Create a comprehensive automated solution for pediatric and adult VMAT-CSI including contouring, planning, and plan check to reduce planning time and improve plan quality.Seventy-seven previously treated CSI patients (age, 2-67 years) were used for creation of an auto-contouring model to segment 25 organs at risk (OARs). The auto-contoured OARs were evaluated using the Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and a qualitative ranking by one physician and one physicist (scale: 1-acceptable, 2-minor edits, 3-major edits). The auto-planning script was developed using the Varian Eclipse Scripting API and tested with 20 patients previously treated with either low-dose VMAT-CSI (12 Gy) or high-dose VMAT-CSI (36 Gy + 18 Gy boost). Clinically relevant metrics, planning time, and blinded physician review were used to evaluate significance of differences between the auto and manual plans. Finally, the plan preparation for treatment and plan check processes were automated to improve efficiency and safety of VMAT-CSI.The auto-contours achieved an average DSC of 0.71 ± 0.15, HD95 of 4.81 ± 4.68, and reviewers' ranking of 1.22 ± 0.39, indicating close to "acceptable-as-is" contours. Compared to the manual CSI plans, the auto-plans for both dose regimens achieved statistically significant reductions in body V50% and Dmean for parotids, submandibular, and thyroid glands. The variance in the dosimetric parameters decreased for the auto-plans as compared to the manual plans indicating better plan consistency. From the blinded review, the auto-plans were marked as equivalent or superior to the manual-plans 88.3% of the time. The required time for the auto-contouring and planning was consistently between 1-2 hours compared to an estimated 5-6 hours for manual contouring and planning.Reductions in contouring and planning time without sacrificing plan quality were obtained using the developed auto-planning process. The auto-planning scripts and documentation will be made freely available to other institutions and clinics.

    View details for DOI 10.3389/fonc.2024.1378449

    View details for PubMedID 38660134

    View details for PubMedCentralID PMC11039907

  • Quantifying particle concentration via AI-enhanced optical coherence tomography. Nanoscale Ye, S., Xing, L., Myung, D., Chen, F. 2024

    Abstract

    Efficient and robust quantification of the number of nanoparticles in solution is not only essential but also insufficient in nanotechnology and biomedical research. This paper proposes to use optical coherence tomography (OCT) to quantify the number of gold nanorods, which exemplify the nanoparticles with high light scattering signals. Additionally, we have developed an AI-enhanced OCT image processing to improve the accuracy and robustness of the quantification result.

    View details for DOI 10.1039/d4nr00195h

    View details for PubMedID 38511606

  • Electronic Documentation of Intraoperative Observation of Cystoscopic Procedures Using the cMDX Information System. JCO clinical cancer informatics Eminaga, O., Lee, T. J., La, V., Breil, B., Xing, L., Liao, J. C. 2024; 8: e2300114

    Abstract

    PURPOSE: Accurate documentation of lesions during transurethral resection of bladder tumors (TURBT) is essential for precise diagnosis, treatment planning, and follow-up care. However, optimizing schematic documentation techniques for bladder lesions has received limited attention.MATERIALS AND METHODS: This prospective observational study used a cMDX-based documentation system that facilitates graphical representation, a lesion-specific questionnaire, and heatmap analysis with a posterization effect. We designed a graphical scheme for bladder covering bladder landmarks to visualize anatomic features and to document the lesion location. The lesion-specific questionnaire was integrated for comprehensive lesion characterization. Finally, spatial analyses were applied to investigate the anatomic distribution patterns of bladder lesions.RESULTS: A total of 97 TURBT cases conducted between 2021 and 2023 were included, identifying 176 lesions. The lesions were distributed in different bladder areas with varying frequencies. The distribution pattern, sorted by frequency, was observed in the following areas: posterior, trigone, lateral right and anterior, and lateral left and dome. Suspicious levels were assigned to the lesions, mostly categorized either as indeterminate or moderate. Lesion size analysis revealed that most lesions fell between 5 and 29 mm.CONCLUSION: The study highlights the potential of schematic documentation techniques for informed decision making, quality assessment, primary research, and secondary data utilization of intraoperative data in the context of TURBT. Integrating cMDX and heatmap analysis provides valuable insights into lesion distribution and characteristics.

    View details for DOI 10.1200/CCI.23.00114

    View details for PubMedID 38484216

  • A time- and space-saving Monte Carlo simulation method using post-collimation generative adversarial network for dose calculation of an O-ring gantry Linac. Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Shi, M., Cui, S., Chuang, C., Oderinde, O., Kovalchuk, N., Surucu, M., Xing, L., Han, B. 2024; 119: 103318

    Abstract

    This study explores the feasibility of employing Generative Adversarial Networks (GANs) to model the RefleXion X1 Linac. The aim is to investigate the accuracy of dose simulation and assess the potential computational benefits.The X1 Linac is a new radiotherapy machine with a binary multi-leaf collimation (MLC) system, facilitating innovative biology-guided radiotherapy. A total of 34 GAN generators, each representing a desired MLC aperture, were developed. Each generator was trained using a phase space file generated underneath the corresponding aperture, enabling the generation of particles and serving as a beam source for Monte Carlo simulation. Dose distributions in water were simulated for each aperture using both the GAN and phase space sources. The agreement between dose distributions was evaluated. The computational time reduction from bypassing the collimation simulation and storage space savings were estimated.The percentage depth dose at 10 cm, penumbra, and full-width half maximum of the GAN simulation agree with the phase space simulation, with differences of 0.4 % ± 0.2 %, 0.32 ± 0.66 mm, and 0.26 ± 0.44 mm, respectively. The gamma passing rate (1 %/1mm) for the planar dose exceeded 90 % for all apertures. The estimated time-saving for simulating an plan using 5766 beamlets was 530 CPU hours. The storage usage was reduced by a factor of 102.The utilization of the GAN in simulating the X1 Linac demonstrated remarkable accuracy and efficiency. The reductions in both computational time and storage requirements make this approach highly valuable for future dosimetry studies and beam modeling.

    View details for DOI 10.1016/j.ejmp.2024.103318

    View details for PubMedID 38382210

  • Self-supervised deep learning of gene-gene interactions for improved gene expression recovery. Briefings in bioinformatics Wei, Q., Islam, M. T., Zhou, Y., Xing, L. 2024; 25 (2)

    Abstract

    Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, leading to inaccurate gene counts. Existing methods, including advanced deep learning techniques, struggle to reliably impute gene expressions due to a lack of mechanisms that explicitly consider the underlying biological knowledge of the system. In reality, it has long been recognized that gene-gene interactions may serve as reflective indicators of underlying biology processes, presenting discriminative signatures of the cells. A genomic data analysis framework that is capable of leveraging the underlying gene-gene interactions is thus highly desirable and could allow for more reliable identification of distinctive patterns of the genomic data through extraction and integration of intricate biological characteristics of the genomic data. Here we tackle the problem in two steps to exploit the gene-gene interactions of the system. We first reposition the genes into a 2D grid such that their spatial configuration reflects their interactive relationships. To alleviate the need for labeled ground truth gene expression datasets, a self-supervised 2D convolutional neural network is employed to extract the contextual features of the interactions from the spatially configured genes and impute the omitted values. Extensive experiments with both simulated and experimental scRNA-seq datasets are carried out to demonstrate the superior performance of the proposed strategy against the existing imputation methods.

    View details for DOI 10.1093/bib/bbae031

    View details for PubMedID 38349062

  • PolypMixNet: Enhancing semi-supervised polyp segmentation with polyp-aware augmentation. Computers in biology and medicine Jia, X., Shen, Y., Yang, J., Song, R., Zhang, W., Meng, M. Q., Liao, J. C., Xing, L. 2024; 170: 108006

    Abstract

    AI-assisted polyp segmentation in colonoscopy plays a crucial role in enabling prompt diagnosis and treatment of colorectal cancer. However, the lack of sufficient annotated data poses a significant challenge for supervised learning approaches. Existing semi-supervised learning methods also suffer from performance degradation, mainly due to task-specific characteristics, such as class imbalance in polyp segmentation.The purpose of this work is to develop an effective semi-supervised learning framework for accurate polyp segmentation in colonoscopy, addressing limited annotated data and class imbalance challenges.We proposed PolypMixNet, a semi-supervised framework, for colorectal polyp segmentation, utilizing novel augmentation techniques and a Mean Teacher architecture to improve model performance. PolypMixNet introduces the polyp-aware mixup (PolypMix) algorithm and incorporates dual-level consistency regularization. PolypMix addresses the class imbalance in colonoscopy datasets and enhances the diversity of training data. By performing a polyp-aware mixup on unlabeled samples, it generates mixed images with polyp context along with their artificial labels. A polyp-directed soft pseudo-labeling (PDSPL) mechanism was proposed to generate high-quality pseudo labels and eliminate the dilution of lesion features caused by mixup operations. To ensure consistency in the training phase, we introduce the PolypMix prediction consistency (PMPC) loss and PolypMix attention consistency (PMAC) loss, enforcing consistency at both image and feature levels. Code is available at https://github.com/YChienHung/PolypMix.PolypMixNet was evaluated on four public colonoscopy datasets, achieving 88.97% Dice and 88.85% mIoU on the benchmark dataset of Kvasir-SEG. In scenarios where the labeled training data is limited to 15%, PolypMixNet outperforms the state-of-the-art semi-supervised approaches with a 2.88-point improvement in Dice. It also shows the ability to reach performance comparable to the fully supervised counterpart. Additionally, we conducted extensive ablation studies to validate the effectiveness of each module and highlight the superiority of our proposed approach.PolypMixNet effectively addresses the challenges posed by limited annotated data and unbalanced class distributions in polyp segmentation. By leveraging unlabeled data and incorporating novel augmentation and consistency regularization techniques, our method achieves state-of-the-art performance. We believe that the insights and contributions presented in this work will pave the way for further advancements in semi-supervised polyp segmentation and inspire future research in the medical imaging domain.

    View details for DOI 10.1016/j.compbiomed.2024.108006

    View details for PubMedID 38325216

  • Ultrafast Labeling for Multiplexed Immunobiomarkers from Label-free Fluorescent Images Zhou, Z., Jiang, Y., Li, R., Xing, L., Wu, S., Shabestari, B., Xing, L. SPRINGER INTERNATIONAL PUBLISHING AG. 2024: 125-134
  • Revealing hidden patterns in deep neural network feature space continuum via manifold learning. Nature communications Islam, M. T., Zhou, Z., Ren, H., Khuzani, M. B., Kapp, D., Zou, J., Tian, L., Liao, J. C., Xing, L. 2023; 14 (1): 8506

    Abstract

    Deep neural networks (DNNs) extract thousands to millions of task-specific features during model training for inference and decision-making. While visualizing these features is critical for comprehending the learning process and improving the performance of the DNNs, existing visualization techniques work only for classification tasks. For regressions, the feature points lie on a high dimensional continuum having an inherently complex shape, making a meaningful visualization of the features intractable. Given that the majority of deep learning applications are regression-oriented, developing a conceptual framework and computational method to reliably visualize the regression features is of great significance. Here, we introduce a manifold discovery and analysis (MDA) method for DNN feature visualization, which involves learning the manifold topology associated with the output and target labels of a DNN. MDA leverages the acquired topological information to preserve the local geometry of the feature space manifold and provides insightful visualizations of the DNN features, highlighting the appropriateness, generalizability, and adversarial robustness of a DNN. The performance and advantages of the MDA approach compared to the existing methods are demonstrated in different deep learning applications.

    View details for DOI 10.1038/s41467-023-43958-w

    View details for PubMedID 38129376

    View details for PubMedCentralID 8791835

  • Automating the Treatment Planning Process for Volumetric Modulated Arc Therapy Craniospinal Irradiation (VMAT-CSI). Practical radiation oncology Romero, I. O., Simiele, E. A., Lozko, Y., Severyn, Y., Skinner, L. B., Yang, Y., Wang, J. Y., Xing, L., Gibbs, I., Hiniker, S. M., Kovalchuk, N. 2023

    Abstract

    The purpose of this work is to develop a method to automate the treatment planning process of craniospinal irradiation (CSI) using volumetric modulated arc therapy (VMAT).Two scripts were developed using the Eclipse Scripting Application Programming Interface (ESAPI) to perform auto-plan preparation and optimization. Ten patients (age, 5-44 years) previously treated at our institution with low dose VMAT-CSI (prescription of 12 Gy) prior to total body irradiation were selected to evaluate the efficacy of the proposed auto-planning process. Paired t-tests compared the dosimetric indices of the auto-plans to the manually generated clinical plans. All plans were normalized to 95% of PTV coverage with the prescription dose. Two physicians and one physicist were asked to evaluate the manual plans and auto-plans of each patient in a blinded retrospective review and to indicate clinical acceptability and which plans were preferred for treatment.Compared to the manual CSI plans, the auto plans obtained significant reductions in Dmean to the parotids, submandibular glands, larynx, thyroid, and significant reduction in the plan PTV Dmax and D0.03cc. The standard deviation range of the dosimetric parameters was greatly reduced for auto plans (range, 0.1-1.3 Gy) relative to manual plans (range, 0.4-5.9 Gy) indicating better plan consistency. Among the ten patients, the auto-plans were preferred over the manual plans 90% of the time by the reviewing experts. The required time for auto-planning was approximately 1 hour compared to estimated 4 or more hours for manual planning.Reductions in planning time without sacrifices in plan quality were obtained using the auto-planning process compared with manual planning. Variation in plan quality was also reduced. The auto-planning scripts will be made freely available to other institutions and clinics.

    View details for DOI 10.1016/j.prro.2023.11.014

    View details for PubMedID 38048988

  • Ultrasound-guided needle tracking with deep learning: A novel approach with photoacoustic ground truth. Photoacoustics Hui, X., Rajendran, P., Ling, T., Dai, X., Xing, L., Pramanik, M. 2023; 34: 100575

    Abstract

    Accurate needle guidance is crucial for safe and effective clinical diagnosis and treatment procedures. Conventional ultrasound (US)-guided needle insertion often encounters challenges in consistency and precisely visualizing the needle, necessitating the development of reliable methods to track the needle. As a powerful tool in image processing, deep learning has shown promise for enhancing needle visibility in US images, although its dependence on manual annotation or simulated data as ground truth can lead to potential bias or difficulties in generalizing to real US images. Photoacoustic (PA) imaging has demonstrated its capability for high-contrast needle visualization. In this study, we explore the potential of PA imaging as a reliable ground truth for deep learning network training without the need for expert annotation. Our network (UIU-Net), trained on ex vivo tissue image datasets, has shown remarkable precision in localizing needles within US images. The evaluation of needle segmentation performance extends across previously unseen ex vivo data and in vivo human data (collected from an open-source data repository). Specifically, for human data, the Modified Hausdorff Distance (MHD) value stands at approximately 3.73, and the targeting error value is around 2.03, indicating the strong similarity and small needle orientation deviation between the predicted needle and actual needle location. A key advantage of our method is its applicability beyond US images captured from specific imaging systems, extending to images from other US imaging systems.

    View details for DOI 10.1016/j.pacs.2023.100575

    View details for PubMedID 38174105

    View details for PubMedCentralID PMC10761306

  • Volumetric MRI with sparse sampling for MR-guided 3D motion tracking via sparse prior-augmented implicit neural representation learning. Medical physics Liu, L., Shen, L., Johansson, A., Balter, J. M., Cao, Y., Vitzthum, L., Xing, L. 2023

    Abstract

    Volumetric reconstruction of magnetic resonance imaging (MRI) from sparse samples is desirable for 3D motion tracking and promises to improve magnetic resonance (MR)-guided radiation treatment precision. Data-driven sparse MRI reconstruction, however, requires large-scale training datasets for prior learning, which is time-consuming and challenging to acquire in clinical settings.To investigate volumetric reconstruction of MRI from sparse samples of two orthogonal slices aided by sparse priors of two static 3D MRI through implicit neural representation (NeRP) learning, in support of 3D motion tracking during MR-guided radiotherapy.A multi-layer perceptron network was trained to parameterize the NeRP model of a patient-specific MRI dataset, where the network takes 4D data coordinates of voxel locations and motion states as inputs and outputs corresponding voxel intensities. By first training the network to learn the NeRP of two static 3D MRI with different breathing motion states, prior information of patient breathing motion was embedded into network weights through optimization. The prior information was then augmented from two motion states to 31 motion states by querying the optimized network at interpolated and extrapolated motion state coordinates. Starting from the prior-augmented NeRP model as an initialization point, we further trained the network to fit sparse samples of two orthogonal MRI slices and the final volumetric reconstruction was obtained by querying the trained network at 3D spatial locations. We evaluated the proposed method using 5-min volumetric MRI time series with 340 ms temporal resolution for seven abdominal patients with hepatocellular carcinoma, acquired using golden-angle radial MRI sequence and reconstructed through retrospective sorting. Two volumetric MRI with inhale and exhale states respectively were selected from the first 30 s of the time series for prior embedding and augmentation. The remaining 4.5-min time series was used for volumetric reconstruction evaluation, where we retrospectively subsampled each MRI to two orthogonal slices and compared model-reconstructed images to ground truth images in terms of image quality and the capability of supporting 3D target motion tracking.Across the seven patients evaluated, the peak signal-to-noise-ratio between model-reconstructed and ground truth MR images was 38.02 ± 2.60 dB and the structure similarity index measure was 0.98 ± 0.01. Throughout the 4.5-min time period, gross tumor volume (GTV) motion estimated by deforming a reference state MRI to model-reconstructed and ground truth MRI showed good consistency. The 95-percentile Hausdorff distance between GTV contours was 2.41 ± 0.77 mm, which is less than the voxel dimension. The mean GTV centroid position difference between ground truth and model estimation was less than 1 mm in all three orthogonal directions.A prior-augmented NeRP model has been developed to reconstruct volumetric MRI from sparse samples of orthogonal cine slices. Only one exhale and one inhale 3D MRI were needed to train the model to learn prior information of patient breathing motion for sparse image reconstruction. The proposed model has the potential of supporting 3D motion tracking during MR-guided radiotherapy for improved treatment precision and promises a major simplification of the workflow by eliminating the need for large-scale training datasets.

    View details for DOI 10.1002/mp.16845

    View details for PubMedID 38014764

  • Personalized Accelerated ChEmoRadiation (PACER) for Lung Cancer: Protocol for a Bayesian Optimal Phase I/II Trial. Clinical lung cancer Hui, C., Brown, E., Wong, S., Das, M., Wakelee, H., Neal, J., Ramchandran, K., Myall, N. J., Pham, D., Xing, L., Yang, Y., Kovalchuk, N., Yuan, Y., Lu, Y., Xiang, M., Chin, A., Diehn, M., Loo, B. W., Vitzthum, L. K. 2023

    Abstract

    Prior attempts to escalate radiation dose for non-small cell lung cancer (NSCLC) have not improved survival. Given the high risk for cardiopulmonary toxicity with treatment and heterogenous presentation of locally advanced NSCLC, it is unlikely that a single dose regimen is optimal for all patients. This phase I/II trial aims to evaluate a novel treatment approach where the level of accelerated hypofractionation is determined by the predicted toxicity from dose to organs at risk (OARs).Patients ≥ 18 years old with lung cancer planned for fractionated radiotherapy to the lung with concurrent chemotherapy will be eligible. Radiation therapy (RT) will be delivered to a total dose of 60 to 66 Gy in 30, 25, or 20 fractions depending on the ability to meet constraints to key organs at risk including the lungs, heart, and esophagus. The primary endpoint is high grade pulmonary, esophageal, or cardiac toxicity. A Bayesian optimized design is used to determine stopping boundaries and evaluate the primary endpoint.PACER will evaluate the safety and feasibility of personalized accelerated chemoradiotherapy for lung cancer.

    View details for DOI 10.1016/j.cllc.2023.11.004

    View details for PubMedID 38040540

  • Biology-aware mutation-based deep learning for outcome prediction of cancer immunotherapy with immune checkpoint inhibitors. NPJ precision oncology Liu, J., Islam, M. T., Sang, S., Qiu, L., Xing, L. 2023; 7 (1): 117

    Abstract

    The response rate of cancer immune checkpoint inhibitors (ICI) varies among patients, making it challenging to pre-determine whether a particular patient will respond to immunotherapy. While gene mutation is critical to the treatment outcome, a framework capable of explicitly incorporating biology knowledge has yet to be established. Here we aim to propose and validate a mutation-based deep learning model for survival analysis on 1571 patients treated with ICI. Our model achieves an average concordance index of 0.59±0.13 across nine types of cancer, compared to the gold standard Cox-PH model (0.52±0.10). The "black box" nature of deep learning is a major concern in healthcare field. This model's interpretability, which results from incorporating the gene pathways and protein interaction (i.e., biology-aware) rather than relying on a 'black box' approach, helps patient stratification and provides insight into novel gene biomarkers, advancing our understanding of ICI treatment.

    View details for DOI 10.1038/s41698-023-00468-8

    View details for PubMedID 37932419

  • Artificial Intelligence Reveals Distinct Prognostic Subgroups of Muscle-Invasive Bladder Cancer on Histology Images. Cancers Eminaga, O., Leyh-Bannurah, S. R., Shariat, S. F., Krabbe, L. M., Lau, H., Xing, L., Abbas, M. 2023; 15 (20)

    Abstract

    Muscle-invasive bladder cancer (MIBC) is a highly heterogeneous and costly disease with significant morbidity and mortality. Understanding tumor histopathology leads to tailored therapies and improved outcomes. In this study, we employed a weakly supervised learning and neural architecture search to develop a data-driven scoring system. This system aimed to capture prognostic histopathological patterns observed in H&E-stained whole-slide images. We constructed and externally validated our scoring system using multi-institutional datasets with 653 whole-slide images. Additionally, we explored the association between our scoring system, seven histopathological features, and 126 molecular signatures. Through our analysis, we identified two distinct risk groups with varying prognoses, reflecting inherent differences in histopathological and molecular subtypes. The adjusted hazard ratio for overall mortality was 1.46 (95% CI 1.05-2.02; z: 2.23; p = 0.03), thus identifying two prognostic subgroups in high-grade MIBC. Furthermore, we observed an association between our novel digital biomarker and the squamous phenotype, subtypes of miRNA, mRNA, long non-coding RNA, DNA hypomethylation, and several gene mutations, including FGFR3 in MIBC. Our findings underscore the risk of confounding bias when reducing the complex biological and clinical behavior of tumors to a single mutation. Histopathological changes can only be fully captured through comprehensive multi-omics profiles. The introduction of our scoring system has the potential to enhance daily clinical decision making for MIBC. It facilitates shared decision making by offering comprehensive and precise risk stratification, treatment planning, and cost-effective preselection for expensive molecular characterization.

    View details for DOI 10.3390/cancers15204998

    View details for PubMedID 37894365

    View details for PubMedCentralID PMC10605516

  • Leveraging cell-cell similarity for high-performance spatial and temporal cellular mappings from gene expression data. Patterns (New York, N.Y.) Islam, M. T., Xing, L. 2023; 4 (10): 100840

    Abstract

    Single-cell trajectory mapping and spatial reconstruction are two important developments in life science and provide a unique means to decode heterogeneous tissue formation, cellular dynamics, and tissue developmental processes. The success of these techniques depends critically on the performance of analytical tools used for high-dimensional (HD) gene expression data processing. Existing methods discern the patterns of the data without explicitly considering the underlying biological characteristics of the system, often leading to suboptimal solutions. Here, we present a cell-cell similarity-driven framework of genomic data analysis for high-fidelity spatial and temporal cellular mappings. The approach exploits the similarity features of the cells to discover discriminative patterns of the data. We show that for a wide variety of datasets, the proposed approach drastically improves the accuracies of spatial and temporal mapping analyses compared with state-of-the-art techniques.

    View details for DOI 10.1016/j.patter.2023.100840

    View details for PubMedID 37876896

    View details for PubMedCentralID PMC10591141

  • Super-resolution biomedical imaging via reference-free statistical implicit neural representation. Physics in medicine and biology Ye, S., Shen, L., Islam, M. T., Xing, L. 2023

    Abstract

    Supervised deep learning for image super-resolution (SR) has limitations in biomedical imaging due to the lack of large amounts of low- and high-resolution image pairs for model training. In this work, we propose a reference-free statistical implicit neural representation (INR) framework, which needs only a single or a few observed low-resolution (LR) image(s), to generate high-quality SR images. Approach. The framework models the statistics of the observed LR images via maximum likelihood estimation and trains the INR network to represent the latent high-resolution (HR) image as a continuous function in the spatial domain. The INR network is constructed as a coordinate-based multi-layer perceptron (MLP), whose inputs are image spatial coordinates and outputs are corresponding pixel intensities. The trained INR not only constrains functional smoothness but also allows an arbitrary scale in SR imaging. Main results. We demonstrate the efficacy of the proposed framework on various biomedical images, including CT, MRI, fluorescence microscopy images, and ultrasound images, across different SR magnification scales of 2×, 4×, and 8×. A limited number of LR images were used for each of the SR imaging tasks to show the potential of the proposed statistical INR framework. Significance. The proposed method provides an urgently needed unsupervised deep learning framework for numerous biomedical SR applications that lack HR reference images.

    View details for DOI 10.1088/1361-6560/acfdf1

    View details for PubMedID 37757838

  • Efficient Augmented Intelligence Framework for Bladder Lesion Detection. JCO clinical cancer informatics Eminaga, O., Lee, T. J., Laurie, M., Ge, T. J., La, V., Long, J., Semjonow, A., Bogemann, M., Lau, H., Shkolyar, E., Xing, L., Liao, J. C. 2023; 7: e2300031

    Abstract

    Development of intelligence systems for bladder lesion detection is cost intensive. An efficient strategy to develop such intelligence solutions is needed.We used four deep learning models (ConvNeXt, PlexusNet, MobileNet, and SwinTransformer) covering a variety of model complexity and efficacy. We trained these models on a previously published educational cystoscopy atlas (n = 312 images) to estimate the ratio between normal and cancer scores and externally validated on cystoscopy videos from 68 cases, with region of interest (ROI) pathologically confirmed to be benign and cancerous bladder lesions (ie, ROI). The performance measurement included specificity and sensitivity at frame level, frame sequence (block) level, and ROI level for each case.Specificity was comparable between four models at frame (range, 30.0%-44.8%) and block levels (56%-67%). Although sensitivity at the frame level (range, 81.4%-88.1%) differed between the models, sensitivity at the block level (100%) and ROI level (100%) was comparable between these models. MobileNet and PlexusNet were computationally more efficient for real-time ROI detection than ConvNeXt and SwinTransformer.Educational cystoscopy atlas and efficient models facilitate the development of real-time intelligence system for bladder lesion detection.

    View details for DOI 10.1200/CCI.23.00031

    View details for PubMedID 37774313

  • Biology-guided deep learning predicts prognosis and cancer immunotherapy response. Nature communications Jiang, Y., Zhang, Z., Wang, W., Huang, W., Chen, C., Xi, S., Ahmad, M. U., Ren, Y., Sang, S., Xie, J., Wang, J. Y., Xiong, W., Li, T., Han, Z., Yuan, Q., Xu, Y., Xing, L., Poultsides, G. A., Li, G., Li, R. 2023; 14 (1): 5135

    Abstract

    Substantial progress has been made in using deep learning for cancer detection and diagnosis in medical images. Yet, there is limited success on prediction of treatment response and outcomes, which has important implications for personalized treatment strategies. A significant hurdle for clinical translation of current data-driven deep learning models is lack of interpretability, often attributable to a disconnect from the underlying pathobiology. Here, we present a biology-guided deep learning approach that enables simultaneous prediction of the tumor immune and stromal microenvironment status as well as treatment outcomes from medical images. We validate the model for predicting prognosis of gastric cancer and the benefit from adjuvant chemotherapy in a multi-center international study. Further, the model predicts response to immune checkpoint inhibitors and complements clinically approved biomarkers. Importantly, our model identifies a subset of mismatch repair-deficient tumors that are non-responsive to immunotherapy and may inform the selection of patients for combination treatments.

    View details for DOI 10.1038/s41467-023-40890-x

    View details for PubMedID 37612313

    View details for PubMedCentralID PMC10447467

  • Deep learning-based fluorescence image correction for high spatial resolution precise dosimetry. Physics in medicine and biology Nomura, Y., Ashraf, M. R., Shi, M., Xing, L. 2023

    Abstract

    While radiation-excited fluorescence imaging has great potential to measure absolute 2D dose distributions with high spatial resolution, the fluorescence images are contaminated by noise or artifacts due to Cherenkov light, scattered light or background noise. This study developed a novel deep learning-based model to correct the fluorescence images for accurate dosimetric application.181 single-aperture static photon beams were delivered to an acrylic tank containing quinine hemisulfate water solution. The emitted radiation-exited optical signals were detected by a complementary metal-oxide semiconductor camera to acquire fluorescence images with 0.3×0.3 mm2 pixel size. 2D labels of projected dose distributions were obtained by applying forward projection calculation of the 3D dose distributions calculated by a clinical treatment planning system. To calibrate the projected dose distributions for Cherenkov angular dependency, a novel empirical Cherenkov emission calibration method was performed. Total 400-epoch supervised learning was applied to a convolutional neural network (CNN) model to predict the projected dose distributions from fluorescence images, gantry, and collimator angles. Accuracy of the calculated projected dose distributions was evaluated with that of uncorrected or conventional methods by using a few quantitative evaluation metrics.The projected dose distributions corrected by the empirical Cherenkov emission calibration represented more accurate noise-free images than the uncalibrated distributions. The proposed CNN model provided accurate projected dose distributions. The mean absolute error of the projected dose distributions was improved from 2.02 to 0.766 mm∙Gy by the CNN model correction. Moreover, the CNN correction provided higher gamma index passing rates for three different threshold criteria than the conventional methods.The deep learning-based method improves the accuracy of dose distribution measurements. This technique will also be applied to optical signal denoising or Cherenkov light discrimination in other imaging modalities. This method will provide an accurate dose verification tool with high spatial resolution.

    View details for DOI 10.1088/1361-6560/acf182

    View details for PubMedID 37591253

  • Tumor detection under cystoscopy with transformer-augmented deep learning algorithm. Physics in medicine and biology Jia, X., Shkolyar, E., Laurie, M. A., Eminaga, O., Liao, J. C., Xing, L. 2023; 68 (16)

    Abstract

    Objective.Accurate tumor detection is critical in cystoscopy to improve bladder cancer resection and decrease recurrence. Advanced deep learning algorithms hold the potential to improve the performance of standard white-light cystoscopy (WLC) in a noninvasive and cost-effective fashion. The purpose of this work is to develop a cost-effective, transformer-augmented deep learning algorithm for accurate detection of bladder tumors in WLC and to assess its performance on archived patient data.Approach.'CystoNet-T', a deep learning-based bladder tumor detector, was developed with a transformer-augmented pyramidal CNN architecture to improve automated tumor detection of WLC. CystoNet-T incorporated the self-attention mechanism by attaching transformer encoder modules to the pyramidal layers of the feature pyramid network (FPN), and obtained multi-scale activation maps with global features aggregation. Features resulting from context augmentation served as the input to a region-based detector to produce tumor detection predictions. The training set was constructed by 510 WLC frames that were obtained from cystoscopy video sequences acquired from 54 patients. The test set was constructed based on 101 images obtained from WLC sequences of 13 patients.Main results.CystoNet-T was evaluated on the test set with 96.4 F1 and 91.4 AP (Average Precision). This result improved the benchmark of Faster R-CNN and YOLO by 7.3 points in F1 and 3.8 points in AP. The improvement is attributed to the strong ability of global attention of CystoNet-T and better feature learning of the pyramids architecture throughout the training. The model was found to be particularly effective in highlighting the foreground information for precise localization of the true positives while favorably avoiding false alarmsSignificance.We have developed a deep learning algorithm that accurately detects bladder tumors in WLC. Transformer-augmented AI framework promises to aid in clinical decision-making for improved bladder cancer diagnosis and therapeutic guidance.

    View details for DOI 10.1088/1361-6560/ace499

    View details for PubMedID 37548023

  • Real-time Detection of Bladder Cancer Using Augmented Cystoscopy with Deep Learning: a Pilot Study. Journal of endourology Chang, T. C., Shkolyar, E., Del Giudice, F., Eminaga, O., Lee, T., Laurie, M., Seufert, C., Jia, X., Mach, K. E., Xing, L., Liao, J. C. 2023

    Abstract

    Detection of bladder tumors under white light cystoscopy (WLC) is challenging yet impactful on treatment outcomes. Artificial intelligence (AI) holds the potential to improve tumor detection; however, its application in the real-time setting remains unexplored. AI has been applied to previously recorded images for post hoc analysis. In this study, we evaluate the feasibility of real-time AI integration during clinic cystoscopy and transurethral resection of bladder tumor (TURBT) on live, streaming video.Patients undergoing clinic flexible cystoscopy and TURBT were prospectively enrolled. A real-time alert device system (real-time CystoNet) was developed and integrated with standard cystoscopy towers. Streaming videos were processed in real time to display alert boxes in sync with live cystoscopy. The per-frame diagnostic accuracy was measured.Real-time CystoNet was successfully integrated in the operating room during TURBT and clinic cystoscopy in 50 consecutive patients. There were 55 procedures that met the inclusion criteria for analysis including 21 clinic cystoscopies and 34 TURBTs. For clinic cystoscopy, real-time CystoNet achieved per-frame tumor specificity of 98.8% with a median error rate of 3.6% (range: 0 - 47%) frames per cystoscopy. For TURBT, the per-frame tumor sensitivity was 52.9% and the per-frame tumor specificity was 95.4% with an error rate of 16.7% for cases with pathologically confirmed bladder cancers.The current pilot study demonstrates the feasibility of using a real-time AI system (real-time CystoNet) during cystoscopy and TURBT to generate active feedback to the surgeon. Further optimization of CystoNet for real-time cystoscopy dynamics may allow for clinically useful AI-augmented cystoscopy.

    View details for DOI 10.1089/end.2023.0056

    View details for PubMedID 37432899

  • Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging. IEEE transactions on medical imaging Yan, R., Qu, L., Wei, Q., Huang, S. C., Shen, L., Rubin, D. L., Xing, L., Zhou, Y. 2023; 42 (7): 1932-1943

    Abstract

    The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing. Federated learning (FL) is a promising solution that enables privacy-preserving collaborative learning among different institutions, but it generally suffers from performance deterioration due to heterogeneous data distributions and a lack of quality labeled data. In this paper, we present a robust and label-efficient self-supervised FL framework for medical image analysis. Our method introduces a novel Transformer-based self-supervised pre-training paradigm that pre-trains models directly on decentralized target task datasets using masked image modeling, to facilitate more robust representation learning on heterogeneous data and effective knowledge transfer to downstream models. Extensive empirical results on simulated and real-world medical imaging non-IID federated datasets show that masked image modeling with Transformers significantly improves the robustness of models against various degrees of data heterogeneity. Notably, under severe data heterogeneity, our method, without relying on any additional pre-training data, achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training. In addition, we show that our federated self-supervised pre-training methods yield models that generalize better to out-of-distribution data and perform more effectively when fine-tuning with limited labeled data, compared to existing FL algorithms. The code is available at https://github.com/rui-yan/SSL-FL.

    View details for DOI 10.1109/TMI.2022.3233574

    View details for PubMedID 37018314

  • Adaptive Region-Specific Loss for Improved Medical Image Segmentation. IEEE transactions on pattern analysis and machine intelligence Chen, Y., Yu, L., Wang, J., Panjwani, N., Obeid, J., Liu, W., Liu, L., Kovalchuk, N., Gensheimer, M. F., Vitzthum, L. K., Beadle, B. M., Chang, D. T., Le, Q., Han, B., Xing, L. 2023; PP

    Abstract

    Defining the loss function is an important part of neural network design and critically determines the success of deep learning modeling. A significant shortcoming of the conventional loss functions is that they weight all regions in the input image volume equally, despite the fact that the system is known to be heterogeneous (i.e., some regions can achieve high prediction performance more easily than others). Here, we introduce a region-specific loss to lift the implicit assumption of homogeneous weighting for better learning. We divide the entire volume into multiple sub-regions, each with an individualized loss constructed for optimal local performance. Effectively, this scheme imposes higher weightings on the sub-regions that are more difficult to segment, and vice versa. Furthermore, the regional false positive and false negative errors are computed for each input image during a training step and the regional penalty is adjusted accordingly to enhance the overall accuracy of the prediction. Using different public and in-house medical image datasets, we demonstrate that the proposed regionally adaptive loss paradigm outperforms conventional methods in the multi-organ segmentations, without any modification to the neural network architecture or additional data preparation.

    View details for DOI 10.1109/TPAMI.2023.3289667

    View details for PubMedID 37363838

  • Patient-specific Auto-segmentation on Daily kVCT Images for Adaptive Radiotherapy. International journal of radiation oncology, biology, physics Chen, Y., Gensheimer, M. F., Bagshaw, H. P., Butler, S., Yu, L., Zhou, Y., Shen, L., Kovalchuk, N., Surucu, M., Chang, D. T., Xing, L., Han, B. 2023

    Abstract

    This study explored deep learning-based patient-specific auto-segmentation using transfer learning on daily kVCT images to facilitate adaptive radiotherapy, based on data from the first group of patients treated with the innovative RefleXion system.For head and neck (HaN) site and pelvic site, a deep convolutional segmentation network was initially trained on a population dataset, which contained 67 and 56 patient cases respectively. Then the pre-trained population network was adapted to the specific RefleXion patient by fine-tuning the network weights with a transfer learning method. For each of the 6 collected RefleXion HaN cases and 4 pelvic cases, initial planning CT and 5-26 sets of daily RefleXion kVCT were used for the patient-specific learning and evaluation separately. The performance of the patient-specific network was compared with the population network and the clinical rigid registration method and evaluated by the Dice similarity coefficient (DSC) with manual contours being the reference. The corresponding dosimetric impacts resulting from different auto-segmentation and registration methods were also investigated.The proposed patient-specific network achieved mean DSC results of 0.88 for three HaN organs at risk (OARs) of interest and 0.90 for eight pelvic target and OARs, outperforming the population network (0.70 and 0.63) and the registration method (0.72 and 0.72). The DSC of the patient-specific network gradually increased with the increment of longitudinal training cases and approached saturation with more than 6 training cases. Compared with using the registration contour, the target and OAR mean doses and dose-volume histograms obtained using the patient-specific auto-segmentation were closer to the results using the manual contour.Auto-segmentation of RefleXion kVCT images based on the patient-specific transfer learning could achieve higher accuracy, outperforming a common population network and clinical registration-based method. This approach shows promise in improving dose evaluation accuracy in RefleXion adaptive radiotherapy.

    View details for DOI 10.1016/j.ijrobp.2023.04.026

    View details for PubMedID 37141982

  • Model-Based 3-D X-Ray Induced Acoustic Computerized Tomography. IEEE transactions on radiation and plasma medical sciences Pandey, P. K., Wang, S., Sun, L., Xing, L., Xiang, L. 2023; 7 (5): 532-543

    Abstract

    X-ray-induced acoustic (XA) computerized tomography (XACT) is an evolving imaging technique that aims to reconstruct the X-ray energy deposition from XA measurements. Main challenges in XACT are the poor signal-to-noise ratio and limited field-of-view, which cause artifacts in the images. We demonstrate the efficacy of model-based (MB) algorithms for three-dimensional XACT and compare with the traditional algorithms. The MB algorithm is based on iterative, matrix-free approach for regularized-least-squares minimization corresponding to XACT. The matrix-free-LSQR (MF-LSQR) and the non-iterative model-backprojection (MBP) reconstructions were evaluated and compared with universal backprojection (UBP), time-reversal (TR) and fast-Fourier transform (FFT)-based reconstructions for numerical and experimental XACT datasets. The results demonstrate the capability of MF-LSQR algorithm to reduce noisy artifacts thus yielding better reconstructions. MBP and MF-LSQR algorithms perform particularly well with the experimental XACT dataset, where noise in signals significantly affects the reconstruction of the target in UBP and FFT-based reconstructions. The TR reconstruction for experimental XACT are comparable to MF-LSQR, but takes thrice as much time and filters the frequency components greater than maximum frequency supported by the grid, resulting loss of resolution. The MB algorithms are able to overcome the challenges in XACT and hence are vital for the clinical translation of XACT.

    View details for DOI 10.1109/TRPMS.2023.3238017

    View details for PubMedID 38046375

    View details for PubMedCentralID PMC10691826

  • Model-Based 3-D X-Ray Induced Acoustic Computerized Tomography IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES Pandey, P., Wang, S., Sun, L., Xing, L., Xiang, L. 2023; 7 (5): 532-543
  • An Integrated 3D Printed Enclosure for a Radioluminescent-Based Phantom for Quality Assurance on a Robotic-Arm Linac. Physics in medicine and biology Ashraf, M. R., Gibson, C., Skinner, L. B., Gu, X., Xing, L., Wang, L. 2023

    Abstract

    To develop, characterize and improve upon a high-resolution 3D printed radioluminescence-based imaging phantom for quality assurance (QA) of a robotic arm linear accelerator. Approach: A phantom was constructed which consisted of a scintillating sheet, fiducial markers, a low-cost CMOS camera and a 3D printed light-tight enclosure. The camera, equipped with a 12 mm lens, was angled 45 degrees from the horizontal axis with a direct line of sight of the scintillating sheet. A perspective image transformation with optical distortion correction was employed to obtain beam's eye view images for different collimators. Beam profiles, Iris™ field size, MLC leaf positioning and central laser-radiation field coincidence QA tests were performed and compared against data obtained with gafchromic film. The phantom's short-term stability, sensitivity to changes in output, field size and leaf positioning were also assessed. Main Results: The limiting resolution of the optical system was measured to be ~ 0.26 mm. Field size, as measured by the radioluminescence system for Iris apertures, agreed to within 0.2 mm of the values measured using film. The imaging system was sensitive to field size changes well below 0.2 mm and output changes as small as 1 Monitor Unit (MU). For the optical setup, the mean leaf deviation error for banks X1 and X2 was 0.21 and 0.17 mm at 800 mm SAD, whereas the mean difference for the film dataset was 0.16 mm and 0.22 mm for banks X1 and X2, respectively. The optical system was able to detect leaf positioning errors as small as 0.2 mm. Compared with film data, excellent agreement was seen for relative central axis beam profiles for 10 mm and 5 mm beams. Significance: The phantom presented here is an alternative to film and electronic portal imager devices, due to its low-cost, portability, and high spatial and temporal resolution. .

    View details for DOI 10.1088/1361-6560/acd162

    View details for PubMedID 37116515

  • Learning image representations for content-based image retrieval of radiotherapy treatment plans. Physics in medicine and biology Huang, C., Vasudevan, V., Pastor-Serrano, O., Islam, M. T., Nomura, Y., Dubrowski, P., Wang, J. Y., Schulz, J. B., Yang, Y., Xing, L. 2023

    Abstract

    In this work, we propose a content-based image retrieval (CBIR) method for retrieving dose distributions of previously planned patients based on anatomical similarity. Retrieved dose distributions from this method can be incorporated into automated treatment planning workflows in order to streamline the iterative planning process. As CBIR has not yet been applied to treatment planning, our work seeks to understand which current machine learning models are most viable in this context.Our proposed CBIR method trains a representation model that produces latent space embeddings of a patient's anatomical information. The latent space embeddings of new patients are then compared against those of previous patients in a database for image retrieval of dose distributions. All source code for this project is available on github.The retrieval performance of various CBIR methods is evaluated on a dataset consisting of both publicly available image sets and clinical image sets from our institution. This study compares various encoding methods, ranging from simple autoencoders to more recent Siamese networks like SimSiam, and the best performance was observed for the multitask Siamese network.Our current results demonstrate that excellent image retrieval performance can be obtained through slight changes to previously developed Siamese networks. We hope to integrate CBIR into automated planning workflow in future works.

    View details for DOI 10.1088/1361-6560/accdb0

    View details for PubMedID 37068492

  • A probabilistic deep learning model of inter-fraction anatomical variations in radiotherapy. Physics in medicine and biology Pastor-Serrano, O., Habraken, S., Hoogeman, M. S., Lathouwers, D., Schaart, D. R., Nomura, Y., Xing, L., Perkó, Z. 2023

    Abstract

    In radiotherapy, the internal movement of organs between treatment sessions causes errors in the final radiation dose delivery. To assess the need for adaptation, motion models can be used to simulate dominant motion patterns and assess anatomical robustness before delivery. Traditionally, such models are based on principal component analysis (PCA) and are either patient-specific (requiring several scans per patient) or population-based, applying the same set of deformations to all patients. We present a hybrid approach which, based on population data, allows to predict patient-specific inter-fraction variations for an individual patient. Approach: We propose a deep learning probabilistic framework that generates deformation vector fields (DVFs) warping a patient's planning computed tomography (CT) into possible patient-specific anatomies. This daily anatomy model (DAM) uses few random variables capturing groups of correlated movements. Given a new planning CT, DAM estimates the joint distribution over the variables, with each sample from the distribution corresponding to a different deformation. We train our model using dataset of 312 CT pairs with prostate, bladder, and rectum delineations from 38 prostate cancer patients. For 2 additional patients (22 CTs), we compute the contour overlap between real and generated images, and compare the sampled and "ground truth" distributions of volume and center of mass changes. Results: With a DICE score of $0.86\pm0.05$ and a distance between prostate contours of $1.09\pm0.93$ mm, DAM matches and improves upon previously published PCA-based models, using as few as 8 latent variables. The overlap between distributions further indicates that DAM's sampled movements match the range and frequency of clinically observed daily changes on repeat CTs. Significance: Conditioned only on planning CT values and organ contours of a new patient without any pre-processing, DAM can accurately deformations seen during following treatment sessions, enabling anatomically robust treatment planning and robustness evaluation against inter-fraction anatomical changes.

    View details for DOI 10.1088/1361-6560/acc71d

    View details for PubMedID 36958058

  • Multibranch CNN With MLP-Mixer-Based Feature Exploration for High-Performance Disease Diagnosis IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Zhou, Z., Islam, M., Xing, L. 2023
  • Fully automated segmentally boosted VMAT. Medical physics Huang, C., Nomura, Y., Yang, Y., Xing, L. 2023

    Abstract

    Treatment planning for volumetric modulated arc therapy (VMAT) typically involves the use of multiple arcs to achieve sufficient intensity modulation. Alternatively, we can perform segment boosting to achieve similar intensity modulation while also reducing the number of control points used. Here, we propose the MetaPlanner Boosted VMAT (MPBV) approach, which generates boosted VMAT plans through a fully automated framework.The proposed MPBV approach is an open-source framework that consists of three main stages: meta-optimization of treatment plan hyperparameters, fast beam angle optimization on a coarse dose grid to select desirable segments for boosting, and final plan generation (i.e. constructing the boosted VMAT arc and performing optimization).Performance for the MPBV approach is evaluated on 21 prostate cases and 6 head and neck cases using clinically relevant plan quality metrics (i.e. target coverage, dose conformity, dose homogeneity, and OAR sparing). As compared to two baseline methods with multiple arcs, MPBV maintains or improves dosimetric performance for the evaluated metrics while substantially reducing average estimated delivery times (from 2.6 to 2.1 minutes).Our proposed MPBV approach provides an automated framework for producing high quality VMAT plans that uses fewer control points and reduces delivery time as compared to traditional approaches with multiple arcs. MPBV applies automated treatment planning to segmentally boosted VMAT to address the beam utilization inefficiencies of traditional VMAT approaches that use multiple full arcs. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.16295

    View details for PubMedID 36779662

  • Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data. Nature communications Islam, M. T., Xing, L. 2023; 14 (1): 679

    Abstract

    Remarkable advances in single cell genomics have presented unique challenges and opportunities for interrogating a wealth of biomedical inquiries. High dimensional genomic data are inherently complex because of intertwined relationships among the genes. Existing methods, including emerging deep learning-based approaches, do not consider the underlying biological characteristics during data processing, which greatly compromises the performance of data analysis and hinders the maximal utilization of state-of-the-art genomic techniques. In this work, we develop an entropy-based cartography strategy to contrive the high dimensional gene expression data into a configured image format, referred to as genomap, with explicit integration of the genomic interactions. This unique cartography casts the gene-gene interactions into the spatial configuration of genomaps and enables us to extract the deep genomic interaction features and discover underlying discriminative patterns of the data. We show that, for a wide variety of applications (cell clustering and recognition, gene signature extraction, single cell data integration, cellular trajectory analysis, dimensionality reduction, and visualization), the proposed approach drastically improves the accuracies of data analyses as compared to the state-of-the-art techniques.

    View details for DOI 10.1038/s41467-023-36383-6

    View details for PubMedID 36755047

  • Image classification using graph neural network and multiscale wavelet superpixels PATTERN RECOGNITION LETTERS Vasudevan, V., Bassenne, M., Islam, M., Xing, L. 2023; 166: 89-96
  • Sub-second photon dose prediction via transformer neural networks. Medical physics Pastor-Serrano, O., Dong, P., Huang, C., Xing, L., Perkó, Z. 2023

    Abstract

    Fast dose calculation is critical for online and real time adaptive therapy workflows. While modern physics-based dose algorithms must compromise accuracy to achieve low computation times, deep learning models can potentially perform dose prediction tasks with both high fidelity and speed.We present a deep learning algorithm that, exploiting synergies between Transformer and convolutional layers, accurately predicts broad photon beam dose distributions in few milliseconds.The proposed improved Dose Transformer Algorithm (iDoTA) maps arbitrary patient geometries and beam information (in the form of a 3D projected shape resulting from a simple ray tracing calculation) to their corresponding 3D dose distribution. Treating the 3D CT input and dose output volumes as a sequence of 2D slices along the direction of the photon beam, iDoTA solves the dose prediction task as sequence modeling. The proposed model combines a Transformer backbone routing long-range information between all elements in the sequence, with a series of 3D convolutions extracting local features of the data. We train iDoTA on a dataset of 1700 beam dose distributions, using 11 clinical volumetric modulated arc therapy (VMAT) plans (from prostate, lung and head and neck cancer patients with 194-354 beams per plan) to assess its accuracy and speed.iDoTA predicts individual photon beams in ≈ 50 milliseconds with a high gamma pass rate of 97.72±1.93% (2 mm, 2%). Furthermore, estimating full VMAT dose distributions in 6-12 seconds, iDoTA achieves state-of-the-art performance with a 99.51±0.66% (2 mm, 2%) pass rate and an average relative dose error of 0.75±0.36%.Offering the millisecond speed prediction per beam angle needed in online and real-time adaptive treatments, iDoTA represents a new state of the art in data-driven photon dose calculation. The proposed model can massively speed-up current photon workflows, reducing calculation times from few minutes to just a few seconds. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.16231

    View details for PubMedID 36669122

  • Modeling linear accelerator (Linac) beam data by implicit neural representation learning for commissioning and quality assurance applications. Medical physics Liu, L., Shen, L., Yang, Y., Schüler, E., Zhao, W., Wetzstein, G., Xing, L. 2023

    Abstract

    Linear accelerator (Linac) beam data commissioning and quality assurance (QA) play a vital role in accurate radiation treatment delivery and entail a large number of measurements using a variety of field sizes. How to optimize the effort in data acquisition while maintaining high quality of medical physics practice has been sought after.We propose to model Linac beam data through implicit neural representation (NeRP) learning. The potential of the beam model in predicting beam data from sparse measurements and detecting data collection errors was evaluated, with the goal of using the beam model to verify beam data collection accuracy and simplify the commissioning and QA process.NeRP models with continuous and differentiable functions parameterized by multilayer perceptrons (MLPs) were used to represent various beam data including percentage depth dose and profiles of 6 MV beams with and without flattening filter. Prior knowledge of the beam data was embedded into the MLP network by learning the NeRP of a vendor-provided "golden" beam dataset. The prior-embedded network was then trained to fit clinical beam data collected at one field size and used to predict beam data at other field sizes. We evaluated the prediction accuracy by comparing network-predicted beam data to water tank measurements collected from 14 clinical Linacs. Beam datasets with intentionally introduced errors were used to investigate the potential use of the NeRP model for beam data verification, by evaluating the model performance when trained with erroneous beam data samples.Linac beam data predicted by the model agreed well with water tank measurements, with averaged Gamma passing rates (1%/1mm passing criteria) higher than 95% and averaged mean absolute errors less than 0.6%. Beam data samples with measurement errors were revealed by inconsistent beam predictions between networks trained with correct versus erroneous data samples, characterized by a Gamma passing rate lower than 90%.A NeRP beam data modeling technique has been established for predicting beam characteristics from sparse measurements. The model provides a valuable tool to verify beam data collection accuracy and promises to simplify commissioning/QA processes by reducing the number of measurements without compromising the quality of medical physics service. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.16212

    View details for PubMedID 36621812

  • Potential of educational cystoscopy atlas for augmented intelligence Eminaga, O., Laurie, M., Lee, T., Jia, X., Liao, J. C. 2023

    View details for DOI 10.1117/12.2650920

  • Consistency-Guided Meta-learning for Bootstrapping Semi-supervised Medical Image Segmentation Wei, Q., Yu, L., Li, X., Shao, W., Xie, C., Xing, L., Zhou, Y., Greenspan, H., Madabhushi, A., Mousavi, P., Salcudean, S., Duncan, J., Syeda-Mahmood, T., Taylor, R. SPRINGER INTERNATIONAL PUBLISHING AG. 2023: 183-193
  • PINER: Prior-informed Implicit Neural Representation Learning for Test-time Adaptation in Sparse-view CT Reconstruction Song, B., Shen, L., Xing, L., IEEE IEEE COMPUTER SOC. 2023: 1928-1937
  • Bladder Cancer and Artificial Intelligence: Emerging Applications Urologic Clinics North America Laurie, M., Zhou, S. R., Islam, M., Shkolyar, E., Xing, L., Liao, J. C. 2023
  • Flat lesion detection of white light cystoscopy with deep learning Jia, X., Shkolyar, E., Eminaga, O., Laurie, M., Zhou, Z., Lee, T., Islam, M., Meng, M. Q., Liao, J. C., Xing, L. 2023

    View details for DOI 10.1117/12.2650583

  • Sequential modeling for cystoscopic image classification Laurie, M., Eminaga, O., Shkolyar, E., Jia, X., Lee, T., Long, J., Islam, M., Lau, H., Xing, L., Liao, J. C. 2023

    View details for DOI 10.1117/12.2649334

  • Leveraging data-driven self-consistency for high-fidelity gene expression recovery. Nature communications Islam, M. T., Wang, J., Ren, H., Li, X., Khuzani, M. B., Sang, S., Yu, L., Shen, L., Zhao, W., Xing, L. 2022; 13 (1): 7142

    Abstract

    Single cell RNA sequencing is a promising technique to determine the states of individual cells and classify novel cell subtypes. In current sequence data analysis, however, genes with low expressions are omitted, which leads to inaccurate gene counts and hinders downstream analysis. Recovering these omitted expression values presents a challenge because of the large size of the data. Here, we introduce a data-driven gene expression recovery framework, referred to as self-consistent expression recovery machine (SERM), to impute the missing expressions. Using a neural network, the technique first learns the underlying data distribution from a subset of the noisy data. It then recovers the overall expression data by imposing a self-consistency on the expression matrix, thus ensuring that the expression levels are similarly distributed in different parts of the matrix. We show that SERM improves the accuracy of gene imputation with orders of magnitude enhancement in computational efficiency in comparison to the state-of-the-art imputation techniques.

    View details for DOI 10.1038/s41467-022-34595-w

    View details for PubMedID 36414658

  • Image-mode performance characterization of a positron emission tomography subsystem designed for Biology-guided radiotherapy (BgRT). The British journal of radiology Hu, Z., Bieniosek, M., Ferri, V., Iagaru, A., Kovalchuk, N., Han, B., Xing, L., Vitzthum, L., Olcott, P., Narayanan, M., Laurence, T., Ren, Y., Oderinde, O. M., Shirvani, S. M., Chang, D., Surucu, M. 2022: 20220387

    Abstract

    OBJECTIVES: In this study, we characterize the imaging-mode performance of the positron emission tomography (PET) subsystem of the RefleXion X1 machine using the NEMA NU-2 2018 standard.METHODS: The X1 machine consists of two symmetrically opposing 900 arcs of PET detectors incorporated into the architecture of a ring-gantry linear accelerator rotating up to 60RPM. PET emissions from a tumor are detected by the PET detectors and used to guide the delivery of radiation beam. Imaging performance of the PET subsystem on X1 machine was evaluated based on1 sensitivity of the PET detectors,2 spatial resolution,3 count-loss performance,4 Image quality, and daily system performance check.RESULTS: PET subsystem sensitivity was measured as 0.183 and 0.161 cps/kBq at the center and off-center positions, respectively. Spatial resolution: average FWHM values of 4.3, 5.1, and 6.7mm for the point sources at 1, 10, and 20cm off center, respectively were recorded. For count loss, max NECR: 2.63 kcps, max true coincidence rate: 5.56 kcps, and scatter fraction: 39.8%. The 10mm sphere was not visible. Image-quality contrast values were: 29.6%, 64.9%, 66.5%, 81.8%, 81.2%, and background variability: 14.8%, 12.4%, 10.3%, 8.8%, 8.3%, for the 13, 17, 22, 28, 37mm sphere sizes, respectively.CONCLUSIONS: When operating in an imaging mode, the spatial resolution and image contrast of the X1 PET subsystem were comparable to those of typical diagnostic imaging systems for large spheres, while the sensitivity and count rate were lower due to the significantly smaller PET detector area in the X1 system. Clinical efficacy when used in BgRT remains to be validated.ADVANCES IN KNOWLEDGE: This is the first performance evaluation of the PET subsystem on the novel BgRT machine. The dual arcs rotating PET subsystem on RefleXion X1 machine performance is comparable to those of the typical diagnostic PET system based on the spatial resolution and image contrast for larger spheres.

    View details for DOI 10.1259/bjr.20220387

    View details for PubMedID 36317922

  • Deep Learning-Based Water-Fat Separation from Dual-Echo Chemical Shift-Encoded Imaging. Bioengineering (Basel, Switzerland) Wu, Y., Alley, M., Li, Z., Datta, K., Wen, Z., Sandino, C., Syed, A., Ren, H., Xing, L., Lustig, M., Pauly, J., Vasanawala, S. 2022; 9 (10)

    Abstract

    Conventional water-fat separation approaches suffer long computational times and are prone to water/fat swaps. To solve these problems, we propose a deep learning-based dual-echo water-fat separation method. With IRB approval, raw data from 68 pediatric clinically indicated dual echo scans were analyzed, corresponding to 19382 contrast-enhanced images. A densely connected hierarchical convolutional network was constructed, in which dual-echo images and corresponding echo times were used as input and water/fat images obtained using the projected power method were regarded as references. Models were trained and tested using knee images with 8-fold cross validation and validated on out-of-distribution data from the ankle, foot, and arm. Using the proposed method, the average computational time for a volumetric dataset with ~400 slices was reduced from 10 min to under one minute. High fidelity was achieved (correlation coefficient of 0.9969, l1 error of 0.0381, SSIM of 0.9740, pSNR of 58.6876) and water/fat swaps were mitigated. I is of particular interest that metal artifacts were substantially reduced, even when the training set contained no images with metallic implants. Using the models trained with only contrast-enhanced images, water/fat images were predicted from non-contrast-enhanced images with high fidelity. The proposed water-fat separation method has been demonstrated to be fast, robust, and has the added capability to compensate for metal artifacts.

    View details for DOI 10.3390/bioengineering9100579

    View details for PubMedID 36290546

  • An Efficient Framework for Video Documentation of Bladder Lesions for Cystoscopy: A Proof-of-Concept Study. Journal of medical systems Eminaga, O., Ge, T. J., Shkolyar, E., Laurie, M. A., Lee, T. J., Hockman, L., Jia, X., Xing, L., Liao, J. C. 2022; 46 (11): 73

    Abstract

    Processing full-length cystoscopy videos is challenging for documentation and research purposes. We therefore designed a surgeon-guided framework to extract short video clips with bladder lesions for more efficient content navigation and extraction. Screenshots of bladder lesions were captured during transurethral resection of bladder tumor, then manually labeled according to case identification, date, lesion location, imaging modality, and pathology. The framework used the screenshot to search for and extract a corresponding 10-seconds video clip. Each video clip included a one-second space holder with a QR barcode informing the video content. The success of the framework was measured by the secondary use of these short clips and the reduction of storage volume required for video materials. From 86 cases, the framework successfully generated 249 video clips from 230 screenshots, with 14 erroneous video clips from 8 screenshots excluded. The HIPPA-compliant barcodes provided information of video contents with a 100% data completeness. A web-based educational gallery was curated with various diagnostic categories and annotated frame sequences. Compared with the unedited videos, the informative short video clips reduced the storage volume by 99.5%. In conclusion, our framework expedites the generation of visual contents with surgeon's instruction for cystoscopy and potential incorporation of video data towards applications including clinical documentation, education, and research.

    View details for DOI 10.1007/s10916-022-01862-8

    View details for PubMedID 36190581

  • Radio-luminescent imaging for rapid, high resolution eye plaque loading verification. Medical physics Yan, H., De Jean, P., Grafil, E., Ashraf, R., Niedermayr, T., Astrahan, M., Mruthyunjaya, P., Beadle, B., Xing, L., Liu, W. 2022

    Abstract

    BACKGROUND: Eye plaque brachytherapy (EPB) is currently an optimal therapy for intraocular cancers. Due to the lack of an effective and practical technique to measure the seed radioactivity distribution, current quality assurance (QA) practice according to the AAPM TG129 only stipulates that the plaque assembly be visually inspected. Consequently, uniform seed activity is routinely adopted to avoid possible loading mistakes of differential seed loading. However, modulated dose delivery, which represents a general trend in radiotherapy to provide more personalized treatment for a given tumor and patient, requires differential activities in the loaded seeds.PURPOSE: In this study, a fast and low-cost radio-luminescent imaging and dose calculating system to verify the seed activity distribution for differential loading was developed.METHODS: A proof-of-concept system consisting of a thin scintillator sheet coupled to a camera/lens system was constructed. A seed-loaded plaque can be placed directly on the scintillator surface with the radioactive seeds facing the scintillator. The camera system collects the radioluminescent signal generated by the scintillator at its opposite side. The predicted dose distribution in the scintillator's sensitive layer was calculated using a Monte Carlo simulation with the planned plaque loading pattern of I-125 seeds. Quantitative comparisons of the distribution of relative measured signal intensity and that of the relative predicted dose in the sensitive layer were performed by gamma analysis, similar to IMRT QA.RESULTS: Data analyses showed high gamma (3%/0.3mm, global, 20% threshold) passing rates for correct seed loadings and low passing rates with distinguished high gamma value area for incorrect loadings, indicating that possible errors may be detected. The measurement and analysis only required a few extra minutes, significantly shorter than the time to assay the extra verification seeds the physicist already must perform as recommended by TG129.CONCLUSIONS: Radio-luminescent QA can be used to facilitate and assure the implementation of intensity modulated, customized plaque loading. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.16003

    View details for PubMedID 36183146

  • Small-Object Sensitive Segmentation Using Across Feature Map Attention. IEEE transactions on pattern analysis and machine intelligence Sang, S., Zhou, Y., Islam, M. T., Xing, L. 2022; PP

    Abstract

    Semantic segmentation is an important step in understanding the scene for many practical applications such as autonomous driving. Although Deep Convolutional Neural Networks-based methods have significantly improved segmentation accuracy, small/thin objects remain challenging to segment due to convolutional and pooling operations that result in information loss, especially for small objects. This paper presents a novel attention-based method called Across Feature Map Attention (AFMA) to address this challenge. It quantifies the inner-relationship between small and large objects belonging to the same category by utilizing the different feature levels of the original image. The AFMA could compensate for the loss of high-level feature information of small objects and improve the small/thin object segmentation. Our method can be used as an efficient plug-in for a wide range of existing architectures and produces much more interpretable feature representation than former studies. Extensive experiments on eight widely used segmentation methods and other existing small-object segmentation models on CamVid and Cityscapes demonstrate that our method substantially and consistently improves the segmentation of small/thin objects.

    View details for DOI 10.1109/TPAMI.2022.3211171

    View details for PubMedID 36178991

  • Mitigating the uncertainty in small field dosimetry by leveraging machine learning strategies. Physics in medicine and biology Zhao, W., Yang, Y., Xing, L., Chuang, C. F., Schüler, E. 2022

    Abstract

    Small field dosimetry is significantly different from the dosimetry of broad beams due to loss of electron side scatter equilibrium, source occlusion, and effects related to the choice of detector. However, use of small fields is increasing with the increase in indications for intensity-modulated radiation therapy (IMRT) and stereotactic body radiation therapy (SBRT), and thus the need for accurate dosimetry is ever more important. Here we propose to leverage machine learning (ML) strategies to reduce the uncertainties and increase the accuracy in determining small field output factors (OFs). Linac OFs from a Varian TrueBeam STx were calculated either by the treatment planning system (TPS) or measured with a W1 scintillator detector at various multi-leaf collimator (MLC) positions, jaw positions, and with and without contribution from leaf-end transmission. The fields were defined by the MLCs with the jaws at various positions. Field sizes between 5 and 100 mm were evaluated. Separate ML regression models were generated based on the TPS calculated or the measured datasets. Accurate predictions of small field OFs at different field sizes (FSs) were achieved independent of jaw and MLC position. A mean and maximum % relative error (RE) of 0.380.39% and 3.62%, respectively, for the best-performing models based on the measured datasets were found. The prediction accuracy was independent of contribution from leaf-end transmission. Several ML models for predicting small field OFs were generated, validated, and tested. Incorporating these models into the dose calculation workflow could greatly increase the accuracy and robustness of dose calculations for any radiotherapy delivery technique that relies heavily on small fields.

    View details for DOI 10.1088/1361-6560/ac7fd6

    View details for PubMedID 35803256

  • Shifting machine learning for healthcare from development to deployment and from models to data. Nature biomedical engineering Zhang, A., Xing, L., Zou, J., Wu, J. C. 2022

    Abstract

    In the past decade, the application of machine learning (ML) to healthcare has helped drive the automation of physician tasks as well as enhancements in clinical capabilities and access to care. This progress has emphasized that, from model development to model deployment, data play central roles. In this Review, we provide a data-centric view of the innovations and challenges that are defining ML for healthcare. We discuss deep generative models and federated learning as strategies to augment datasets for improved model performance, as well as the use of the more recent transformer models for handling larger datasets and enhancing the modelling of clinical text. We also discuss data-focused problems in the deployment of ML, emphasizing the need to efficiently deliver data to ML models for timely clinical predictions and to account for natural data shifts that can deteriorate model performance.

    View details for DOI 10.1038/s41551-022-00898-y

    View details for PubMedID 35788685

  • Real Time Volumetric MRI for 3D Motion Tracking via Geometry-Informed Deep Learning. Medical physics Liu, L., Shen, L., Johansson, A., Balter, J. M., Cao, Y., Chang, D., Xing, L. 2022

    Abstract

    To develop a geometry-informed deep learning framework for volumetric MRI with sub-second acquisition time in support of 3D motion tracking, which is highly desirable for improved radiotherapy precision but hindered by the long image acquisition time.A 2D-3D deep learning network with an explicitly defined geometry module that embeds geometric priors of the k-space encoding pattern was investigated, where a 2D generation network first augmented the sparsely sampled image dataset by generating new 2D representations of the underlying 3D subject. A geometry module then unfolded the 2D representations to the volumetric space. Finally, a 3D refinement network took the unfolded 3D data and outputted high-resolution volumetric images. Patient-specific models were trained for 7 abdominal patients to reconstruct volumetric MRI from both orthogonal cine slices and sparse radial samples. To evaluate the robustness of the proposed method to longitudinal patient anatomy and position changes, we tested the trained model on separate datasets acquired more than one month later and evaluated 3D target motion tracking accuracy using the model-reconstructed images by deforming a reference MRI with gross tumor volume (GTV) contours to a 5-min time series of both ground truth and model-reconstructed volumetric images with a temporal resolution of 340 ms.Across the 7 patients evaluated, the median distances between model-predicted and ground truth GTV centroids in the superior-inferior direction were 0.4±0.3 mm and 0.5±0.4 mm for cine and radial acquisitions respectively. The 95-percentile Hausdorff distances between model-predicted and ground truth GTV contours were 4.7±1.1 mm and 3.2±1.5 mm for cine and radial acquisitions, which are of the same scale as cross-plane image resolution.Incorporating geometric priors into deep learning model enables volumetric imaging with high spatial and temporal resolution, which is particularly valuable for 3D motion tracking and has the potential of greatly improving MRI-guided radiotherapy precision. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.15822

    View details for PubMedID 35766221

  • Artificial Intelligence-Based Prognostic Model for Urologic Cancers: A SEER-Based Study. Cancers Eminaga, O., Shkolyar, E., Breil, B., Semjonow, A., Boegemann, M., Xing, L., Tinay, I., Liao, J. C. 2022; 14 (13)

    Abstract

    BACKGROUND: Prognostication is essential to determine the risk profile of patients with urologic cancers.METHODS: We utilized the SEER national cancer registry database with approximately 2 million patients diagnosed with urologic cancers (penile, testicular, prostate, bladder, ureter, and kidney). The cohort was randomly divided into the development set (90%) and the out-held test set (10%). Modeling algorithms and clinically relevant parameters were utilized for cancer-specific mortality prognosis. The model fitness for the survival estimation was assessed using the differences between the predicted and observed Kaplan-Meier estimates on the out-held test set. The overall concordance index (c-index) score estimated the discriminative accuracy of the survival model on the test set. A simulation study assessed the estimated minimum follow-up duration and time points with the risk stability.RESULTS: We achieved a well-calibrated prognostic model with an overall c-index score of 0.800 (95% CI: 0.795-0.805) on the representative out-held test set. The simulation study revealed that the suggestions for the follow-up duration covered the minimum duration and differed by the tumor dissemination stages and affected organs. Time points with a high likelihood for risk stability were identifiable.CONCLUSIONS: A personalized temporal survival estimation is feasible using artificial intelligence and has potential application in clinical settings, including surveillance management.

    View details for DOI 10.3390/cancers14133135

    View details for PubMedID 35804904

  • A geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction. Computers in biology and medicine Shen, L., Zhao, W., Capaldi, D., Pauly, J., Xing, L. 2022: 105710

    Abstract

    Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging. However, the pure data-driven nature of deep learning models may limit the model generalizability and application scope. Here we establish a geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction. We introduce a novel mechanism for integrating geometric priors of the imaging system. We demonstrate that the seamless inclusion of known priors is essential to enhance the performance of 3D volumetric computed tomography imaging with ultra-sparse sampling. The study opens new avenues for data-driven biomedical imaging and promises to provide substantially improved imaging tools for various clinical imaging and image-guided interventions.

    View details for DOI 10.1016/j.compbiomed.2022.105710

    View details for PubMedID 35715260

  • NeRP: Implicit Neural Representation Learning With Prior Embedding for Sparsely Sampled Image Reconstruction. IEEE transactions on neural networks and learning systems Shen, L., Pauly, J., Xing, L. 2022; PP

    Abstract

    Image reconstruction is an inverse problem that solves for a computational image based on sampled sensor measurement. Sparsely sampled image reconstruction poses additional challenges due to limited measurements. In this work, we propose a methodology of implicit Neural Representation learning with Prior embedding (NeRP) to reconstruct a computational image from sparsely sampled measurements. The method differs fundamentally from previous deep learning-based image reconstruction approaches in that NeRP exploits the internal information in an image prior and the physics of the sparsely sampled measurements to produce a representation of the unknown subject. No large-scale data is required to train the NeRP except for a prior image and sparsely sampled measurements. In addition, we demonstrate that NeRP is a general methodology that generalizes to different imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). We also show that NeRP can robustly capture the subtle yet significant image changes required for assessing tumor progression.

    View details for DOI 10.1109/TNNLS.2022.3177134

    View details for PubMedID 35657845

  • Operator splitting for adaptive radiation therapy with nonlinear health dynamics OPTIMIZATION METHODS & SOFTWARE Fu, A., Xing, L., Boyd, S. 2022
  • Treatment planning system commissioning of the first clinical biology-guided radiotherapy machine. Journal of applied clinical medical physics Simiele, E., Capaldi, D., Breitkreutz, D., Han, B., Yeung, T., White, J., Zaks, D., Owens, M., Maganti, S., Xing, L., Surucu, M., Kovalchuk, N. 2022: e13638

    Abstract

    PURPOSE: The RefleXion X1 is a novel radiotherapy machine designed for image-guided radiotherapy (IGRT) and biology-guided radiotherapy (BgRT). Its treatment planning system (TPS) generates IMRT and SBRT plans for a 6MV-FFF beam delivered axially via 50 firing positions with the couch advancing every 2.1mm. The purpose of this work is to report the TPS commissioning results for the first clinical installation of RefleXion X1.METHODS: CT images of multiple phantoms were imported into the RefleXion TPS to evaluate the accuracy of data transfer, anatomical modeling, plan evaluation, and dose calculation. Comparisons were made between the X1, Eclipse, and MIM. Dosimetric parameters for open static fields were evaluated in water and heterogeneous slab phantoms. Representative clinical IMRT and SBRT cases were planned and verified with ion chamber, film, and ArcCHECK@ measurements. The agreement between TPS and measurements for various clinical plans was evaluated using Gamma analysis with a criterion of 3%/2mm for ArcCHECK@ and film. End-to-end (E2E) testing was performed using anthropomorphic head and lung phantoms.RESULTS: The average difference between the TPS-reported and known HU values was -1.4 ± 6.0 HU. For static fields, the agreements between the TPS-calculated and measured PDD10 , crossline profiles, and inline profiles (FWHM) were within 1.5%, 1.3%, and 0.5mm, respectively. Measured output factors agreed with the TPS within 1.3%. Measured and calculated dose for static fields in heterogeneous phantoms agreed within 2.5%. The ArcCHECK@ mean absolute Gamma passing rate was 96.4% ± 3.4% for TG 119 and TG 244 plans and 97.8% ± 3.6% for the 21 clinical plans. E2E film analysis showed 0.8mm total targeting error for isocentric and 1.1mm for off-axis treatments.CONCLUSIONS: The TPS commissioning results of the RefleXion X1 TPS were within the tolerances specified by AAPM TG 53, MPPG 5.a, TG 119, and TG 148. A subset of the commissioning tests has been identified as baseline data for an ongoing QA program.

    View details for DOI 10.1002/acm2.13638

    View details for PubMedID 35644039

  • Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study LANCET DIGITAL HEALTH Jiang, Y., Zhang, Z., Yuan, Q., Wang, W., Wang, H., Li, T., Huang, W., Xie, J., Chen, C., Sun, Z., Yu, J., Xu, Y., Poultsides, G. A., Xing, L., Zhou, Z., Li, G., Li, R. 2022; 4 (5): E340-E350
  • Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study. The Lancet. Digital health Jiang, Y., Zhang, Z., Yuan, Q., Wang, W., Wang, H., Li, T., Huang, W., Xie, J., Chen, C., Sun, Z., Yu, J., Xu, Y., Poultsides, G. A., Xing, L., Zhou, Z., Li, G., Li, R. 2022; 4 (5): e340-e350

    Abstract

    BACKGROUND: Peritoneal recurrence is the predominant pattern of relapse after curative-intent surgery for gastric cancer and portends a dismal prognosis. Accurate individualised prediction of peritoneal recurrence is crucial to identify patients who might benefit from intensive treatment. We aimed to develop predictive models for peritoneal recurrence and prognosis in gastric cancer.METHODS: In this retrospective multi-institution study of 2320 patients, we developed a multitask deep learning model for the simultaneous prediction of peritoneal recurrence and disease-free survival using preoperative CT images. Patients in the training cohort (n=510) and the internal validation cohort (n=767) were recruited from Southern Medical University, Guangzhou, China. Patients in the external validation cohort (n=1043) were recruited from Sun Yat-sen University Cancer Center, Guangzhou, China. We evaluated the prognostic accuracy of the model as well as its association with chemotherapy response. Furthermore, we assessed whether the model could improve the ability of clinicians to predict peritoneal recurrence.FINDINGS: The deep learning model had a consistently high accuracy in predicting peritoneal recurrence in the training cohort (area under the receiver operating characteristic curve [AUC] 0·857; 95% CI 0·826-0·889), internal validation cohort (0·856; 0·829-0·882), and external validation cohort (0·843; 0·819-0·866). When informed by the artificial intelligence (AI) model, the sensitivity and inter-rater agreement of oncologists for predicting peritoneal recurrence was improved. The model was able to predict disease-free survival in the training cohort (C-index 0·654; 95% CI 0·616-0·691), internal validation cohort (0·668; 0·643-0·693), and external validation cohort (0·610; 0·583-0·636). In multivariable analysis, the model predicted peritoneal recurrence and disease-free survival independently of clinicopathological variables (p<0·0001 for all). For patients with a predicted high risk of peritoneal recurrence and low survival, adjuvant chemotherapy was associated with improved disease-free survival in both stage II disease (hazard ratio [HR] 0·543 [95% CI 0·362-0·815]; p=0·003) and stage III disease (0·531 [0·432-0·652]; p<0·0001). By contrast, chemotherapy had no impact on disease-free survival for patients with a predicted low risk of peritoneal recurrence and high survival. For the remaining patients, the benefit of chemotherapy depended on stage: only those with stage III disease derived benefit from chemotherapy (HR 0·637 [95% CI 0·484-0·838]; p=0·001).INTERPRETATION: The deep learning model could allow accurate prediction of peritoneal recurrence and survival in patients with gastric cancer. Prospective studies are required to test the clinical utility of this model in guiding personalised treatment in combination with clinicopathological criteria.FUNDING: None.

    View details for DOI 10.1016/S2589-7500(22)00040-1

    View details for PubMedID 35461691

  • Beam commissioning of the first clinical biology-guided radiotherapy system. Journal of applied clinical medical physics Han, B., Capaldi, D., Kovalchuk, N., Simiele, E., White, J., Zaks, D., Xing, L., Surucu, M. 2022: e13607

    Abstract

    This study reports the beam commissioning results for the first clinical RefleXion Linac.METHODS: The X1 produces a 6MV photon beam and the maximum clinical field size is 40*2cm2 at source-to-axis distance of 85cm. Treatment fields are collimated by a binary multileaf collimator (MLC) system with 64 leaves with width of 0.625cm and y-jaw pairs to provide either a 1 or 2cm opening. The mechanical alignment of the radiation source, the y-jaw, and MLC were checked with film and ion chambers. The beam parameters were characterized using a diode detector in a compact water tank. In-air lateral profiles and in-water percentage depth dose (PDD) were measured for beam modeling of the treatment planning system (TPS). The lateral profiles, PDDs, and output factors were acquired for field sizes from 1.25*1 to 40*2cm2 field to verify the beam modeling. The rotational output variation and synchronicity were tested to check the gantry angle, couch motion, and gantry rotation.RESULTS: The source misalignments were 0.049mm in y-direction, 0.66% out-of-focus in x-direction. The divergence of the beam axis was 0.36mm with a y-jaw twist of 0.03°. Clinical off-axis treatment fields shared a common center in y-direction were within 0.03mm. The MLC misalignment and twist were 0.57mm and 0.15°. For all measured fields ranging from the size from 1.25*1 to 40*2cm2 , the mean difference between measured and TPS modeled PDD at 10cm depth was -0.3%. The mean transverse profile difference in the field core was -0.3%±1.1%. The full-width half maximum (FWHM) modeling was within 0.5mm. The measured output factors agreed with TPS within 0.8%.CONCLUSIONS: This study summarizes our specific experience commissioning the first novel RefleXion linac, which may assist future users of this technology when implementing it into their own clinics.

    View details for DOI 10.1002/acm2.13607

    View details for PubMedID 35482018

  • Implicit neural representation for radiation therapy dose distribution. Physics in medicine and biology Vasudevan, V., Shen, L., Huang, C., Chuang, C. F., Islam, M. T., Ren, H., Yang, Y., Dong, P., Xing, L. 2022

    Abstract

    OBJECTIVE: Dose distribution data plays a pivotal role in radiotherapy treatment planning. The data is typically represented using voxel grids, and its size ranges from 10^6--10^8. A concise representation of the treatment plan is of great value in facilitating treatment planning and downstream applications. This work aims to develop an implicit neural representation of 3D dose distribution data.APPROACH: Instead of storing the dose values at each voxel, in the proposed approach, the weights of a multilayer perceptron (MLP) are employed to characterize the dosimetric data for plan representation and subsequent applications. We train a coordinate-based MLP with sinusoidal activations to map the voxel spatial coordinates to the corresponding dose values. We identify the best architecture for a given parameter budget and use that to train a model for each patient. The trained MLP is evaluated at each voxel location to reconstruct the dose distribution. We perform extensive experiments on dose distributions of prostate, spine, and head and neck tumor cases to evaluate the quality of the proposed representation. We also study the change in representation quality by varying model size and activation function.MAIN RESULTS: Using coordinate-based MLPs with sinusoidal activations, we can learn implicit representations that achieve a mean-squared error of 10^{-6} and peak signal-to-noise ratio greater than 50 dB at a target bitrate of ~1 across all the datasets, with a compression ratio of ~32. Our results also show that model sizes with a bitrate of 1--2 achieve optimal accuracy. For smaller bitrates, performance starts to drop significantly.SIGNIFICANCE: The proposed model provides a low-dimensional, implicit, and continuous representation of 3D dose data. In summary, given a dose distribution, we systematically show how to find a compact model to fit the data accurately. This study lays the groundwork for future applications of neural representations of dose data in radiation oncology.

    View details for DOI 10.1088/1361-6560/ac6b10

    View details for PubMedID 35477171

  • Meta-optimization for fully automated radiation therapy treatment planning. Physics in medicine and biology Huang, C., Nomura, Y., Yang, Y., Xing, L. 2022

    Abstract

    OBJECTIVE: Radiation therapy treatment planning is a time-consuming process involving iterative adjustments of hyperparameters. To automate the treatment planning process, we propose a meta-optimization framework, called MetaPlanner (MP).APPROACH: Our MP algorithm automates planning by performing meta-optimization of treatment planning hyperparameters. The algorithm uses a derivative-free method (i.e. parallel Nelder-Mead simplex search) to search for weight configurations that minimize a meta-scoring function. Meta-scoring is performed by constructing a tier list of the relevant considerations (e.g. dose homogeneity, conformity, spillage, and OAR sparing) to mimic the clinical decision-making process. Additionally, we have made our source code publicly available via github.MAIN RESULTS: The proposed MP method is evaluated on two datasets (21 prostate cases and 6 head and neck cases) collected as part of clinical workflow. MP is applied to both IMRT and VMAT planning and compared to a baseline of manual VMAT plans. MP in both IMRT and VMAT scenarios has comparable or better performance than manual VMAT planning for all evaluated metrics.SIGNIFICANCE: Our proposed MP provides a general framework for fully automated treatment planning that produces high quality treatment plans. Our MP method promises to substantially reduce the workload of treatment planners while maintaining or improving plan quality.

    View details for DOI 10.1088/1361-6560/ac5672

    View details for PubMedID 35176734

  • Dose Prediction for Cervical Cancer Brachytherapy Using 3-D Deep Convolutional Neural Network IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES Ma, M., Kidd, E., Fahimian, B. P., Han, B., Niedermayr, T. R., Hristov, D., Xing, L., Yang, Y. 2022; 6 (2): 214-221
  • Novel-view X-ray projection synthesis through geometry-integrated deep learning. Medical image analysis Shen, L., Yu, L., Zhao, W., Pauly, J., Xing, L. 2022; 77: 102372

    Abstract

    X-ray imaging is a widely used approach to view the internal structure of a subject for clinical diagnosis, image-guided interventions and decision-making. The X-ray projections acquired at different view angles provide complementary information of patient's anatomy and are required for stereoscopic or volumetric imaging of the subject. In reality, obtaining multiple-view projections inevitably increases radiation dose and complicates clinical workflow. Here we investigate a strategy of obtaining the X-ray projection image at a novel view angle from a given projection image at a specific view angle to alleviate the need for actual projection measurement. Specifically, a Deep Learning-based Geometry-Integrated Projection Synthesis (DL-GIPS) framework is proposed for the generation of novel-view X-ray projections. The proposed deep learning model extracts geometry and texture features from a source-view projection, and then conducts geometry transformationon the geometry features to accommodate the change of view angle.At the final stage, the X-ray projection in the target view is synthesized from the transformed geometry and the shared texture features via an image generator. The feasibility and potential impact of the proposed DL-GIPS model are demonstrated using lung imaging cases.The proposed strategy can be generalized to a general case of multiple projections synthesis from multiple input views and potentially provides a new paradigm for various stereoscopic and volumetric imaging with substantially reduced efforts in data acquisition.

    View details for DOI 10.1016/j.media.2022.102372

    View details for PubMedID 35131701

  • Attention-guided deep learning for gestational age prediction using fetal brain MRI. Scientific reports Shen, L., Zheng, J., Lee, E. H., Shpanskaya, K., McKenna, E. S., Atluri, M. G., Plasto, D., Mitchell, C., Lai, L. M., Guimaraes, C. V., Dahmoush, H., Chueh, J., Halabi, S. S., Pauly, J. M., Xing, L., Lu, Q., Oztekin, O., Kline-Fath, B. M., Yeom, K. W. 1800; 12 (1): 1408

    Abstract

    Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. However, gauging age-appropriate neural development remains a difficult task due to the constantly changing appearance of the fetal brain, variable image quality, and frequent motion artifacts. Here we present an end-to-end, attention-guided deep learning model that predicts gestational age with R2 score of 0.945, mean absolute error of 6.7days, and concordance correlation coefficient of 0.970. The convolutional neural network was trained on a heterogeneous dataset of 741 developmentally normal fetal brain images ranging from 19 to 39weeks in gestational age. We also demonstrate model performance and generalizability using independent datasets from four academic institutions across the U.S. and Turkey with R2 scores of 0.81-0.90 after minimal fine-tuning. The proposed regression algorithm provides an automated machine-enabled tool with the potential to better characterize in utero neurodevelopment and guide real-time gestational age estimation after the first trimester.

    View details for DOI 10.1038/s41598-022-05468-5

    View details for PubMedID 35082346

  • Mechanoporation enables rapid and efficient radiolabeling of stem cells for PET imaging. Scientific reports Jung, K. O., Theruvath, A. J., Nejadnik, H., Liu, A., Xing, L., Sulchek, T., Daldrup-Link, H. E., Pratx, G. 2022; 12 (1): 2955

    Abstract

    Regenerative medicine uses the patient own stem cells to regenerate damaged tissues. Molecular imaging techniques are commonly used to image the transplanted cells, either right after surgery or at a later time. However, few techniques are fast or straightforward enough to label cells intraoperatively. Adipose tissue-derived stem cells (ADSCs) were harvested from knee joints of minipigs. The cells were labeled with PET contrast agent by flowing mechanoporation using a microfluidic device. While flowing through a series of microchannels, cells are compressed repeatedly by micro-ridges, which open transient pores in their membranes and induce convective transport, intended to facilitate the transport of 68Ga-labeled and lipid-coated mesoporous nanoparticles (MSNs) into the cells. This process enables cells to be labeled in a matter of seconds. Cells labeled with this approach were then implanted into cartilage defects, and the implant was imaged using positron emission tomography (PET) post-surgery. The microfluidic device can efficiently label millions of cells with 68Ga-labeled MSNs in as little as 15 min. The method achieved labeling efficiency greater than 5 Bq/cell on average, comparable to 30 min-long passive co-incubation with 68Ga-MSNs, but with improved biocompatibility due to the reduced exposure to ionizing radiation. Labeling time could also be accelerated by increasing throughput through more parallel channels. Finally, as a proof of concept, ADSCs were labeled with 68Ga-MSNs and quantitatively assessed using clinical PET/MR in a mock transplant operation in pig knee joints. MSN-assisted mechanoporation is a rapid, effective and straightforward approach to label cells with 68Ga. Given its high efficiency, this labeling method can be used to track small cells populations without significant effects on viability. The system is applicable to a variety of cell tracking studies for cancer therapy, regenerative therapy, and immunotherapy.

    View details for DOI 10.1038/s41598-022-06938-6

    View details for PubMedID 35194089

  • Biology-guided deep learning predicts prognosis and cancer immunotherapy response Society for Immunotherapy of Cancer’s (SITC) 37th Annual Meeting Jiang, Y., Zhang, Z., Wang, W., Huang, W., Chen, C., Xi, S., Ahmad, M., Ren, Y., Sang, S., Xie, J., Xiong, W., Li, T., Han, Z., Yuan, Q., Xu, Y., Xing, L., Poultsides, G., Li, G., Li, R. 2022
  • IMRT and SBRT Treatment Planning Study for the First Clinical Biology-Guided Radiotherapy System. Technology in cancer research & treatment Pham, D., Simiele, E., Breitkreutz, D., Capaldi, D., Han, B., Surucu, M., Oderinde, S., Vitzthum, L., Gensheimer, M., Bagshaw, H., Chin, A., Xing, L., Chang, D. T., Kovalchuk, N. 2022; 21: 15330338221100231

    Abstract

    Purpose: The first clinical biology-guided radiation therapy (BgRT) system-RefleXionTM X1-was installed and commissioned for clinical use at our institution. This study aimed at evaluating the treatment plan quality and delivery efficiency for IMRT/SBRT cases without PET guidance. Methods: A total of 42 patient plans across 6 cancer sites (conventionally fractionated lung, head, and neck, anus, prostate, brain, and lung SBRT) planned with the EclipseTM treatment planning system (TPS) and treated with either a TrueBeam or Trilogy were selected for this retrospective study. For each Eclipse VMAT plan, 2 corresponding plans were generated on the X1 TPS with 10mm jaws (X1-10mm) and 20mm jaws (X1-20mm) using our institutional planning constraints. All clinically relevant metrics in this study, including PTV D95%, PTV D2%, Conformity Index (CI), R50, organs-at-risk (OAR) constraints, and beam-on time were analyzed and compared between 126 VMAT and RefleXion plans using paired t-tests. Results: All but 3 planning metrics were either equivalent or superior for the X1-10mm plans as compared to the Eclipse VMAT plans across all planning sites investigated. The Eclipse VMAT and X1-10mm plans generally achieved superior plan quality and sharper dose fall-off superior/inferior to targets as compared to the X1-20mm plans, however, the X1-20mm plans were still considered acceptable for treatment. On average, the required beam-on time increased by a factor of 1.6 across all sites for X1-10mm compared to X1-20mm plans. Conclusions: Clinically acceptable IMRT/SBRT treatment plans were generated with the X1 TPS for both the 10mm and 20mm jaw settings.

    View details for DOI 10.1177/15330338221100231

    View details for PubMedID 35579876

  • CateNorm: Categorical Normalization for Robust Medical Image Segmentation Xiao, J., Yu, L., Zhou, Z., Bai, Y., Xing, L., Yuille, A., Zhou, Y., Kamnitsas, K., Koch, L., Islam, M., Xu, Z., Cardoso, J., Dou, Q., Rieke, N., Tsaftaris, S. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 129-146
  • Human-level comparable control volume mapping with a deep unsupervised-learning model for image-guided radiation therapy. Computers in biology and medicine Liang, X., Bassenne, M., Hristov, D. H., Islam, M. T., Zhao, W., Jia, M., Zhang, Z., Gensheimer, M., Beadle, B., Le, Q., Xing, L. 1800; 141: 105139

    Abstract

    PURPOSE: To develop a deep unsupervised learning method with control volume (CV) mapping from patient positioning daily CT (dCT) to planning computed tomography (pCT) for precise patient positioning.METHODS: We propose an unsupervised learning framework, which maps CVs from dCT to pCT to automatically generate the couch shifts, including translation and rotation dimensions. The network inputs are dCT, pCT and CV positions in the pCT. The output is the transformation parameter of the dCT used to setup the head and neck cancer (HNC) patients. The network is trained to maximize image similarity between the CV in the pCT and the CV in the dCT. A total of 554 CT scans from 158 HNC patients were used for the evaluation of the proposed model. At different points in time, each patient had many CT scans. Couch shifts are calculated for the testing by averaging the translation and rotation from the CVs. The ground-truth of the shifts come from bone landmarks determined by an experienced radiation oncologist.RESULTS: The system positioning errors of translation and rotation are less than 0.47mm and 0.17°, respectively. The random positioning errors of translation and rotation are less than 1.13mm and 0.29°, respectively. The proposed method enhanced the proportion of cases registered within a preset tolerance (2.0mm/1.0°) from 66.67% to 90.91% as compared to standard registrations.CONCLUSIONS: We proposed a deep unsupervised learning architecture for patient positioning with inclusion of CVs mapping, which weights the CVs regions differently to mitigate any potential adverse influence of image artifacts on the registration. Our experimental results show that the proposed method achieved efficient and effective HNC patient positioning.

    View details for DOI 10.1016/j.compbiomed.2021.105139

    View details for PubMedID 34942395

  • Deep Neural Network With Consistency Regularization of Multi-Output Channels for Improved Tumor Detection and Delineation IEEE TRANSACTIONS ON MEDICAL IMAGING Seo, H., Yu, L., Ren, H., Li, X., Shen, L., Xing, L. 2021; 40 (12): 3369-3378

    Abstract

    Deep learning is becoming an indispensable tool for imaging applications, such as image segmentation, classification, and detection. In this work, we reformulate a standard deep learning problem into a new neural network architecture with multi-output channels, which reflects different facets of the objective, and apply the deep neural network to improve the performance of image segmentation. By adding one or more interrelated auxiliary-output channels, we impose an effective consistency regularization for the main task of pixelated classification (i.e., image segmentation). Specifically, multi-output-channel consistency regularization is realized by residual learning via additive paths that connect main-output channel and auxiliary-output channels in the network. The method is evaluated on the detection and delineation of lung and liver tumors with public data. The results clearly show that multi-output-channel consistency implemented by residual learning improves the standard deep neural network. The proposed framework is quite broad and should find widespread applications in various deep learning problems.

    View details for DOI 10.1109/TMI.2021.3084748

    View details for Web of Science ID 000724511900011

    View details for PubMedID 34048339

  • Geometry and statistics-preserving manifold emb e dding for nonlinear dimensionality reduction PATTERN RECOGNITION LETTERS Islam, M., Xing, L. 2021; 151: 155-162
  • Automated Contour Propagation of the Prostate From pCT to CBCT Images via Deep Unsupervised Learning Liang, X., Bibault, J. E., Leroy, T., Escande, A., Zhao, W., Chen, Y., Buyyounouski, M. K., Hancock, S. L., Bagshaw, H. P., Xing, L. ELSEVIER SCIENCE INC. 2021: E95
  • Small field measurement and monte carlo model validation of a novel image-guided radiotherapy system. Medical physics Shi, M., Chuang, C. F., Kovalchuk, N., Bush, K. K., Zaks, D., Xing, L., Surucu, M., Han, B. 2021

    Abstract

    PURPOSE: The RefleXionTM X1 is a novel radiotherapy system that is designed for image-guided radiotherapy and, eventually, biology-guided radiotherapy (BgRT). BgRT is a treatment paradigm that tracks tumor motion using real-time positron emission signals. This study reports the small field measurement results and the validation of a Monte Carlo (MC) model of the first clinical RefleXion unit.METHODS: The RefleXion linear accelerator (linac) produces a 6 MV flattening filter free (FFF) photon beam and consists of a binary multi-leaf collimator (MLC) system with 64 leaves and two pairs of y-jaws. The maximum clinical field size achievable is 400 * 20 mm2 . The y-jaws provide either a 10 mm or 20 mm opening at source-to-axis distance (SAD) of 850 mm. The width of each MLC leaf at SAD is 6.25 mm. Percentage depth doses (PDDs) and relative beam profiles were acquired using an Edge diode detector in a water tank for field sizes from 12.5 * 10 mm2 to 100 * 20 mm2 . Beam profiles were also measured using films. Output factors of fields ranging from 6.25 * 10 mm2 to 100 * 20 mm2 were measured using W2 scintillator detector, Edge detector, and films. Output correction factors k of the Edge detector for RefleXion were calculated. A MC model of the linac including pre-MLC beam sources and detailed structures of MLC and lower y-jaws was validated against the measurements. Simulation codes BEAMnrc and GATE were utilized.RESULTS: The diode measured PDD at 10 cm depth (PDD10) increases from 53.6% to 56.9% as the field opens from 12.5 * 10 mm2 to 100 * 20 mm2 . The W2-measured output factor increases from 0.706 to 1 as the field opens from 6.25 * 10 mm2 to 100 * 20 mm2 (reference field size). The output factors acquired by diode and film differ from the W2 results by 1.65% (std = 1.49%) and 2.09% (std = 1.41%) on average, respectively. The profile penumbra and full width half maximum (FWHM) measured by diode agree well with the film results with a deviation of 0.60 mm and 0.73% on average, respectively. The averaged beam profile consistency calculated between the diode and film measured profiles among different depths is within 1.72%. By taking the W2 measurements as the ground truth, the output correction factors k for Edge detector ranging from 0.958 to 1 were reported. For the MC model validation, the simulated PDD10 agreed within 0.6% to the diode measurement. The MC simulated output factor differed from the W2 results by 2.3% on average (std = 3.7%) while the MC simulated beam penumbra differed from the diode results by 0.67 mm on average (std = 0.42 mm). The MC FWHM agreed with the diode results to within 1.40% on average. The averaged beam profile consistency calculated between the diode and MC profiles among different depths is less than 1.29%.CONCLUSIONS: This study represents the first small field dosimetry of a clinical RefleXion system. A complete and accurate MC model of the RefleXion linac has been validated. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.15273

    View details for PubMedID 34628666

  • Pareto Optimal Projection Search (POPS): Automated Radiation Therapy Treatment Planning by Direct Search of the Pareto Surface IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Huang, C., Yang, Y., Panjwani, N., Boyd, S., Xing, L. 2021; 68 (10): 2907-2917

    Abstract

    Radiation therapy treatment planning is a time-consuming, iterative process with potentially high inter-planner variability. Fully automated treatment planning processes could reduce a planner's active treatment planning time and remove inter-planner variability, with the potential to tremendously improve patient turnover and quality of care. In developing fully automated algorithms for treatment planning, we have two main objectives: to produce plans that are 1) Pareto optimal and 2) clinically acceptable. Here, we propose the Pareto optimal projection search (POPS) algorithm, which provides a general framework for directly searching the Pareto front.Our POPS algorithm is a novel automated planning method that combines two main search processes: 1) gradient-free search in the decision variable space and 2) projection of decision variables to the Pareto front using the bisection method. We demonstrate the performance of POPS by comparing with clinical treatment plans. As one possible quantitative measure of treatment plan quality, we construct a clinical acceptability scoring function (SF) modified from the previously developed general evaluation metric (GEM).On a dataset of 21 prostate cases collected as part of clinical workflow, our proposed POPS algorithm produces Pareto optimal plans that are clinically acceptable in regards to dose conformity, dose homogeneity, and sparing of organs-at-risk.Our proposed POPS algorithm provides a general framework for fully automated treatment planning that achieves clinically acceptable dosimetric quality without requiring active planning from human planners.Our fully automated POPS algorithm addresses many key limitations of other automated planning approaches, and we anticipate that it will substantially improve treatment planning workflow.

    View details for DOI 10.1109/TBME.2021.3055822

    View details for Web of Science ID 000697820800006

    View details for PubMedID 33523802

  • Deep learning-augmented radioluminescence imaging for radiotherapy dose verification. Medical physics Jia, M., Yang, Y., Wu, Y., Li, X., Xing, L., Wang, L. 2021

    Abstract

    PURPOSE: We developed a novel dose verification method using a camera-based radioluminescence imaging system (CRIS) combined with a deep learning-based signal processing technique.METHODS: The CRIS consists of a cylindrical chamber coated with scintillator material on the inner surface of the cylinder, coupled with a hemispherical mirror and a digital camera at the two ends. After training, the deep learning model is used for image-to-dose conversion to provide absolute dose prediction at multiple depths of a specific water phantom from a single CRIS image under the assumption of a good consistency between the TPS setting and actual beam energy. The model was trained using a set of captured radioluminescence images and the corresponding dose maps from the clinical treatment planning system (TPS) for the sake of acceptable data collection. To overcome the latent error and inconsistency that exists between the TPS calculation and the corresponding measurement, the model was trained in an unsupervised manner. Validation experiments were performed on five square fields (ranging from 2 * 2 cm2 to 10 * 10 cm2 ), and three clinical IMRT cases. The results were compared to the TPS calculations in terms of gamma index at 1.5 cm, 5 cm and 10 cm depths.RESULTS: The mean 2% / 2mm gamma pass rates were 100% for square fields and 97.2% (range from 95.5% to 99.5%) for the IMRT fields. Further validations were performed by comparing the CRIS results with measurements on various regular fields. The results show a mean gamma pass rate of 91% (1% / 1mm) for cross-profiles and a mean percentage deviation of 1.15% for percentage depth doses (PDDs).CONCLUSIONS: The system is capable of converting the irradiated radioluminescence image to corresponding water-based dose maps at multiple depths with a spatial resolution comparable to the TPS calculations. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.15229

    View details for PubMedID 34523131

  • Fully automated noncoplanar radiation therapy treatment planning. Medical physics Huang, C., Yang, Y., Xing, L. 2021

    Abstract

    PURPOSE: To perform fully automated noncoplanar treatment planning, we propose a method called NC-POPS to produce noncoplanar (NC) plans using the Pareto Optimal Projection Search (POPS) algorithm.METHODS: Noncoplanar radiation therapy treatment planning has the potential to improve dosimetric quality as compared to traditional coplanar techniques. Likewise, automated treatment planning algorithms can reduce a planner's active treatment planning time and remove inter-planner variability. Our NC-POPS algorithm extends the original POPS algorithm to the noncoplanar setting with potential applications to both IMRT and VMAT. The proposed algorithm consists of two main parts: 1) noncoplanar beam angle optimization (BAO) and 2) fully automated inverse planning using the POPS algorithm.RESULTS: We evaluate the performance of NC-POPS by comparing between various noncoplanar and coplanar configurations. To evaluate plan quality, we compute the homogeneity index (HI), conformity index (CI), and dose-volume histogram (DVH) statistics for various organs-at-risk (OARs). As compared to the evaluated coplanar baseline methods, the proposed NC-POPS method achieves significantly better OAR sparing, comparable or better dose conformity, and similar dose homogeneity.CONCLUSIONS: Our proposed NC-POPS algorithm provides a modular approach for fully automated treatment planning of noncoplanar IMRT cases with the potential to substantially improve treatment planning workflow and plan quality. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.15223

    View details for PubMedID 34519064

  • Deep learning-enabled EPID-based 3D dosimetry for dose verification of step-and-shoot radiotherapy. Medical physics Jia, M., Wu, Y., Yang, Y., Wang, L., Chuang, C., Han, B., Xing, L. 2021

    Abstract

    PURPOSE: The study aims at a novel dosimetry methodology to reconstruct a 3D dose distribution as imparted to a virtual cylindrical phantom using an electronic portal imaging device (EPID).METHODS: A deep learning-based signal processing strategy, referred to as 3DosiNet, is utilized to learn a mapping from an EPID image to planar dose distributions at given depths. The network was trained with the volumetric dose exported from the clinical treatment planning system (TPS). Given the latent inconsistency between measurements and corresponding TPS calculations, unsupervised learning is formulated in 3DosiNet to capture abstractive image features that are less sensitive to the potential variations.RESULTS: Validation experiments were performed using five regular fields and three clinical IMRT cases. The measured dose profiles and percentage depth dose (PDD) curves were compared with those measured using standard tools in terms of the 1D gamma index. The mean gamma pass rates (2%/2mm) over the regular fields are 100% and 97.3% for the dose profile and PDD measurements, respectively. The measured volumetric dose was compared to corresponding TPS calculation in terms of the 3D gamma index. The mean 2% / 2mm gamma pass rates are 97.9% for square fields and 94.9% for the IMRT fields.CONCLUSIONS: The system promises to be a practical 3D dosimetric tool for pre-treatment patient-specific quality assurance and further developed for in-treatment patient dose monitoring. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.15218

    View details for PubMedID 34519365

  • Rotation-Oriented Collaborative Self-Supervised Learning for Retinal Disease Diagnosis IEEE TRANSACTIONS ON MEDICAL IMAGING Li, X., Hu, X., Qi, X., Yu, L., Zhao, W., Heng, P., Xing, L. 2021; 40 (9): 2284-2294

    Abstract

    The automatic diagnosis of various conventional ophthalmic diseases from fundus images is important in clinical practice. However, developing such automatic solutions is challenging due to the requirement of a large amount of training data and the expensive annotations for medical images. This paper presents a novel self-supervised learning framework for retinal disease diagnosis to reduce the annotation efforts by learning the visual features from the unlabeled images. To achieve this, we present a rotation-oriented collaborative method that explores rotation-related and rotation-invariant features, which capture discriminative structures from fundus images and also explore the invariant property used for retinal disease classification. We evaluate the proposed method on two public benchmark datasets for retinal disease classification. The experimental results demonstrate that our method outperforms other self-supervised feature learning methods (around 4.2% area under the curve (AUC)). With a large amount of unlabeled data available, our method can surpass the supervised baseline for pathologic myopia (PM) and is very close to the supervised baseline for age-related macular degeneration (AMD), showing the potential benefit of our method in clinical practice.

    View details for DOI 10.1109/TMI.2021.3075244

    View details for Web of Science ID 000692208500009

    View details for PubMedID 33891550

  • Metal artifact reduction in 2D CT images with self-supervised cross-domain learning. Physics in medicine and biology Yu, L., Zhang, Z., Li, X., Ren, H., Zhao, W., Xing, L. 2021

    Abstract

    The presence of metallic implants often introduces severe metal artifacts in the X-ray CT images, which could adversely influence clinical diagnosis or dose calculation in radiation therapy. In this work, we present a novel deep-learning-based approach for metal artifact reduction (MAR). In order to alleviate the need for anatomically identical CT image pairs (\ie, metal artifact-corrupted CT image and metal artifact-free CT image) for network learning, we propose a self-supervised cross-domain learning framework. Specifically, we train a neural network to restore the metal trace region values in the given metal-free sinogram, where the metal trace is identified by the forward projection of metal masks. We then design a novel FBP reconstruction loss to encourage the network to generate more perfect completion results and a residual-learning-based image refinement module to reduce the secondary artifacts in the reconstructed CT images. To preserve the fine structure details and fidelity of the final MAR image, instead of directly adopting CNN-refined images as output, we incorporate the metal trace replacement into our framework and replace the metal-affected projections of the original sinogram with the prior sinogram generated by the forward projection of the CNN output. We then use the filtered backward projection (FBP) algorithms for final MAR image reconstruction. We conduct an extensive evaluation on simulated and real artifact data to show the effectiveness of our design. Our method produces superior MAR results and outperforms other compelling methods. We also demonstrate the potential of our framework for other organ sites.

    View details for DOI 10.1088/1361-6560/ac195c

    View details for PubMedID 34330119

  • Noise2Context: Context-assisted Learning 3D Thin-layer for Low Dose CT. Medical physics Zhang, Z., Liang, X., Zhao, W., Xing, L. 2021

    Abstract

    PURPOSE: Computed tomography (CT) has played a vital role in medical diagnosis, assessment, and therapy planning, etc. In clinical practice, concerns about the increase of X-ray radiation exposure attract more and more attention. To lower the X-ray radiation, low-dose CT (LDCT) has been widely adopted in certain scenarios, while it will induce the degradation of CT image quality. In this paper, we proposed a deep learning-based method that can train denoising neural networks without any clean data.METHODS: In this work, for 3D thin-slice LDCT scanning, we first drive an unsupervised loss function which was equivalent to a supervised loss function with paired noisy and clean samples when the noise in the different slices from a single scan was uncorrelated and zero-mean. Then, we trained the denoising neural network to map one noise LDCT image to its two adjacent LDCT images in a single 3D thin-layer LDCT scanning, simultaneously. In essence, with some latent assumptions, we proposed an unsupervised loss function to train the denoising neural network in an unsupervised manner, which integrated the similarity between adjacent CT slices in 3D thin-layer LDCT.RESULTS: Further experiments on Mayo LDCT dataset and a realistic pig head were carried out. In the experiments using Mayo LDCT dataset, our unsupervised method can obtain performance comparable to that of the supervised baseline. With the realistic pig head, our method can achieve optimal performance at different noise levels as compared to all the other methods that demonstrated the superiority and robustness of the proposed Noise2Context.CONCLUSIONS: In this work, we present a generalizable LDCT image denoising method without any clean data. As a result, our method not only gets rid of the complex artificial image priors but also amounts of paired high-quality training datasets.

    View details for DOI 10.1002/mp.15119

    View details for PubMedID 34287948

  • Detection of Carotid Artery Stenosis with Intraplaque Hemorrhage and Neovascularization Using a Scanning Interferometer. Nano letters Zaman, R. T., Kosuge, H., Gambhir, S. S., Xing, L. 2021

    Abstract

    Carotid artery stenosis (CAS) is a major cause of stroke or transient ischemic attack (TIA, mini-stroke) in the United States. Carotid endarterectomy (CEA), a surgical procedure, is used to treat CAS. According to the American Heart Association, 1 out of 5 patients underwent CEA inappropriately, which was most commonly due to apparent overestimation of stenosis severity, and half had uncertain indicators. The current imaging modalities are limited in providing critical information on carotid arterial plaque content, extent, and biology. To circumvent these limitations, we developed a sensing interferometer (SI) imaging system to assess vulnerable carotid plaques noninvasively to detect stenosis, neovascularization, and intraplaque hemorrhage (IPH). We have custom-built a SI prototype and its peripheral systems with back-mode-projection capability. We detected stenosis, neo-vessels, and IPH through SI imaging system in in vivo mice carotid atherosclerotic plaques and further verified the same plaques ex vivo through a histology scope, CRi Maestro, and histological analysis.

    View details for DOI 10.1021/acs.nanolett.1c01441

    View details for PubMedID 34156253

  • Independent verification of brachytherapy treatment plan by using deep learning inference modeling. Physics in medicine and biology Fan, J., Xing, L., Yang, Y. 2021; 66 (12)

    Abstract

    This study aims to develop a deep learning-based strategy for treatment plan check and verification of high-dose rate (HDR) brachytherapy. A deep neural network was trained to verify the dwell positions and times for a given input brachytherapy isodose distribution. In our modeling, each dwell position is represented by a Gaussian heatmap located in the vicinity of the dwell positions. A deep inception network based architecture was established to learn the mapping between CT, dose distribution and the heatmap volume. The dwell position coordinates were obtained from the predicted heatmap volume by finding the location of the Gaussian peak using non-maximum suppression. An encoder network was employed to predict dwell time by using the same input. 110 HDR brachytherapy cervical patients were used to train the proposed network. Additional 10 patients were employed to evaluate the accuracy of the proposed method through comparing the dwell position coordinates and dwell times with the results from a treatment planning system. The proposed deep learning-based dwell positions and times verification method achieved excellent predictive performance. For the tested patients, the deviation of the deep learning predicted dwell position coordinates was around one pixel from the planned positions (on average, a pixel is 0.5 mm), and the relative deviations of the predicted dwell times were within 2%. A deep learning-based plan check and verification method was established for brachytherapy. Our study showed that the model is capable of predicting the dwell positions and times reliably and promises to provide an efficient and accurate tool for independent verification of HDR brachytherapy treatment plan.

    View details for DOI 10.1088/1361-6560/ac067f

    View details for PubMedID 34132651

  • Frontiers in AI and Its Applications in Medical Physics Xing, L., Zeng, R., Purdie, T., Wen, N., Xing, L. WILEY. 2021
  • MR to Ultrasound Image Registration with Segmentation-Based Learning for HDR Prostate Brachytherapy Chen, Y., Xing, L., Yu, L., Liu, W., Fahimian, B., Niedermayr, T., Bagshaw, H., Buyyounouski, M., Han, B. WILEY. 2021
  • Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer Mortality CANCERS Bibault, J., Hancock, S., Buyyounouski, M. K., Bagshaw, H., Leppert, J. T., Liao, J. C., Xing, L. 2021; 13 (12)

    Abstract

    Prostate cancer treatment strategies are guided by risk-stratification. This stratification can be difficult in some patients with known comorbidities. New models are needed to guide strategies and determine which patients are at risk of prostate cancer mortality. This article presents a gradient-boosting model to predict the risk of prostate cancer mortality within 10 years after a cancer diagnosis, and to provide an interpretable prediction. This work uses prospective data from the PLCO Cancer Screening and selected patients who were diagnosed with prostate cancer. During follow-up, 8776 patients were diagnosed with prostate cancer. The dataset was randomly split into a training (n = 7021) and testing (n = 1755) dataset. Accuracy was 0.98 (±0.01), and the area under the receiver operating characteristic was 0.80 (±0.04). This model can be used to support informed decision-making in prostate cancer treatment. AI interpretability provides a novel understanding of the predictions to the users.

    View details for DOI 10.3390/cancers13123064

    View details for Web of Science ID 000666025900001

    View details for PubMedID 34205398

  • Multi-Domain Image Completion for Random Missing Input Data IEEE TRANSACTIONS ON MEDICAL IMAGING Shen, L., Zhu, W., Wang, X., Xing, L., Pauly, J. M., Turkbey, B., Harmon, S., Sanford, T., Mehralivand, S., Choyke, P. L., Wood, B. J., Xu, D. 2021; 40 (4): 1113–22

    Abstract

    Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI). However, due to possible data corruption and different imaging protocols, the availability of images for each domain could vary amongst multiple data sources in practice, which makes it challenging to build a universal model with a varied set of input data. To tackle this problem, we propose a general approach to complete the random missing domain(s) data in real applications. Specifically, we develop a novel multi-domain image completion method that utilizes a generative adversarial network (GAN) with a representational disentanglement scheme to extract shared content encoding and separate style encoding across multiple domains. We further illustrate that the learned representation in multi-domain image completion could be leveraged for high-level tasks, e.g., segmentation, by introducing a unified framework consisting of image completion and segmentation with a shared content encoder. The experiments demonstrate consistent performance improvement on three datasets for brain tumor segmentation, prostate segmentation, and facial expression image completion respectively.

    View details for DOI 10.1109/TMI.2020.3046444

    View details for Web of Science ID 000637532800002

    View details for PubMedID 33351753

  • Prior-image-based CT reconstruction using attenuation-mismatched priors. Physics in medicine and biology Zhang, H., Capaldi, D., Zeng, D., Ma, J., Xing, L. 2021; 66 (6): 064007

    Abstract

    Prior-image-based reconstruction (PIBR) methods are powerful tools for reducing radiation doses and improving the image quality of low-dose computed tomography (CT). Apart from anatomical changes, prior and current images can also have different attenuations because they originated from different scanners or from the same scanner but with different x-ray beam qualities (e.g., kVp settings, beam filters) during data acquisition. In such scenarios, with attenuation-mismatched priors, PIBR is challenging. In this work, we investigate a specific PIBR method, called statistical image reconstruction, using normal-dose image-induced nonlocal means regularization (SIR-ndiNLM), to address PIBR with such attenuation-mismatched priors and achieve quantitative low-dose CT imaging. We propose two corrective schemes for the original SIR-ndiNLM method, (1) a global histogram-matching approach and (2) a local attenuation correction approach, to account for the attenuation differences between the prior and current images in PIBR. We validate the efficacy of the proposed schemes using images acquired from dual-energy CT scanners to simulate attenuation mismatches. Meanwhile, we utilize different CT slices to simulate anatomical mismatches or changes between the prior and the current low-dose image. We observe that the original SIR-ndiNLM introduces artifacts to the reconstruction when an attenuation-mismatched prior is used. Furthermore, we find that a larger attenuation mismatch between the prior and current images results in more severe artifacts in the SIR-ndiNLM reconstruction. Our two proposed corrective schemes enable SIR-ndiNLM to effectively handle the attenuation mismatch and anatomical changes between the two images and successfully eliminate the artifacts. We demonstrate that the proposed techniques permit SIR-ndiNLM to leverage the attenuation-mismatched prior and achieve quantitative low-dose CT reconstruction from both low-flux and sparse-view data acquisitions. This work permits robust and reliable PIBR for CT data acquired using different beam settings.

    View details for DOI 10.1088/1361-6560/abe760

    View details for PubMedID 33729997

  • Prior-image-based CT reconstruction using attenuation mismatched prior. Physics in medicine and biology Zhang, H., Capaldi, D. P., Zeng, D., Ma, J., Xing, L. 2021

    Abstract

    Prior-image-based reconstruction (PIBR) methods are powerful in reducing radiation dose and improving image quality for low-dose CT. Besides anatomical changes, the prior and current images can also have different attenuation due to different scanners or the same scanner but with different x-ray beam quality (e.g., kVp setting, beam filtration) during data acquisitions. PIBR is challenged in such scenarios with attenuation mismatched prior. In this work, we investigate a specific PIBR method, called statistical image reconstruction using normal dose image induced nonlocal means regularization (SIR-ndiNLM), to address PIBR with such attenuation mismatched prior and achieve quantitative low-dose CT imaging. We proposed two corrective schemes for the original SIR-ndiNLM method, 1) a global histogram matching approach and 2) a local attenuation correction approach, to account for the attenuation differences between the prior and current images in PIBR. We validated the efficacy of the proposed schemes using images acquired from dual-energy CT scanners to emulate attenuation mismatches. Meanwhile, we utilized different CT slices to emulate anatomical mismatches/changes between the prior and the current low-dose images. We observed that the original SIR-ndiNLM introduces artifacts to the reconstruction when using attenuation mismatched prior. Furthermore, we found that larger attenuation mismatch between the prior and current images results in more severe artifacts in the SIR-ndiNLM reconstruction. Our proposed two corrective schemes enabled SIR-ndiNLM to effectively handle attenuation mismatch and anatomical changes between two images and successfully eliminate the artifacts. We demonstrated that the proposed techniques permit SIR-ndiNLM to leverage the attenuation mismatched prior and achieve quantitative low-dose CT reconstruction from both low-flux and sparse-view data acquisitions. This work permits robust and reliable PIBR for CT data acquired using different beam settings.

    View details for DOI 10.1088/1361-6560/abe760

    View details for PubMedID 33596553

  • Modularized Data-Driven Reconstruction Framework for Non-ideal Focal Spot Effect Elimination in Computed Tomography. Medical physics Zhang, Z., Yu, L., Zhao, W., Xing, L. 2021

    Abstract

    PURPOSE: High-performance computed tomography (CT) plays a vital role in clinical decision making. However, the performance of CT imaging is adversely affected by the non-ideal focal spot size of the X-ray source or degraded by an enlarged focal spot size due to aging. In this work, we aim to develop a deep learning-based strategy to mitigate the problem so that high spatial resolution CT images can be obtained even in the case of a non-ideal X-ray source.METHODS: To reconstruct high-quality CT images from blurred sinograms via joint image and sinogram learning, a cross-domain hybrid model is formulated via deep learning into a modularized data-driven reconstruction (MDR) framework. The proposed MDR framework comprises several blocks, and all the blocks share the same network architecture and network parameters. In essence, each block utilizes two sub-models to generate an estimated blur kernel and a high-quality CT image simultaneously. In this way, our framework generates not only a final high-quality CT image but also a series of intermediate images with gradually improved anatomical details, enhancing the visual perception for clinicians through the dynamic process. We used simulated training datasets to train our model in an end-to-end manner and tested our model on both simulated and realistic experimental datasets.RESULTS: On the simulated testing datasets, our approach increases the information fidelity criterion (IFC) by up to 34.2%, the universal quality index (UQI) by up to 20.3%, the signal-to-noise (SNR) by up to 6.7%, and reduces the root mean square error (RMSE) by up to 10.5% as compared with FBP. Compared with the iterative deconvolution method (NSM), MDR increases IFC by up to 24.7%, UQI by up to 16.7%, SNR by up to 6.0%, and reduces RMSE by up to 9.4%. In the modulation transfer function (MTF) experiment, our method improves the MTF50% by 34.5% and MTF10% by 18.7% as compared with FBP, Similarly remarkably, our method improves MTF50% by 14.3% and MTF10% by 0.9% as compared with NSM. Also, our method shows better imaging results in the edge of bony structures and other tiny structures in the experiments using phantom consisting of ham and a bottle of peanuts.CONCLUSIONS: A modularized data-driven CT reconstruction framework is established to mitigate the blurring effect caused by a non-ideal X-ray source with relatively large focal spot. The proposed method enables us to obtain high-resolution images with less ideal X-ray source.

    View details for DOI 10.1002/mp.14785

    View details for PubMedID 33595900

  • Estimating dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network. Medical image analysis Lyu, T., Zhao, W., Zhu, Y., Wu, Z., Zhang, Y., Chen, Y., Luo, L., Li, S., Xing, L. 2021; 70: 102001

    Abstract

    Dual-energy computed tomography (DECT) is of great significance for clinical practice due to its huge potential to provide material-specific information. However, DECT scanners are usually more expensive than standard single-energy CT (SECT) scanners and thus are less accessible to undeveloped regions. In this paper, we show that the energy-domain correlation and anatomical consistency between standard DECT images can be harnessed by a deep learning model to provide high-performance DECT imaging from fully-sampled low-energy data together with single-view high-energy data. We demonstrate the feasibility of the approach with two independent cohorts (the first cohort including contrast-enhanced DECT scans of 5753 image slices from 22 patients and the second cohort including spectral CT scans without contrast injection of 2463 image slices from other 22 patients) and show its superior performance on DECT applications. The deep-learning-based approach could be useful to further significantly reduce the radiation dose of current premium DECT scanners and has the potential to simplify the hardware of DECT imaging systems and to enable DECT imaging using standard SECT scanners.

    View details for DOI 10.1016/j.media.2021.102001

    View details for PubMedID 33640721

  • Automated Contour Propagation of the Prostate From pCT to CBCT Images Via Deep Unsupervised Learning. Medical physics Liang, X., Bibault, J., Leroy, T., Escande, A., Zhao, W., Chen, Y., Buyyounouski, M. K., Hancock, S. L., Bagshaw, H., Xing, L. 2021

    Abstract

    PURPOSE: To develop and evaluate a deep unsupervised learning (DUL) framework based on a regional deformable model for automated prostate contour propagation from planning computed tomography (pCT) to cone-beam CT (CBCT).METHODS: We introduce a DUL model to map the prostate contour from pCT to on-treatment CBCT. The DUL framework used a regional deformable model via narrow band mapping to augment the conventional strategy. 251 anonymized CBCT images from prostate cancer patients were retrospectively selected and divided into three sets: 180 were used for training, 12 for validation, and 59 for testing. The testing dataset was divided into two Groups. Group one contained 50 CBCT volumes, with one physician-generated prostate contour on CBCT image. Group two contained 9 CBCT images, each including prostate contours delineated by four independent physicians and a consensus contour generated using the STAPLE method. Results were compared between the proposed DUL and physician-generated contours through the Dice similarity coefficients (DSC), the Hausdorff distances, and the distances of the center-of-mass.RESULTS: The average DSCs between DUL-based prostate contours and reference contours for test data in Group one and Group two-consensus were 0.83 ± 0.04, and 0.85 ± 0.04, respectively. Correspondingly, the mean center-of-mass distances were 3.52 mm ± 1.15 mm, and 2.98 mm ± 1.42 mm, respectively.CONCLUSIONS: This novel DUL technique can automatically propagate the contour of the prostate from pCT to CBCT. The proposed method shows that highly accurate contour propagation for CBCT-guided adaptive radiotherapy is achievable via the deep learning technique.

    View details for DOI 10.1002/mp.14755

    View details for PubMedID 33544390

  • Multicellular spheroids as in vitro models of oxygen depletion during FLASH irradiation. International journal of radiation oncology, biology, physics Khan, S., Bassenne, M., Wang, J., Manjappa, R., Melemenidis, S., Breitkreutz, D. Y., Maxim, P. G., Xing, L., Loo, B. W., Pratx, G. 2021

    Abstract

    PURPOSE: The differential response of normal and tumor tissues to ultra-high dose rate radiation (FLASH) has raised new hope for treating solid tumors but, to date, the mechanism remains elusive. One leading hypothesis is that FLASH radiochemically depletes oxygen from irradiated tissues faster than it is replenished through diffusion. The purpose of this study is to investigate these effects within hypoxic multicellular tumor spheroids, through simulations and experiments.MATERIALS AND METHODS: Physicobiological equations were derived to model (i) the diffusion and metabolism of oxygen within spheroids; (ii) its depletion through reactions involving radiation-induced radicals; and (iii) the increase in radioresistance of spheroids, modeled according to the classical oxygen enhancement ratio and linear-quadratic response. These predictions were then tested experimentally in A549 spheroids exposed to electron irradiation at conventional (0.075 Gy/s) or FLASH (90 Gy/s) dose rates. Clonogenic survival, cell viability, and spheroid growth were scored post-radiation. Clonogenic survival of two other cell lines was also investigated.RESULTS: The existence of a hypoxic core in unirradiated tumor spheroids is predicted by simulations and visualized by fluorescence microscopy. Upon FLASH irradiation, this hypoxic core transiently expands, engulfing a large number of well-oxygenated cells. In contrast, oxygen is steadily replenished during slower conventional irradiation. Experimentally, clonogenic survival was around 3-fold higher in FLASH-irradiated spheroid compared to conventional irradiation, but no significant difference was observed for well-oxygenated 2D-cultured cells. This differential survival is consistent with the predictions of the computational model. FLASH irradiation of spheroids resulted in a dose-modifying factor of around 1.3 for doses above 10 Gy.CONCLUSION: Tumor spheroids can be used as a model to study FLASH irradiation in vitro . The improved survival of tumor spheroids receiving FLASH radiation confirms that ultra-fast radiochemical oxygen depletion and its slow replenishment are critical components of the FLASH effect.

    View details for DOI 10.1016/j.ijrobp.2021.01.050

    View details for PubMedID 33545301

  • Closing the Gap Between Deep Neural Network Modeling and Biomedical Decision-Making Metrics in Segmentation via Adaptive Loss Functions IEEE TRANSACTIONS ON MEDICAL IMAGING Seo, H., Bassenne, M., Xing, L. 2021; 40 (2): 585–93

    Abstract

    Deep learning is becoming an indispensable tool for various tasks in science and engineering. A critical step in constructing a reliable deep learning model is the selection of a loss function, which measures the discrepancy between the network prediction and the ground truth. While a variety of loss functions have been proposed in the literature, a truly optimal loss function that maximally utilizes the capacity of neural networks for deep learning-based decision-making has yet to be established. Here, we devise a generalized loss function with functional parameters determined adaptively during model training to provide a versatile framework for optimal neural network-based decision-making in small target segmentation. The method is showcased by more accurate detection and segmentation of lung and liver cancer tumors as compared with the current state-of-the-art. The proposed formalism opens new opportunities for numerous practical applications such as disease diagnosis, treatment planning, and prognosis.

    View details for DOI 10.1109/TMI.2020.3031913

    View details for Web of Science ID 000615044900012

    View details for PubMedID 33074800

    View details for PubMedCentralID PMC7858236

  • Calibrated uncertainty estimation for interpretable proton computed tomography image correction using Bayesian deep learning. Physics in medicine and biology Nomura, Y. n., Tanaka, S. n., Wang, J. n., Shirato, H. n., Shimizu, S. n., Xing, L. n. 2021

    Abstract

    Integrated-type proton computed tomography (pCT) measures proton stopping power ratio (SPR) images for proton therapy treatment planning, but its image quality is degraded due to noise and scatter. Although several correction methods have been proposed, techniques that include estimation of uncertainty are limited. This study proposes a novel uncertainty-aware pCT image correction method using a Bayesian convolutional neural network (BCNN). A DenseNet-based BCNN was constructed to predict both a corrected SPR image and its uncertainty from a noisy SPR image. A total 432 noisy SPR images of 6 non-anthropomorphic and 3 head phantoms were collected with Monte Carlo simulations, while true noise-free images were calculated with known geometric and chemical components. Heteroscedastic loss and deep ensemble techniques were performed to estimate aleatoric and epistemic uncertainties by training 25 unique BCNN models. 200-epoch end-to-end training was performed for each model independently. Feasibility of the predicted uncertainty was demonstrated after applying two post-hoc calibrations and calculating spot-specific path length uncertainty distribution. For evaluation, accuracy of head SPR images and water-equivalent thickness (WET) corrected by the trained BCNN models was compared with a conventional method and non-Bayesian CNN model. BCNN-corrected SPR images represent noise-free images with high accuracy. Mean absolute error in test data was improved from 0.263 for uncorrected images to 0.0538 for BCNN-corrected images. Moreover, the calibrated uncertainty represents accurate confidence levels, and the BCNN-corrected calibrated WET was more accurate than non-Bayesian CNN with high statistical significance. Computation time for calculating one image and its uncertainties with 25 BCNN models is 0.7 seconds with a consumer grade GPU. Our model is able to predict accurate pCT images as well as two types of uncertainty. These uncertainties will be useful to identify potential cause of SPR errors and develop a spot-specific range margin criterion, toward elaboration of uncertainty-guided proton therapy.

    View details for DOI 10.1088/1361-6560/abe956

    View details for PubMedID 33626513

  • TransCT: Dual-Path Transformer for Low Dose Computed Tomography Zhang, Z., Yu, L., Liang, X., Zhao, W., Xing, L., deBruijne, M., Cattin, P. C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. SPRINGER INTERNATIONAL PUBLISHING AG. 2021: 55-64
  • MR to ultrasound image registration with segmentation-based learning for HDR prostate brachytherapy. Medical physics Chen, Y. n., Xing, L. n., Yu, L. n., Liu, W. n., Fahimian, B. P., Niedermayr, T. n., Bagshaw, H. P., Buyyounouski, M. n., Han, B. n. 2021

    Abstract

    Propagation of contours from high-quality magnetic resonance (MR) images to treatment planning ultrasound (US) images with severe needle artifacts is a challenging task, which can greatly aid the organ contouring in high dose rate (HDR) prostate brachytherapy. In this study, a deep learning approach was developed to automatize this registration procedure for HDR brachytherapy practice.Because of the lack of training labels and difficulty of accurate registration from inferior image quality, a new segmentation-based registration framework was proposed for this multi-modality image registration problem. The framework consisted of two segmentation networks and a deformable registration network, based on the weakly-supervised registration strategy. Specifically, two 3D V-Nets were trained for the prostate segmentation on the MR and US images separately, to generate the weak supervision labels for the registration network training. Besides the image pair, the corresponding prostate probability maps from the segmentation were further fed to the registration network to predict the deformation matrix, and an augmentation method was designed to randomly scale the input and label probability maps during the registration network training. The overlap between the deformed and fixed prostate contours was analyzed to evaluate the registration accuracy. Three datasets were collected from our institution for the MR and US image segmentation networks, and the registration network learning, which contained 121, 104 and 63 patient cases, respectively.The mean Dice similarity coefficient (DSC) results of the two prostate segmentation networks are 0.86±0.05 and 0.90±0.03, for MR images and the US images after the needle insertion, respectively. The mean DSC, center-of-mass (COM) distance, Hausdorff distance (HD) and averaged symmetric surface distance (ASSD) results for the registration of manual prostate contours were 0.87±0.05, 1.70±0.89 mm, 7.21±2.07 mm, 1.61±0.64 mm, respectively. By providing the prostate probability map from the segmentation to the registration network, as well as applying the random map augmentation method, the evaluation results of the four metrics were all improved, such as an increase of DSC from 0.83±0.08 to 0.86±0.06 and from 0.86±0.06 to 0.87±0.05, respectively.A novel segmentation-based registration framework was proposed to automatically register prostate MR images to the treatment planning US images with metal artifacts, which not only largely saved the labor work on the data preparation, but also improved the registration accuracy. The evaluation results showed the potential of this approach in HDR prostate brachytherapy practice.

    View details for DOI 10.1002/mp.14901

    View details for PubMedID 33905566

  • A robotically assisted 3D printed quality assurance lung phantom for Calypso. Physics in medicine and biology Capaldi, D. P., Skinner, L. B., Dubrowski, P. n., Zhang, H. n., Xing, L. n., Chuang, C. F., Loo, B. W., Bush, K. K., Fahimian, B. P., Yu, A. S. 2021

    Abstract

    Purpose:Radiation dose delivered to targets located near the upper-abdomen or in the thorax are significantly affected by respiratory-motion. Relatively large-margins are commonly added to compensate for this motion, limiting radiation-dose-escalation. Internal-surrogates of target motion, such as a radiofrequency (RF) tracking system, i.e. Calypso® System, are used to overcome this challenge and improve normal-tissue sparing. RF tracking systems consist of implanting transponders in the vicinity of the tumor to be tracked using radiofrequency-waves. Unfortunately, although the manufacture provides a universal quality-assurance (QA) phantom, QA-phantoms specifically for lung-applications are limited, warranting the development of alternative solutions to fulfil the tests mandated by AAPM's TG142. Accordingly, our objective was to design and develop a motion-phantom to evaluate Calypso for lung-applications that allows the Calypso® Beacons to move in different directions to better simulate true lung-motion.Methods and Materials:A Calypso lung QA-phantom was designed, and 3D-printed. The design consists of three independent arms where the transponders were attached. A pinpoint-chamber with a buildup-cap was also incorporated. A 4-axis robotic arm was programmed to drive the motion-phantom to mimic breathing. After acquiring a four-dimensional-computed-tomography (4DCT) scan of the motion-phantom, treatment-plans were generated and delivered on a Varian TrueBeam® with Calypso capabilities. Stationary and gated-treatment plans were generated and delivered to determine the dosimetric difference between gated and non-gated treatments. Portal cine-images were acquired to determine the temporal-accuracy of delivery by calculating the difference between the observed versus expected transponders locations with the known speed of the transponders' motion.Results:Dosimetric accuracy is better than TG142 tolerance of 2%. Temporal accuracy is greater than, TG142 tolerance of 100ms for beam-on, but less than 100ms for beam-hold.Conclusions:The robotic QA-phantom designed and developed in this study provides an independent phantom for performing Calypso lung-QA for commissioning and acceptance testing of Calypso for lung treatments.

    View details for DOI 10.1088/1361-6560/abebaa

    View details for PubMedID 33657537

  • Deep Sinogram Completion With Image Prior for Metal Artifact Reduction in CT Images IEEE TRANSACTIONS ON MEDICAL IMAGING Yu, L., Zhang, Z., Li, X., Xing, L. 2021; 40 (1): 228–38

    Abstract

    Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence of metallic objects, which could lead to severe metal artifacts and influence clinical diagnosis or dose calculation in radiation therapy. In this article, we propose a generalizable framework for metal artifact reduction (MAR) by simultaneously leveraging the advantages of image domain and sinogram domain-based MAR techniques. We formulate our framework as a sinogram completion problem and train a neural network (SinoNet) to restore the metal-affected projections. To improve the continuity of the completed projections at the boundary of metal trace and thus alleviate new artifacts in the reconstructed CT images, we train another neural network (PriorNet) to generate a good prior image to guide sinogram learning, and further design a novel residual sinogram learning strategy to effectively utilize the prior image information for better sinogram completion. The two networks are jointly trained in an end-to-end fashion with a differentiable forward projection (FP) operation so that the prior image generation and deep sinogram completion procedures can benefit from each other. Finally, the artifact-reduced CT images are reconstructed using the filtered backward projection (FBP) from the completed sinogram. Extensive experiments on simulated and real artifacts data demonstrate that our method produces superior artifact-reduced results while preserving the anatomical structures and outperforms other MAR methods.

    View details for DOI 10.1109/TMI.2020.3025064

    View details for Web of Science ID 000604883800020

    View details for PubMedID 32956044

  • Deep learning-augmented radiotherapy visualization with a cylindrical radioluminescence system. Physics in medicine and biology Jia, M., Li, X., Wu, Y., Yang, Y., Kasimbeg, P., Skinner, L. B., Wang, L., Xing, L. 2020

    Abstract

    This study aims to demonstrate a low-cost camera-based radioluminescence imaging system (CRIS) for high-quality beam visualization that encourages accurate pre-treatment verifications on radiation delivery in external beam radiotherapy. To ameliorate the optical image that suffers from mirror glare and edge blurring caused by photon scattering, a deep learning model is proposed and trained to learn from an on-board electronic portal imaging device (EPID). Beyond the typical purposes of an on-board EPID, the developed system maintains independent measurement with co-planar detection ability by involving a cylindrical receptor. Three task-aware modules are integrated into the network design to enhance its robustness against the artifacts that exist in an EPID running at the cine mode for efficient image acquisition. The training data consists of various designed beam fields that were modulated via the multi-leaf collimator (MLC). Validation experiments are performed for five regular fields ranging from 2 * 2 cm2 to 10 * 10 cm2 and three clinical IMRT cases. The captured CRIS images are compared to the high-quality images collected from an EPID running at the integration-mode, in terms of gamma index and other typical similarity metrics. The mean 2% / 2mm gamma pass rate is 99.14% (range between 98.6% and 100%) and 97.1% (ranging between 96.3% and 97.9%), for the regular fields and IMRT cases, respectively. The CRIS is further applied as a tool for MLC leaf-end position verification. A rectangular field with introduced leaf displacement is designed, and the measurements using CRIS and EPID agree within 0.100 mm ± 0.072 mm with maximum of 0.292 mm. Coupled with its simple system design and low-cost nature, the technique promises to provide viable choice for routine quality assurance in radiation oncology practice.

    View details for DOI 10.1088/1361-6560/abd673

    View details for PubMedID 33361563

  • Deep Learning Prediction of Cancer Prevalence from Satellite Imagery. Cancers Bibault, J., Bassenne, M., Ren, H., Xing, L. 2020; 12 (12)

    Abstract

    The worldwide growth of cancer incidence can be explained in part by changes in the prevalence and distribution of risk factors. There are geographical gaps in the estimates of cancer prevalence, which could be filled with innovative methods. We used deep learning (DL) features extracted from satellite images to predict cancer prevalence at the census tract level in seven cities in the United States. We trained the model using detailed cancer prevalence estimates from 2018 available in the CDC (Center for Disease Control) 500 Cities project. Data from 3500 census tracts covering 14,483,366 inhabitants were included. Features were extracted from 170,210 satellite images with deep learning. This method explained up to 64.37% (median = 43.53%) of the variation of cancer prevalence. Satellite features are highly correlated with individual socioeconomic and health measures that are linked to cancer prevalence (age, smoking and drinking status, and obesity). A higher similarity between two environments is associated with better generalization of the model (p = 1.10-6). This method can be used to accurately estimate cancer prevalence at a high spatial resolution without using surveys at a fraction of the cost.

    View details for DOI 10.3390/cancers12123844

    View details for PubMedID 33352801

  • Pulmonary Ventilation Maps Generated with Free-breathing Proton MRI and a Deep Convolutional Neural Network. Radiology Capaldi, D. P., Guo, F., Xing, L., Parraga, G. 2020: 202861

    Abstract

    Background Hyperpolarized noble gas MRI helps measure lung ventilation, but clinical translation remains limited. Free-breathing proton MRI may help quantify lung function using existing MRI systems without contrast material and may assist in providing information about ventilation not visible to the eye or easily extracted with segmentation methods. Purpose To explore the use of deep convolutional neural networks (DCNNs) to generate synthetic MRI ventilation scans from free-breathing MRI (deep learning [DL] ventilation MRI)-derived specific ventilation maps as a surrogate of noble gas MRI and to validate this approach across a wide range of lung diseases. Materials and Methods In this secondary analysis of prospective trials, 114 paired noble gas MRI and two-dimensional free-breathing MRI scans were obtained in healthy volunteers with no history of chronic or acute respiratory disease and in study participants with a range of different obstructive lung diseases, including asthma, bronchiectasis, chronic obstructive pulmonary disease, and non-small-cell lung cancer between September 2013 and April 2018 (ClinicalTrials.gov identifiers: NCT03169673, NCT02351141, NCT02263794, NCT02282202, NCT02279329, and NCT02002052). A U-Net-based DCNN model was trained to map free-breathing proton MRI to hyperpolarized helium 3 (3He) MRI ventilation and validated using a sixfold validation. During training, the DCNN ventilation maps were compared with noble gas MRI scans using the Pearson correlation coefficient (r) and mean absolute error. DCNN ventilation images were segmented for ventilation and ventilation defects and were compared with noble gas MRI scans using the Dice similarity coefficient (DSC). Relationships were evaluated with the Spearman correlation coefficient (rS). Results One hundred fourteen study participants (mean age, 56 years ± 15 [standard deviation]; 66 women) were evaluated. As compared with 3He MRI, DCNN model ventilation maps had a mean r value of 0.87 ± 0.08. The mean DSC for DL ventilation MRI and 3He MRI ventilation was 0.91 ± 0.07. The ventilation defect percentage for DL ventilation MRI was highly correlated with 3He MRI ventilation defect percentage (rS = 0.83, P < .001, mean bias = -2.0% ± 5). Both DL ventilation MRI (rS = -0.51, P < .001) and 3He MRI (rS = -0.61, P < .001) ventilation defect percentage were correlated with the forced expiratory volume in 1 second. The DCNN model required approximately 2 hours for training and approximately 1 second to generate a ventilation map. Conclusion In participants with diverse pulmonary pathologic findings, deep convolutional neural networks generated ventilation maps from free-breathing proton MRI trained with a hyperpolarized noble-gas MRI ventilation map data set. The maps showed correlation with noble gas MRI ventilation and pulmonary function measurements. © RSNA, 2020 See also the editorial by Vogel-Claussen in this issue.

    View details for DOI 10.1148/radiol.2020202861

    View details for PubMedID 33289613

  • Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis IEEE TRANSACTIONS ON MEDICAL IMAGING Li, X., Jia, M., Islam, M., Yu, L., Xing, L. 2020; 39 (12): 4023–33

    Abstract

    The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decision-making. However, developing such automatic solutions is challenging due to the requirement of a large amount of human-annotated data. Recently, unsupervised/self-supervised feature learning techniques receive a lot of attention, as they do not need massive annotations. Most of the current self-supervised methods are analyzed with single imaging modality and there is no method currently utilize multi-modal images for better results. Considering that the diagnostics of various vitreoretinal diseases can greatly benefit from another imaging modality, e.g., FFA, this paper presents a novel self-supervised feature learning method by effectively exploiting multi-modal data for retinal disease diagnosis. To achieve this, we first synthesize the corresponding FFA modality and then formulate a patient feature-based softmax embedding objective. Our objective learns both modality-invariant features and patient-similarity features. Through this mechanism, the neural network captures the semantically shared information across different modalities and the apparent visual similarity between patients. We evaluate our method on two public benchmark datasets for retinal disease diagnosis. The experimental results demonstrate that our method clearly outperforms other self-supervised feature learning methods and is comparable to the supervised baseline. Our code is available at GitHub.

    View details for DOI 10.1109/TMI.2020.3008871

    View details for Web of Science ID 000595547500024

    View details for PubMedID 32746140

  • Automated multi-parameter high-dose-rate brachytherapy quality assurance via radioluminescence imaging. Physics in medicine and biology Jia, M., Kim, T. J., Yang, Y., Xing, L., Jean, P. D., Grafil, E., Jenkins, C. H., Fahimian, B. P. 2020; 65 (22): 225005

    Abstract

    The purpose of this study is to leverage radioluminescence imaging for the development of an automated high-dose-rate (HDR) brachytherapy quality assurance (QA) system that enables simultaneous measurements of dwell position, dwell time, wire velocity, and relative source strength in a single test. The system consists of a radioluminescence phosphor sheet (a mixture of Gd2O2S:Tb and PDMS) positioned atop a HDR needle applicator, a complementary metal-oxide-semiconductor digital camera used to capture the emitted radioluminescence signals from the scintillator sheet, and an in-house graphical user interface for signal processing. The signal processing was used to extract source intensity, location, and elapsed time, yielding the final measurements on dwell position, dwell time, and wire velocity. The source strength relative to the well chamber calibration (in unit of Air-Kerma strength, Sk ) is measured by establishing a calibration curve that correlates Sk with the detector response. Validation experiments are performed using three customized treatment plans. With these plans, the dwell position and dwell time are verified for a range of 110.0 cm-117.5 cm and 2 s-16 s, respectively, and the linear correlation with Sk is demonstrated for the source strength varying between 28348 U (cGy cm2 h-1) and 41906 U. The wire velocity, i.e. the speed of the radioactive source averaged over the distance in between dwell positions, is calculated for various distances ranging from 5 mm to 50 mm. Results show that the mean deviations of the measured dwell position and dwell time are 0.1 mm (range from 0 to 0.2 mm) and 32.5 ms (range from 0 to 60.0 ms) with respect to the planned values, respectively, and the system response is highly linear with Sk ( R2 = 0.998). Moreover, the measured wire velocities are comparable to previously reported values. Benefitting from the compact hardware design and image processing algorithms, the system provides a practical, reliable, and comprehensive solution for HDR QA.

    View details for DOI 10.1088/1361-6560/abb570

    View details for PubMedID 33200751

  • Artificial Intelligence should be part of medical physics graduate program curriculum. Medical physics Xing, L., Goetsch, S., Cai, J. 2020

    Abstract

    Recent advances in artificial intelligence (AI) have transformed it from an indispensable tool in research and development into a mainstream technology that is fundamentally altering how we work and live. AI has already been incorporated into some common medical physics tools used to support key tasks such as diagnosis, treatment planning, and quality assurance. A paradigm shift in clinical practice in which AI is used more widely is likely to occur in the near future. Some therefore advocate incorporating AI into the medical physics graduate program curriculum to better adapt workers' skill sets to this new paradigm. However, others have reservations about such curricular adaptation. This is the premise debated in this month's Point/Counterpoint.

    View details for DOI 10.1002/mp.14587

    View details for PubMedID 33159812

  • A data-driven dimensionality-reduction algorithm for the exploration of patterns in biomedical data. Nature biomedical engineering Islam, M. T., Xing, L. 2020

    Abstract

    Dimensionality reduction is widely used in the visualization, compression, exploration and classification of data. Yet a generally applicable solution remains unavailable. Here, we report an accurate and broadly applicable data-driven algorithm for dimensionality reduction. The algorithm, which we named 'feature-augmented embedding machine' (FEM), first learns the structure of the data and the inherent characteristics of the data components (such as central tendency and dispersion), denoises the data, increases the separation of the components, and then projects the data onto a lower number of dimensions. We show that the technique is effective at revealing the underlying dominant trends in datasets of protein expression and single-cell RNA sequencing, computed tomography, electroencephalography and wearable physiological sensors.

    View details for DOI 10.1038/s41551-020-00635-3

    View details for PubMedID 33139824

  • Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation. IEEE transactions on medical imaging Olefir, I., Tzoumas, S., Restivo, C., Mohajerani, P., Xing, L., Ntziachristos, V. 2020; 39 (11): 3643–54

    Abstract

    Label free imaging of oxygenation distribution in tissues is highly desired in numerous biomedical applications, but is still elusive, in particular in sub-epidermal measurements. Eigenspectra multispectral optoacoustic tomography (eMSOT) and its Bayesian-based implementation have been introduced to offer accurate label-free blood oxygen saturation (sO2) maps in tissues. The method uses the eigenspectra model of light fluence in tissue to account for the spectral changes due to the wavelength dependent attenuation of light with tissue depth. eMSOT relies on the solution of an inverse problem bounded by a number of ad hoc hand-engineered constraints. Despite the quantitative advantage offered by eMSOT, both the non-convex nature of the optimization problem and the possible sub-optimality of the constraints may lead to reduced accuracy. We present herein a neural network architecture that is able to learn how to solve the inverse problem of eMSOT by directly regressing from a set of input spectra to the desired fluence values. The architecture is composed of a combination of recurrent and convolutional layers and uses both spectral and spatial features for inference. We train an ensemble of such networks using solely simulated data and demonstrate how this approach can improve the accuracy of sO2 computation over the original eMSOT, not only in simulations but also in experimental datasets obtained from blood phantoms and small animals (mice) in vivo. The use of a deep-learning approach in optoacoustic sO2 imaging is confirmed herein for the first time on ground truth sO2 values experimentally obtained in vivo and ex vivo.

    View details for DOI 10.1109/TMI.2020.3001750

    View details for PubMedID 32746111

  • Distinctive Energy Profile of Water-Soluble, Thiolate-Protected Gold Nanoparticles as Potential Molecular Marker for Vulnerable Plaque Detection with XFCT Imaging. Journal of radiology and radiation therapy Zaman, R. T., Vernekhol, D., Xing, L. 2020; 8 (1)

    Abstract

    X-ray CT plays a pivotal role in diagnostic imaging, radiotherapy, and its indispensable contribution to preclinical small animal imaging research. This study characterizes a distinctive energy spectrum of a novel 3-mercaptobenzoic-acid (3MBA)-protected-144-atoms gold-nanoparticles (3MBA-Au-144-NPs) after X-ray excitation and detects vulnerable atherosclerotic plaques non-invasively using this novel contrast agent in mice carotid arteries for the first time to the best of our knowledge.We designed a four-chamber heart apex model using a 3D-printer and filled with four different concentrations of 3MBA-Au-144-NPs. The X-ray system was equipped with a pencil beam collimator, which was calibrated using a 1×1 in2 large radiochromic film. The tube was operated at 320 kVp with 12.5 mA current and multiple filtration options were available for the X-ray excitation source. The resulting pencil beam had a 3.2 mm diameter. The four-chamber apex was translated and rotated relative to the stationary pencil beam. Each sample chamber was irradiated for 2-minutes and emitted fluorescent X-rays from the excited 3MBA-Au-144-NPs were collected with CdTe and Silicon Drift (SD) detectors for 15 seconds. The spectra were used for L-shell XRF peak isolation and sonogram generation of this novel 3MBA-Au-144-NPs. The distribution and concentration of 3MBA-Au-144-NPs were reconstructed with an alternative maximum likelihood expectation maximization algorithm. For in vivo detection of unstable plaques, we developed atherosclerotic mice model after feeding them 1% high cholesterol diet (HCD) for four weeks before diabetic was induced by intraperitoneal injection of streptozotocin (STZ) to accelerate the plaque progression. Two weeks after the diabetic induction, surgically left carotid artery was ligated. Two weeks after the surgical ligation, a 250 μL of 3MBA-Au-144-NPs was IV injected after 6 hours of fasting. One hour after injection, the mice were imaged non-invasively with a cone-beam micro-CT system.Two distinctive L-shell energy peaks were observed at 10 KeV and 11.13 KeV for 3MBA-Au-144-NPs in the energy spectrum of the SD detector. K-shell fluorescence events vanished in the Compton scatter and characteristic background of the tungsten source due to the lead shielding for the SD and CdTe detectors. There is a space missing at 12.5 KeV. The signal intensity varied with different 3MBA-Au-144-NPs concentration of 5%, 10%, 20%, and 100%. The X-ray fluorescence (XRF) intensity showed a highly linear response (R2=0.999) with respect to different concentrations of 3MBA-Au-144-NPs. High XRF signal was detected in the left carotid artery at 2 mm below the ligation and in aortic arch. Non-ligated right carotid artery (negative control) showed no such signal.These distinct energy spectra in the L-shell fluorescent energies render 3MBA-Au-144-NPs as a viable contrast agent for future in vivo XFCT imaging.

    View details for PubMedID 35822084

    View details for PubMedCentralID PMC9272981

  • Development and validation of a model to predict survival in colorectal cancer using a gradient-boosted machine. Gut Bibault, J., Chang, D. T., Xing, L. 2020

    Abstract

    OBJECTIVE: The success of treatment planning relies critically on our ability to predict the potential benefit of a therapy. In colorectal cancer (CRC), several nomograms are available to predict different outcomes based on the use of tumour specific features. Our objective is to provide an accurate and explainable prediction of the risk to die within 10 years after CRC diagnosis, by incorporating the tumour features and the patient medical and demographic information.DESIGN: In the prostate, lung, colorectal and ovarian cancer screening (PLCO) Trial, participants (n=154 900) were randomised to screening with flexible sigmoidoscopy, with a repeat screening at 3 or 5 years, or to usual care. We selected patients who were diagnosed with CRC during the follow-up to train a gradient-boosted model to predict the risk to die within 10 years after CRC diagnosis. Using Shapley values, we determined the 20 most relevant features and provided explanation to prediction.RESULTS: During the follow-up, 2359 patients were diagnosed with CRC. Median follow-up was 16.8 years (14.4-18.9) for mortality. In total, 686 patients (29%) died from CRC during the follow-up. The dataset was randomly split into a training (n=1887) and a testing (n=472) dataset. The area under the receiver operating characteristic was 0.84 (±0.04) and accuracy was 0.83 (±0.04) with a 0.5 classification threshold. The model is available online for research use.CONCLUSIONS: We trained and validated a model with prospective data from a large multicentre cohort of patients. The model has high predictive performances at the individual scale. It could be used to discuss treatment strategies.

    View details for DOI 10.1136/gutjnl-2020-321799

    View details for PubMedID 32887732

  • Second window near-infrared dosimeter (NIR2D) system for radiation dosimetry. Physics in medicine and biology Kim, T. J., Cheng, K., Zhang, H., Liu, S., Skinner, L., Xing, L. 2020; 65 (17): 175013

    Abstract

    Fiber-coupled scintillation dosimeters are a cost-effective alternative to the conventional ion chambers in radiation dosimetry. However, stem effects from optical fibers such as Cerenkov radiation incur significant errors in the readout signal. Here we introduce a second near-infrared window dosimeter, dubbed as NIR2D, that can potentially be used as real-time radiation detector for clinical megavoltage beams. Lanthanide-based rare-earth NaYF4 nano-phosphors doped with both erbium and cerium elements were synthesized, and a compact 3D printed reader device integrated with a photodetector and data acquisition system was designed. The performance of the NIR2D was tested using a pre-clinical orthovoltage radiation source and a clinical megavoltage radiation source. The system was tested for dose linearity (100, 200, 600 MU), dose rate dependency (100, 200, 400, 600 MU min-1), and energy dependency (6, 10, 15 MV). Test results with the clinical linear accelerator demonstrated excellent dose linearity and dose rate independency when exposed to 6 MV linac beams-both data follows a linear trendline with R2 > 0.99. On the other hand, the NIR2D was energy dependent, where the readout dropped by 9% between 6 and 15 MV. For stem effects, we observed a finite Cerenkov contribution of 1%-3% when exposed between 100-600 MU min-1 (6 MV) and 3%-6% when exposed between 5-15 MV (600 MU min-1). While the stem effects were still observable, we expect that enhancing the current optical setup will simultaneously improve the scintillation signal and reduce the stem effects.

    View details for DOI 10.1088/1361-6560/ab9b56

    View details for PubMedID 32869751

  • Automatic Polyp Recognition in Colonoscopy Images Using Deep Learning and Two-Stage Pyramidal Feature Prediction IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M. 2020; 17 (3): 1570–84
  • Accelerating quantitative MR imaging with the incorporation of B1 compensation using deep learning. Magnetic resonance imaging Wu, Y., Ma, Y., Du, J., Xing, L. 2020

    Abstract

    Quantitative magnetic resonance imaging (MRI) attracts attention due to its support to quantitative image analysis and data driven medicine. However, the application of quantitative MRI is severely limited by the long data acquisition time required by repetitive image acquisition and measurement of field map. Inspired by recent development of artificial intelligence, we propose a deep learning strategy to accelerate the acquisition of quantitative MRI, where every quantitative T1 map is derived from two highly undersampled variable-contrast images with radiofrequency field inhomogeneity automatically compensated. In a multi-step framework, variable-contrast images are first jointly reconstructed from incoherently undersampled images using convolutional neural networks; then T1 map and B1 map are predicted from reconstructed images employing deep learning. Thus, the acceleration includes undersampling in every input image, a reduction in the number of variable contrast images, as well as a waiver of B1 map measurement. The strategy is validated in T1 mapping of cartilage. Acquired with a consistent imaging protocol, 1224 image sets from 51 subjects are used for the training of the prediction models, and 288 image sets from 12 subjects are used for testing. High degree of acceleration is achieved with image fidelity well maintained. The proposed method can be broadly applied to quantify other tissue properties (e.g. T2, T1rho) as well.

    View details for DOI 10.1016/j.mri.2020.06.011

    View details for PubMedID 32610065

  • Deep learning applications in automatic needle segmentation in ultrasound-guided prostate brachytherapy. Medical physics Wang, F., Xing, L., Bagshaw, H., Buyyounouski, M., Han, B. 2020

    Abstract

    PURPOSE: High-Dose-Rate (HDR) brachytherapy is one of the most effective ways to treat the prostate cancer, which is the second most common cancer in men worldwide. This treatment delivers highly conformal dose through the transperineal needle implants and is guided by a real time ultrasound (US) imaging system. Currently, the brachytherapy needles in the US images are manually segmented by physicists during the treatment, which is time-consuming and error-prone. In this study, we propose a set of deep learning based algorithms to accurately segment the brachytherapy needles and locate the needle tips from the US images.METHODS: Two deep neural networks are developed to address this problem. First, a modified deep U-Net is used to segment the pixels belonging to the brachytherapy needles from the US images. Second, an additional VGG-16 based deep convolutional network is combined with the segmentation network to predict the locations of the needle tips. The networks are trained and evaluated on a clinical US images dataset with labeled needle trajectories collected in our hospital (Institutional Review Board approval (IRB 41755)).RESULTS: The evaluation results show that our method can accurately extract the trajectories of the needles with a resolution of 0.668 mm and 0.319 mm in x and y direction respectively. 95.4% of the x direction and 99.2% of the y direction have error ≤ 2 mm. Moreover, The position resolutions of the tips are 0.721 mm, 0.369 mm and 1.877 mm in x, y and z directions respectively, while 94.2%, 98.3% and 67.5% of the data have error ≤ 2 mm.CONCLUSIONS: This paper proposed a neural network based algorithm to segment the brachytherapy needles from the US images and locate the needle tip. It can be used in the HDR brachytherapy to help improve the efficiency and quality of the treatments.

    View details for DOI 10.1002/mp.14328

    View details for PubMedID 32542758

  • Automated hepatobiliary toxicity prediction after liver stereotactic body radiation therapy with deep learning -based portal vein segmentation NEUROCOMPUTING Ibragimov, B., Toesca, D. S., Chang, D. T., Yuan, Y., Koong, A. C., Xing, L. 2020; 392: 181–88
  • Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state-of-art applications. Medical physics Seo, H., Badiei Khuzani, M., Vasudevan, V., Huang, C., Ren, H., Xiao, R., Jia, X., Xing, L. 2020; 47 (5): e148–e167

    Abstract

    In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. In this review article, we highlight the imperative role of machine learning algorithms in enabling efficient and accurate segmentation in the field of medical imaging. We specifically focus on several key studies pertaining to the application of machine learning methods to biomedical image segmentation. We review classical machine learning algorithms such as Markov random fields, k-means clustering, random forest, etc. Although such classical learning models are often less accurate compared to the deep-learning techniques, they are often more sample efficient and have a less complex structure. We also review different deep-learning architectures, such as the artificial neural networks (ANNs), the convolutional neural networks (CNNs), and the recurrent neural networks (RNNs), and present the segmentation results attained by those learning models that were published in the past 3yr. We highlight the successes and limitations of each machine learning paradigm. In addition, we discuss several challenges related to the training of different machine learning models, and we present some heuristics to address those challenges.

    View details for DOI 10.1002/mp.13649

    View details for PubMedID 32418337

  • Electro-thermal therapy: Microsecond duration pulsed electric field tissue ablation with dynamic temperature control algorithms COMPUTERS IN BIOLOGY AND MEDICINE Sano, M. B., Petrella, R. A., Kaufman, J. D., Fesmire, C. C., Xing, L., Gerber, D., Fogle, C. A. 2020; 121: 103807

    Abstract

    Electro-thermal therapy (ETT) is a new cancer treatment modality which combines the use of high voltage pulsed electric fields, dynamic energy delivery rates, and closed loop thermal control algorithms to rapidly and reproducibly create focal ablations. This study examines the ablative potential and profile of pulsed electric field treatments delivered in conjunction with precise temperature control algorithms. An ex vivo perfused liver model was utilized to demonstrate the capability of 5000 V 2 μs duration bipolar electrical pulses and dynamic temperature control algorithms to produce ablations. Using a three applicator array, 4 cm ablation zones were created in under 27 min. In this configuration, the algorithms were able to rapidly achieve and maintain temperatures of 80 °C at the tissue-electrode interface. A simplified single applicator and grounding pad approach was used to correlate the measured ablation zones to electric field isocontours in order to determine lethal electric field thresholds of 708 V/cm and 867 V/cm for 45 °C and 60 °C treatments, respectively. These results establish ETT as a viable method for hepatic tumor treatment with ablation profiles equivalent to other energy based techniques. The single applicator and multi-applicator approaches demonstrated may enable the treatment of complex tumor geometries. The flexibility of ETT temperature control yields a malleable intervention which gives clinicians robust control over the ablation modality, treatment time, and safety profile.

    View details for DOI 10.1016/j.compbiomed.2020.103807

    View details for Web of Science ID 000542187300020

    View details for PubMedID 32568680

  • Temperature Dependence of High Frequency Irreversible Electroporation Evaluated in a 3D Tumor Model ANNALS OF BIOMEDICAL ENGINEERING Fesmire, C. C., Petrella, R. A., Fogle, C. A., Gerber, D. A., Xing, L., Sano, M. B. 2020; 48 (8): 2233–46

    Abstract

    Electroporation is a bioelectric phenomenon used to deliver target molecules into cells in vitro and irreversible electroporation (IRE) is an emerging cancer therapy used to treat inoperable tumors in situ. These phenomena are generally considered to be non-thermal in nature. In this study, a 3D tumor model was used to investigate the correlation between temperature and the effectiveness of standard clinical IRE and high frequency (H-FIRE) protocols. It was found for human glioblastoma cells that in the range of 2 to 37 °C the H-FIRE lethal electric field threshold value, which describes the minimum electric field to cause cell death, is highly dependent on temperature. Increasing the initial temperature from 2 to 37 °C resulted in a significant decrease in lethal electric field threshold from 1168 to 507 V/cm and a 139% increase in ablation size for H-FIRE burst treatments. Standard clinical protocol IRE treatments resulted in a decrease in lethal threshold from 485 to 453 V/cm and a 7% increase in ablation size over the same temperature range. Similar results were found for pancreatic cancer cells which indicate that tissue temperature may be a significant factor affecting H-FIRE ablation size and treatment planning in vivo while lower temperatures may be useful in maintaining cell viability for transfection applications.

    View details for DOI 10.1007/s10439-019-02423-w

    View details for Web of Science ID 000532898300001

    View details for PubMedID 32409902

  • Screening for chronic obstructive pulmonary disease with artificial intelligence. The Lancet. Digital health Bibault, J. E., Xing, L. 2020; 2 (5): e216-e217

    View details for DOI 10.1016/S2589-7500(20)30076-5

    View details for PubMedID 33328053

  • Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images IEEE TRANSACTIONS ON MEDICAL IMAGING Seo, H., Huang, C., Bassenne, M., Xiao, R., Xing, L. 2020; 39 (5): 1316–25

    Abstract

    Segmentation of livers and liver tumors is one of the most important steps in radiation therapy of hepatocellular carcinoma. The segmentation task is often done manually, making it tedious, labor intensive, and subject to intra-/inter- operator variations. While various algorithms for delineating organ-at-risks (OARs) and tumor targets have been proposed, automatic segmentation of livers and liver tumors remains intractable due to their low tissue contrast with respect to the surrounding organs and their deformable shape in CT images. The U-Net has gained increasing popularity recently for image analysis tasks and has shown promising results. Conventional U-Net architectures, however, suffer from three major drawbacks. First, skip connections allow for the duplicated transfer of low resolution information in feature maps to improve efficiency in learning, but this often leads to blurring of extracted image features. Secondly, high level features extracted by the network often do not contain enough high resolution edge information of the input, leading to greater uncertainty where high resolution edge dominantly affects the network's decisions such as liver and liver-tumor segmentation. Thirdly, it is generally difficult to optimize the number of pooling operations in order to extract high level global features, since the number of pooling operations used depends on the object size. To cope with these problems, we added a residual path with deconvolution and activation operations to the skip connection of the U-Net to avoid duplication of low resolution information of features. In the case of small object inputs, features in the skip connection are not incorporated with features in the residual path. Furthermore, the proposed architecture has additional convolution layers in the skip connection in order to extract high level global features of small object inputs as well as high level features of high resolution edge information of large object inputs. Efficacy of the modified U-Net (mU-Net) was demonstrated using the public dataset of Liver tumor segmentation (LiTS) challenge 2017. For liver-tumor segmentation, Dice similarity coefficient (DSC) of 89.72 %, volume of error (VOE) of 21.93 %, and relative volume difference (RVD) of - 0.49 % were obtained. For liver segmentation, DSC of 98.51 %, VOE of 3.07 %, and RVD of 0.26 % were calculated. For the public 3D Image Reconstruction for Comparison of Algorithm Database (3Dircadb), DSCs were 96.01 % for the liver and 68.14 % for liver-tumor segmentations, respectively. The proposed mU-Net outperformed existing state-of-art networks.

    View details for DOI 10.1109/TMI.2019.2948320

    View details for Web of Science ID 000532214700003

    View details for PubMedID 31634827

  • Screening for chronic obstructive pulmonary disease with artificial intelligence LANCET DIGITAL HEALTH Bibault, J., Xing, L. 2020; 2 (5): E216–E217
  • Densely Connected Neural Network With Unbalanced Discriminant and Category Sensitive Constraints for Polyp Recognition IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING Yuan, Y., Qin, W., Ibragimov, B., Zhang, G., Han, B., Meng, M., Xing, L. 2020; 17 (2): 574–83
  • REAL-TIME AUGMENTED BLADDER TUMOR DETECTION WITH DEEP LEARNING Chang, T., Shkolyar, E., Jia, X., Lee, T., Mach, K., Conti, S., Xing, L., Liao, J. LIPPINCOTT WILLIAMS & WILKINS. 2020: E1110
  • Hybrid Adversarial-Discriminative Network for Leukocyte Classification in Leukemia. Medical physics Zhang, C., Wu, S., Lu, Z., Shen, Y., Wang, J., Huang, P., Lou, J., Liu, C., Xing, L., Zhang, J., Xue, J., Li, D. 2020

    Abstract

    PURPOSE: Leukemia is a lethal disease that is harmful to bone marrow and overall blood health. The classification of white blood cell images is crucial for leukemia diagnosis. The purpose of this study is to classify white blood cells by extracting discriminative information from cell segmentation and combining it with the fine-grained features. We propose a hybrid adversarial residual network with support vector machine (SVM), which utilizes the extracted features to improve the classification accuracy for human peripheral white cells.METHODS: Firstly, we segment the cell and nucleus by utilizing an adversarial residual network, which contains a segmentation network and a discriminator network. To extract features that can handle the inter-class consistency problem effectively, we introduce the adversarial residual network. Then, we utilize convolutional neural network (CNN) features and histogram of oriented gradient (HOG) features, which can extract discriminative features from images of segmented cell nuclei. To utilize the representative features fully, a discriminative network is introduced to deal with neighboring information at different scales. Finally, we combine the vectors of HOG features with those of CNN features and feed them into a linear SVM to classify white blood cells into six types.RESULTS: We used three methods to evaluate the effect of leukocyte classification based on 5000 leukocyte images acquired from a local hospital. The first approach is to use the CNN features as the input of SVM to classify leukocytes, which achieved 94.23% specificity, 95.10% sensitivity, and 94.41% accuracy. The use of the HOG features for SVM achieved 83.50% specificity, 87.50% sensitivity, and 85.00% accuracy. The use of combined CNN and HOG features achieved 94.57% specificity, 96.11% sensitivity, and 95.93% accuracy.CONCLUSIONS: We propose a novel hybrid adversarial-discriminative network for the classification of microscopic leukocyte images. It improves the accuracy of cell classification, reduces the difficulty and time pressure of doctors' work, and economizes the valuable time of doctors in daily clinical diagnosis.

    View details for DOI 10.1002/mp.14144

    View details for PubMedID 32180243

  • Incorporating prior knowledge via volumetric deep residual network to optimize the reconstruction of sparsely sampled MRI MAGNETIC RESONANCE IMAGING Wu, Y., Ma, Y., Capaldi, D., Liu, J., Zhao, W., Du, J., Xing, L. 2020; 66: 93–103
  • Data-driven dose calculation algorithm based on deep U-Net. Physics in medicine and biology Fan, J. n., Xing, L. n., Dong, P. n., Wang, J. n., Hu, W. n., Yang, Y. n. 2020

    Abstract

    Accurate and efficient dose calculation is an important prerequisite to ensure the success of radiation therapy. However, all the dose calculation algorithms commonly used in current clinical practice have to compromise between calculation accuracy and efficiency, which may result in unsatisfactory dose accuracy or highly intensive computation time in many clinical situations. The purpose of this work is to develop a novel dose calculation algorithm based on the deep learning method for radiation therapy. In this study we performed a feasibility investigation on implementing a fast and accurate dose calculation based on a deep learning technique. A two dimensional (2D) fluence map was first converted into a three dimensional (3D) volume using ray traversal algorithm. A 3D U-Net like deep residual network was then established to learn a mapping between this converted 3D volume, CT and 3D dose distribution. Therefore an indirect relationship was built between a fluence map and its corresponding 3D dose distribution without using significantly complex neural networks. 200 patients, including nasopharyngeal, lung, rectum and breast cancer cases, were collected and applied to train the proposed network. Additional 47 patients were randomly selected to evaluate the accuracy of the proposed method through comparing dose distributions, dose volume histograms (DVH) and clinical indices with the results from a treatment planning system (TPS), which was used as the ground truth in this study. The proposed deep learning based dose calculation algorithm achieved good predictive performance. For 47 tested patients, the average per-voxel bias of the deep learning calculated value and standard deviation (normalized to the prescription), relative to the TPS calculation, is 0.17%±2.28%. The average deep learning calculated values and standard deviations for relevant clinical indices were compared with the TPS calculated results and the t-test p-values demonstrated the consistency between them.

    View details for DOI 10.1088/1361-6560/abca05

    View details for PubMedID 33181506

  • Obtaining dual-energy computed tomography (CT) information from a single-energy CT image for quantitative imaging analysis of living subjects by using deep learning Zhao, W., Lv, T., Lee, R., Chen, Y., Xing, L., Altman, R. B., Dunker, A. K., Hunter, L., Ritchie, M. D., Murray, T., Klein, T. E. WORLD SCIENTIFIC PUBL CO PTE LTD. 2020: 139-148
  • High-Frequency Irreversible Electroporation Using 5,000-V Waveforms to Create Reproducible 2-and 4-cm Ablation Zones - A Laboratory Investigation Using Mechanically Perfused Liver JOURNAL OF VASCULAR AND INTERVENTIONAL RADIOLOGY Kaufman, J. D., Fesmire, C. C., Petrella, R. A., Fogle, C. A., Xing, L., Gerber, D., Sano, M. B. 2020; 31 (1): 162–68

    Abstract

    To investigate if high-frequency irreversible electroporation (H-FIRE) treatments can be delivered at higher voltages and with greater energy delivery rates than currently implemented in clinical irreversible electroporation protocols.Treatments using 3,000 V and 5,000 V were administered to mechanically perfused ex vivo porcine liver via a single applicator and grounding pad (A+GP) as well as a 4-applicator array (4AA). Integrated energized times (IET) 0.01-0.08 seconds and energy delivery rates 25-300 μs/s were investigated. Organs were preserved at 4°C for 10-15 hours before sectioning and gross analysis using a metabolic stain to identify the size and shape of ablation zones.A+GP ablations measured between 1.6 cm and 2.2 cm, which did not increase when IET was increased from 0.02 seconds to 0.08 seconds (P > .055; range, 1.9-2.1 cm). Changes in tissue color and texture consistent with thermal damage were observed for treatments with energy delivery rates 50-300 μs/s, but not for treatments delivered at 25 μs/s. Use of the 4AA with a 3-cm applicator spacing resulted in ablations measuring 4.4-4.9 cm with energy delivery times of 7-80 minutes.H-FIRE treatments can rapidly and reproducibly create 2-cm ablations using an A+GP configuration. Treatments without thermal injury were produced at the expense of extended treatment times. More rapid treatments resulted in ablations with varying degrees of thermal injury within the H-FIRE ablation zone. Production of 4-cm ablations is possible using a 4AA.

    View details for DOI 10.1016/j.jvir.2019.05.009

    View details for Web of Science ID 000507428900025

    View details for PubMedID 31530492

  • Obtaining dual-energy computed tomography (CT) information from a single-energy CT image for quantitative imaging analysis of living subjects by using deep learning. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Zhao, W., Lv, T., Lee, R., Chen, Y., Xing, L. 2020; 25: 139–48

    Abstract

    Computed tomographic (CT) is a fundamental imaging modality to generate cross-sectional views of internal anatomy in a living subject or interrogate material composition of an object, and it has been routinely used in clinical applications and nondestructive testing. In a standard CT image, pixels having the same Hounsfield Units (HU) can correspond to different materials, and it is therefore challenging to differentiate and quantify materials. Dual-energy CT (DECT) is desirable to differentiate multiple materials, but the costly DECT scanners are not widely available as single-energy CT (SECT) scanners. Recent advancement in deep learning provides an enabling tool to map images between different modalities with incorporated prior knowledge. Here we develop a deep learning approach to perform DECT imaging by using the standard SECT data. The end point of the approach is a model capable of providing the high-energy CT image for a given input low-energy CT image. The feasibility of the deep learning-based DECT imaging method using a SECT data is demonstrated using contrast-enhanced DECT images and evaluated using clinical relevant indexes. This work opens new opportunities for numerous DECT clinical applications with a standard SECT data and may enable significantly simplified hardware design, scanning dose and image cost reduction for future DECT systems.

    View details for PubMedID 31797593

  • Deriving new soft tissue contrasts from conventional MR images using deep learning. Magnetic resonance imaging Wu, Y. n., Li, D. n., Xing, L. n., Gold, G. n. 2020

    Abstract

    Versatile soft tissue contrast in magnetic resonance imaging is a unique advantage of the imaging modality. However, the versatility is not fully exploited. In this study, we propose a deep learning-based strategy to derive more soft tissue contrasts from conventional MR images obtained in standard clinical MRI. Two types of experiments are performed. First, MR images corresponding to different pulse sequences are predicted from one or more images already acquired. As an example, we predict T1ρ weighted knee image from T2 weighted image and/or T1 weighted image. Furthermore, we estimate images corresponding to alternative imaging parameter values. In a representative case, variable flip angle images are predicted from a single T1 weighted image, whose accuracy is further validated in quantitative T1 map subsequently derived. To accomplish these tasks, images are retrospectively collected from 56 subjects, and self-attention convolutional neural network models are trained using 1104 knee images from 46 subjects and tested using 240 images from 10 other subjects. High accuracy has been achieved in resultant qualitative images as well as quantitative T1 maps. The proposed deep learning method can be broadly applied to obtain more versatile soft tissue contrasts without additional scans or used to normalize MR data that were inconsistently acquired for quantitative analysis.

    View details for DOI 10.1016/j.mri.2020.09.014

    View details for PubMedID 32956805

  • Deep learning-enhanced LED-based photoacoustic imaging Singh, M., Sivasubramanian, K., Sato, N., Ichihashi, F., Sankai, Y., Xing, L., Oraevsky, A. A., Wang, L. V. SPIE-INT SOC OPTICAL ENGINEERING. 2020

    View details for DOI 10.1117/12.2545654

    View details for Web of Science ID 000558347500030

  • Food based contrast agents for photoacoustic imaging Sivasubramanian, K., Kim, J., Cheng, K., Kim, C., Xing, L., Oraevsky, A. A., Wang, L. V. SPIE-INT SOC OPTICAL ENGINEERING. 2020

    View details for DOI 10.1117/12.2547404

    View details for Web of Science ID 000558347500034

  • Fast spot-scanning proton dose calculation method with uncertainty quantification using a three-dimensional convolutional neural network. Physics in medicine and biology Nomura, Y. n., Wang, J. n., Shirato, H. n., Shimizu, S. n., Xing, L. n. 2020

    Abstract

    This study proposes a near-real-time spot-scanning proton dose calculation method with probabilistic uncertainty estimation using a three-dimensional convolutional neural network (3D-CNN).CT images and clinical target volume contours of 215 head and neck cancer patients were collected from a public database. 1,484 and 488 plans were extracted for training and testing the 3D-CNN model, respectively. Spot beam data and single-field uniform dose (SFUD) labels were calculated for each plan using an open-source dose calculation toolkit. Variable spot data were converted into a fixed-size volume hereby called a "peak map" (PM). 300 epochs of end-to-end training was implemented using sets of stopping power ratio and PM as input. Moreover, transfer learning techniques were used to adjust the trained model to SFUD doses calculated with different beam parameters and calculation algorithm using only 7.95% of training data used for the base model. Finally, accuracy of the 3D-CNN-calculated doses and model uncertainty was reviewed with several evaluation metrics.The 3D-CNN model calculates 3D proton dose distributions accurately with a mean absolute error of 0.778 cGyE. The predicted uncertainty is correlated with dose errors at high contrast edges. Averaged Sørensen-Dice similarity coefficients between binarized outputs and ground truths are mostly above 80%. Once the 3D-CNN model was well-trained, it can be efficiently fine-tuned for different proton doses by transfer learning techniques. Inference time for calculating one dose distribution is around 0.8 seconds for a plan using 1,500 spot beams with a consumer grade GPU.A novel spot-scanning proton dose calculation method using 3D-CNN was developed. The 3D-CNN model is able to calculate 3D doses and uncertainty with any SFUD spot data and beam irradiation angles. Our proposed method should be readily extendable to other setups and plans and be useful for dose verification, image-guided proton therapy, or other applications.

    View details for DOI 10.1088/1361-6560/aba164

    View details for PubMedID 32604078

  • Verification of the machine delivery parameters of treatment plan via deep learning. Physics in medicine and biology Fan, J. n., Xing, L. n., Ma, M. n., Hu, W. n., Yang, Y. n. 2020

    Abstract

    We developed a generative adversarial network (GAN)-based deep learning approach to estimate the multileaf collimator (MLC) aperture and corresponding monitor units (MUs) from a given three dimensional (3D) dose distribution. The proposed design of adversari-al network, which integrates a residual block into pix2pix framework, jointly trains a "U-Net"-like architecture as generator and a convolutional "PatchGAN" classifier as dis-criminator. 199 patients, including nasopharyngeal, lung and rectum, treated with intensi-ty modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) tech-niques were utilized to train the network. Additional 47 patients were used to test the prediction accuracy of the proposed deep learning model. The Dice similarity coefficient (DSC) was calculated to evaluate the similarity between the MLC aperture shapes ob-tained from the treatment planning system (TPS) and the deep learning prediction. The average and standard deviation of the bias between the TPS generated MUs and predicted MUs were calculated to evaluate the MU prediction accuracy. Additionally, the differences between TPS and deep learning-predicted MLC leaf positions were compared. The average and standard deviation of DSC was 0.94 ± 0.043 for 47 testing patients. The average deviation of predicted MUs from the planned MUs normalized to each beam or arc was within 2% for all the testing patients. The average deviation of the predicted MLC leaf positions was around one pixel for all the testing patients. Our results demonstrated the feasibility and reliability of the proposed approach. The proposed technique has strong potential to improve the efficiency and accuracy of patient plan quality assurance (QA) process.

    View details for DOI 10.1088/1361-6560/aba165

    View details for PubMedID 32604082

  • A Deep Learning Framework for Prostate Localization in Cone Beam CT Guided Radiotherapy. Medical physics Liang, X. n., Zhao, W. n., Hristov, D. H., Buyyounouski, M. K., Hancock, S. L., Bagshaw, H. n., Zhang, Q. n., Xie, Y. n., Xing, L. n. 2020

    Abstract

    To develop a deep learning-based model for prostate planning target volume (PTV) localization on cone-beam CT (CBCT) to improve the workflow of CBCT-guided patient setup.A two-step task-based residual network (T2 RN) is proposed to automatically identify inherent landmarks in prostate PTV. The input to the T2 RN is the pre-treatment CBCT images of the patient, and the output is the deep learning-identified landmarks in the PTV. To ensure robust PTV localization, the T2 RN model is trained by using over thousand sets of CT images with labeled landmarks, each of the CTs corresponds to a different scenario of patient position and/or anatomy distribution generated by synthetically changing the planning CT (pCT) image. The changes, including translation, rotation, and deformation, represent vast possible clinical situations of anatomy variations during a course of radiation therapy (RT). The trained patient-specific T2 RN model is tested by using 240 CBCTs from six patients. The testing CBCTs consists of 120 original CBCTs and 120 synthetic CBCTs. The synthetic CBCTs are generated by applying rotation/translation transformations to each of the original CBCT.The systematic/random setup errors between the model prediction and the reference are found to be less than 0.25/2.46 mm and 0.14/1.41° in translation and rotation dimensions, respectively. Pearson's correlation coefficient between model prediction and the reference is higher than 0.94 in translation and rotation dimensions. The Bland-Altman plots show good agreement between the two techniques.A novel T2 RN deep learning technique is established to localize the prostate PTV for RT patient setup. Our results show that highly accurate marker-less prostate setup is achievable by leveraging the state-of-the-art deep learning strategy.

    View details for DOI 10.1002/mp.14355

    View details for PubMedID 32583418

  • High-speed X-ray-induced luminescence computed tomography. Journal of biophotonics Dai, X. n., Cheng, K. n., Zhao, W. n., Xing, L. n. 2020

    Abstract

    X-ray-induced luminescence computed tomography (XLCT) is an emerging molecular imaging. Challenges in improving spatial resolution and reducing the scan time in a whole-body field of view (FOV) still remain for practical in vivo applications. In this study, we present a novel XLCT technique capable of obtaining three-dimensional (3D) images from a single snapshot. Specifically, a customed two-planar-mirror component is integrated into a cone beam XLCT imaging system to obtain multiple optical views of an object simultaneously. Furthermore, a compressive sensing based algorithm is adopted to improve the efficiency of 3D XLCT image reconstruction. Numerical simulations and experiments were conducted to validate the single snapshot X-ray-induced luminescence computed tomography (SS-XLCT). The results show that the 3D distribution of the nanophosphor targets can be visualized much faster than conventional cone beam XLCT imaging method that was used in our comparisons while maintaining comparable spatial resolution as in conventional XLCT imaging. SS-XLCT has the potential to harness the power of XLCT for rapid whole-body in vivo molecular imaging of small animals. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/jbio.202000066

    View details for PubMedID 32445254

  • Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy. Medical physics Ibragimov, B. n., Toesca, D. A., Chang, D. T., Yuan, Y. n., Koong, A. C., Xing, L. n., Vogelius, I. R. 2020

    Abstract

    Radiation therapy (RT) is prescribed for curative and palliative treatment for around 50% of patients with solid tumors. Radiation-induced toxicities of healthy organs accompany many RTs and represent one of the main limiting factors during dose delivery. The existing RT planning solutions generally discard spatial dose distribution information and lose the ability to recognize radiosensitive regions of healthy organs potentially linked to toxicity manifestation. This study proposes a universal deep learning-based algorithm for recognitions of consistent dose patterns and generation of toxicity risk maps for the abdominal area.We investigated whether convolutional neural networks (CNNs) can automatically associate abdominal computed tomography (CT) images and RT dose plans with post-RT toxicities without being provided segmentation of abdominal organs. The CNNs were also applied to study RT plans, where doses at specific anatomical regions were reduced/increased, with the aim to pinpoint critical regions sparing of which significantly reduces toxicity risks. The obtained risk maps were computed for individual anatomical regions inside the liver and statistically compared to the existing clinical studies.A database of 122 liver stereotactic body RT (SBRT) executed at Stanford Hospital from July 2004 and November 2015 was assembled. All patients treated for primary liver cancer, mainly hepatocellular carcinoma and cholangiocarcinoma, with complete follow-ups were extracted from the database. The SBRT treatment doses ranged from 26 to 50 Gy delivered in 1-5 fractions for primary liver cancer. The patients were followed up for 1-68 months depending on the survival time. The CNNs were trained to recognize acute and late grade 3+ biliary stricture/obstruction, hepatic failure or decompensation, hepatobiliary infection, liver function test (LFT) elevation or/and portal vein thrombosis, named for convenience hepatobiliary (HB) toxicities. The toxicity prediction accuracy was of 0.73 measured in terms of the area under the receiving operator characteristic curve. Significantly higher risk scores (p < 0.05) of HB toxicity manifestation were associated with irradiation for the hepatobiliary tract in comparison to the risk scores for liver segments I-VIII and portal vein. This observation is in strong agreement with anatomical and clinical expectations.In this work, we proposed and validated a universal deep learning-based solution for the identification of radiosensitive anatomical regions. Without any prior anatomical knowledge, CNNs automatically recognized the importance of hepatobiliary tract sparing during liver SBRT.

    View details for DOI 10.1002/mp.14235

    View details for PubMedID 32406531

  • Restarted primal-dual Newton conjugate gradient method for enhanced spatial resolution of reconstructed cone-beam X-ray luminescence computed tomography images. Physics in medicine and biology Gao, P. n., Cheng, K. n., Schueler, E. n., Jia, M. n., Zhao, W. n., Xing, L. n. 2020

    Abstract

    Cone-beam X-ray luminescence computed tomography (CB-XLCT) has been proposed as a promising imaging tool, which enables three-dimensional imaging of the distribution of nanophosphors (NPs) in small animals. However, the reconstruction performance is usually unsatisfactory in terms of spatial resolution due to the ill-posedness of CB-XLCT inverse problem. To alleviate this problem and to achieve high spatial resolution, a reconstruction method consisting of inner and outer iterations based on a restarted strategy is proposed. In this method, the primal-dual Newton Conjugate Gradient method (pdNCG) is adopted in the inner iterations to get fast reconstruction, which is used for resetting the permission region and increase the convergence speed of the outer iteration. To assess the performance of the method, numerical simulation and physical phantom experiments were conducted with a CB-XLCT system. The results demonstrate that compared with conventional reconstruction methods, the proposed re-pdNCG method can accurately and efficiently resolve the adjacent NPs with the least relative error.

    View details for DOI 10.1088/1361-6560/ab87fb

    View details for PubMedID 32268318

  • Wireless Capsule Endoscopy: A New Tool for Cancer Screening in the Colon With Deep-Learning-Based Polyp Recognition PROCEEDINGS OF THE IEEE Jia, X., Xing, X., Yuan, Y., Xing, L., Meng, M. 2020; 108 (1): 178–97
  • Beam data modeling of linear accelerators (linacs) through machine learning and its potential applications in fast and robust linac commissioning and quality assurance. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Zhao, W. n., Patil, I. n., Han, B. n., Yang, Y. n., Xing, L. n., Schüler, E. n. 2020

    Abstract

    To propose a novel machine learning-based method for reliable and accurate modeling of linac beam data applicable to the processes of linac commissioning and QA.We hypothesize that the beam data is a function of inherent linac features and percentage depth doses (PDDs) and profiles of different field sizes are correlated with each other. The correlation is formulated as a multivariable regression problem using a machine learning framework. Varian TrueBeam beam data sets (n=43) acquired from multiple institutions were used to evaluate the framework. The data sets included PDDs and profiles across different energies and field sizes. A multivariate regression model was trained for prediction of beam specific PDDs and profiles of different field sizes using a 10x10cm2 field as input.Predictions of PDDs were achieved with a mean absolute percent relative error (%RE) of 0.19-0.35% across the different beam energies investigated. The maximum mean absolute %RE was 0.93%. For profile prediction, the mean absolute %RE was 0.66-0.93% with a maximum absolute %RE of 3.76%. The largest uncertainties in the PDD and profile predictions were found at the build-up region and at the field penumbra, respectively. The prediction accuracy increased with the number of training sets up to around 20 training sets.Through this novel machine learning-based method we have shown accurate and reproducible generation of beam data for linac commissioning for routine radiation therapy. This method has the potential to simplify the linac commissioning procedure, save time and manpower while increasing the accuracy of the commissioning process.

    View details for DOI 10.1016/j.radonc.2020.09.057

    View details for PubMedID 33039427

  • Deciphering tissue relaxation parameters from a single MR image using deep learning SPIE Medical Imaging Wu, Y., Ma, Y., Du, J., Xing, L. 2020

    View details for DOI 10.1117/12.2546025

  • Superpixel Region Merging based on Deep Network for Medical Image Segmentation ACM Transactions on Intelligent Systems and Technology Liu, H., Wang, H., Wu, Y., Xing, L. 2020; 11 (4)

    View details for DOI 10.1145/3386090

  • Technical Note: Evaluation of Audiovisual Biofeedback Smartphone Application for Respiratory Monitoring in Radiation Oncology. Medical physics Capaldi, D. P., Nano, T. F., Zhang, H. n., Skinner, L. B., Xing, L. n. 2020

    Abstract

    Radiation dose delivered to targets located near the upper abdomen or thorax are significantly affected by respiratory motion, necessitating large margins, limiting dose escalation. Surrogate motion management devices, such as the Real-time Position Management (RPM™) system (Varian Medical Systems, Palo Alto, CA), are commonly used to improve normal tissue sparing. Alternative to current solutions, we have developed and evaluated the feasibility of a real-time position management system that leverages the motion data from the onboard hardware of Apple iOS devices to provide patients with visual coaching with the potential to improve the reproducibility of breathing as well as improve patient compliance and reduce treatment delivery time.The iOS application, coined the Instant Respiratory Feedback (IRF) system, was developed in Swift (Apple Inc., Cupertino, CA) using the Core-Motion library and implemented on an Apple iPhone® devices. Operation requires an iPhone®, a 3D printed arm, and a radiolucent projector screen system for feedback. Direct comparison between IRF, which leverages sensor fusion data from the iPhone®, and RPM™, an optical based system, was performed on multiple respiratory motion phantoms and volunteers. The IRF system and RPM™ camera tracking marker were placed on the same location allowing for simultaneous data acquisition. The IRF surrogate measurement of displacement was compared to the signal trace acquired using RPM™ with univariate linear regressions and Bland-Altman analysis.Periodic motion shows excellent agreement between both systems, and subject motion shows good agreement during regular and irregular breathing motion. Comparison of IRF and RPM™ show very similar signal traces that were significantly related across all phantoms, including those motion with different amplitude and frequency, and subjects' waveforms (all r>0.9, p<0.0001). We demonstrate the feasibility of four-dimensional cone beam computed tomography reconstruction using IRF can acquire dynamic phantom images with similar image quality as RPM™.Feasibility of an iOS application to provide real-time respiratory motion is demonstrated. This system generated comparable signal traces to a commercially available system and offers an alternative method to monitor respiratory motion.

    View details for DOI 10.1002/mp.14484

    View details for PubMedID 32969075

  • Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation. IEEE transactions on neural networks and learning systems Li, X. n., Yu, L. n., Chen, H. n., Fu, C. W., Xing, L. n., Heng, P. A. 2020; PP

    Abstract

    A common shortfall of supervised deep learning for medical imaging is the lack of labeled data, which is often expensive and time consuming to collect. This article presents a new semisupervised method for medical image segmentation, where the network is optimized by a weighted combination of a common supervised loss only for the labeled inputs and a regularization loss for both the labeled and unlabeled data. To utilize the unlabeled data, our method encourages consistent predictions of the network-in-training for the same input under different perturbations. With the semisupervised segmentation tasks, we introduce a transformation-consistent strategy in the self-ensembling model to enhance the regularization effect for pixel-level predictions. To further improve the regularization effects, we extend the transformation in a more generalized form including scaling and optimize the consistency loss with a teacher model, which is an averaging of the student model weights. We extensively validated the proposed semisupervised method on three typical yet challenging medical image segmentation tasks: 1) skin lesion segmentation from dermoscopy images in the International Skin Imaging Collaboration (ISIC) 2017 data set; 2) optic disk (OD) segmentation from fundus images in the Retinal Fundus Glaucoma Challenge (REFUGE) data set; and 3) liver segmentation from volumetric CT scans in the Liver Tumor Segmentation Challenge (LiTS) data set. Compared with state-of-the-art, our method shows superior performance on the challenging 2-D/3-D medical images, demonstrating the effectiveness of our semisupervised method for medical image segmentation.

    View details for DOI 10.1109/TNNLS.2020.2995319

    View details for PubMedID 32479407

  • Automatic intraprostatic lesion segmentation in multiparametric magnetic resonance images with proposed multiple branch Unet. Medical physics Chen, Y. n., Xing, L. n., Yu, L. n., Bagshaw, H. P., Buyyounouski, M. K., Han, B. n. 2020

    Abstract

    Contouring intraprostatic lesions is a prerequisite for dose-escalating these lesions in radiotherapy to improve the local cancer control. In this study, a deep learning-based approach was developed for automatic intraprostatic lesion segmentation in multiparametric magnetic resonance imaging (mpMRI) images contributing to the clinical practice.mpMRI images from 136 patient cases were collected from our institution, and all these cases contained suspicious lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 4. The contours of the lesion and prostate were manually created on axial T2-weighted (T2W), apparent diffusion coefficient (ADC) and high b-value diffusion-weighted imaging (DWI) images to provide the ground truth data. Then a multiple branch UNet (MB-UNet) was proposed for the segmentation of indistinct target in multi-modality MRI images. An encoder module was designed with three branches for the three MRI modalities separately, to fully extract the high-level features provided by different MRI modalities; an input module was added by using three sub-branches for three consecutive image slices, to consider the contour consistency among different image slices; deep supervision strategy was also integrated into the network to speed up the convergency of the network and improve the performance. The probability maps of the background, normal prostate and lesion were output by the network to generate the segmentation of the lesion, and the performance was evaluated using the Dice similarity coefficient (DSC) as the main metric.A total of 162 lesions were contoured on 652 image slices, with 119 lesions in the peripheral zone, 38 in the transition zone, 4 in the central zone and 1 in the anterior fibromuscular stroma. All prostates were also contoured on 1,264 image slices. As for the segmentation of lesions in the testing set, MB-UNet achieved a per case DSC of 0.6333, specificity of 0.9993, sensitivity of 0.7056; and global DSC of 0.7205, specificity of 0.9993, sensitivity of 0.7409. All the three deep learning strategies adopted in this study contributed to the performance promotion of the MB-UNet. And missing the DWI modality would degrade the segmentation performance more markedly compared with the other two modalities.A deep learning-based approach with proposed MB-UNet was developed to automatically segment suspicious lesions in mpMRI images. This study makes it feasible to adopt boosting intraprostatic lesions in clinical practice to achieve better outcomes.

    View details for DOI 10.1002/mp.14517

    View details for PubMedID 33012016

  • Technical Note: Machine learning approaches for range and dose verification in proton therapy using proton-induced positron emitters MEDICAL PHYSICS Li, Z., Wang, Y., Yu, Y., Fan, K., Xing, L., Peng, H. 2019; 46 (12): 5748–57

    Abstract

    Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution and the activity distribution of positron emitters in addition to the presence of noise, machine learning approaches were proposed to establish their relationship.Simulations were carried out with a spot-scanning proton system using GATE-8.0 and Geant4-10.3 toolkit with a computed tomography (CT)-based patient phantom. The one-dimensional (1D) distributions of positron emitters and radiation dose were obtained. A feedforward neural network classification model comprising two hidden layers, was developed to estimate whether the range is within a preset threshold. A recurrent neural network (RNN) regression model comprising three layers and ten neurons in each hidden layer was developed to estimate dose distribution. The performance was quantitatively studied in terms of mean squared error (MSE) and mean absolute error (MAE) under different signal-to-noise ratio (SNR) values.The feasibility of proton range and dose verification using the proposed neural network framework was demonstrated. The feedforward NN model achieves high classification accuracy close to 100% for individual classes without bias. The RNN model is able to accurately predict the 1D dose distribution for different energies and irradiation positions. When the SNR of the input activity profiles is above 4, the framework is able to predict with an MAE of ~0.60 mm and an MSE of ~0.066. Moreover, the model demonstrates a good capability of generalization.The RNN model is found to be effective in identifying the relationship between the distributions of dose and positron emitters. The machine learning-based framework and RNN models may be a useful tool to allow for accurate online range and dose verification based on proton-induced positron emitters.

    View details for DOI 10.1002/mp.13827

    View details for Web of Science ID 000516580200040

    View details for PubMedID 31529506

  • Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning. Nature biomedical engineering Shen, L., Zhao, W., Xing, L. 2019

    Abstract

    Tomographic imaging using penetrating waves generates cross-sectional views of the internal anatomy of a living subject. For artefact-free volumetric imaging, projection views from a large number of angular positions are required. Here we show that a deep-learning model trained to map projection radiographs of a patient to the corresponding 3D anatomy can subsequently generate volumetric tomographic X-ray images of the patient from a single projection view. We demonstrate the feasibility of the approach with upper-abdomen, lung, and head-and-neck computed tomography scans from three patients. Volumetric reconstruction via deep learning could be useful in image-guided interventional procedures such as radiation therapy and needle biopsy, and might help simplify the hardware of tomographic imaging systems.

    View details for DOI 10.1038/s41551-019-0466-4

    View details for PubMedID 31659306

  • X-ray-induced shortwave infrared luminescence computed tomography OPTICS LETTERS Dai, X., Cheng, K., Zhao, W., Xing, L. 2019; 44 (19): 4769–72

    Abstract

    X-ray luminescence computed tomography (XLCT) based on x-ray-excitable nanophosphors has been proposed as a new modality for molecular imaging. The technique has two main advantages compared to other modalities. First, autofluorescence, which is problematic for fluorescence imaging, can be substantially reduced. Second, deep-tissue in vivo imaging with high optical contrast and spatial resolution becomes achievable. Here, we extend the novel XLCT modality from the visible or infrared region to a shortwave infrared wavelength by developing an x-ray-induced shortwave infrared luminescence computed tomography (SWIR-XLCT). For this application, rare-earth nanophosphors (RENPs) were synthesized as core/shell structures consisting of a Ho-doped NaYbF4 core surrounded by a NaYF4 shell that emit light efficiently in the shortwave infrared spectral region under x-ray excitation. Through numerical simulations and phantom experiments, we showed the feasibility of SWIR-XLCT and demonstrated its potential for x-ray luminescence imaging with high spatial resolution and deep depth.

    View details for DOI 10.1364/OL.44.004769

    View details for Web of Science ID 000488503500037

    View details for PubMedID 31568438

  • Task Group 174 Report: Utilization of [F-18]Fluorodeoxyglucose Positron Emission Tomography ([F-18]FDG-PET) in Radiation Therapy MEDICAL PHYSICS Das, S. K., McGurk, R., Miften, M., Mutic, S., Bowsher, J., Bayouth, J., Erdi, Y., Mawlawi, O., Boellaard, R., Bowen, S. R., Xing, L., Bradley, J., Schoder, H., Yin, F., Sullivan, D. C., Kinahan, P. 2019; 46 (10): E706–E725

    Abstract

    The use of positron emission tomography (PET) in radiation therapy (RT) is rapidly increasing in the areas of staging, segmentation, treatment planning, and response assessment. The most common radiotracer is 18 F-fluorodeoxyglucose ([18 F]FDG), a glucose analog with demonstrated efficacy in cancer diagnosis and staging. However, diagnosis and RT planning are different endeavors with unique requirements, and very little literature is available for guiding physicists and clinicians in the utilization of [18 F]FDG-PET in RT. The two goals of this report are to educate and provide recommendations. The report provides background and education on current PET imaging systems, PET tracers, intensity quantification, and current utilization in RT (staging, segmentation, image registration, treatment planning, and therapy response assessment). Recommendations are provided on acceptance testing, annual and monthly quality assurance, scanning protocols to ensure consistency between interpatient scans and intrapatient longitudinal scans, reporting of patient and scan parameters in literature, requirements for incorporation of [18 F]FDG-PET in treatment planning systems, and image registration. The recommendations provided here are minimum requirements and are not meant to cover all aspects of the use of [18 F]FDG-PET for RT.

    View details for DOI 10.1002/mp.13676

    View details for Web of Science ID 000485848700001

    View details for PubMedID 31230358

  • In Vivo Translation of the CIRPI System: Revealing Molecular Pathology of Rabbit Aortic Atherosclerotic Plaques JOURNAL OF NUCLEAR MEDICINE Zaman, R. T., Yousefi, S., Chibana, H., Ikeno, F., Long, S. R., Gambhir, S. S., Chin, F. T., McConnell, M. V., Xing, L., Yeung, A. 2019; 60 (9): 1308–16
  • Range and dose verification in proton therapy using proton-induced positron emitters and recurrent neural networks (RNNs) PHYSICS IN MEDICINE AND BIOLOGY Liu, C., Li, Z., Hu, W., Xing, L., Peng, H. 2019; 64 (17): 175009

    Abstract

    Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution and the activity distribution of positron emitters, we aim to establish their relationship using recurrent neural network models (LSTM, BiLSTM, GRU, BiGRU and Seq2seq). Simulations were carried out with a spot-scanning proton system using Geant4-10.3 toolkit and a CT-based patient phantom. The 1D distributions of positron emitters and radiation dose were obtained. Training data were modeled for different beam energy, irradiation positions and counting statistics. The prediction accuracy of range and dose were quantitatively studied. The impact of including anatomical information (HU values in CT images) on the prediction performance was investigated. The BiGRU demonstrates the most stable and accurate performance with good capability of generalization, especially with the inclusion of anatomical information. When the signal-to-noise ratio (SNR) of the 1D activity profiles is about 3, the range accuracy can be within 0.5 mm and the dose accuracy close to the peak region is  <5% (relative uncertainty between prediction and raw input for all datasets). The feasibility of proton range and dose verification using the RNN-based framework was demonstrated. The RNN-based framework promises to provide a reliable and effective way for online monitoring, quality assurance and ultimately allows for adaptive proton therapy.

    View details for DOI 10.1088/1361-6560/ab3564

    View details for Web of Science ID 000484508700002

    View details for PubMedID 31342940

  • Potential of Gd-EOB-DTPA as an imaging biomarker for liver injury estimation after radiation therapy HEPATOBILIARY & PANCREATIC DISEASES INTERNATIONAL Sun, X., Jiang, X., Kuang, Y., Xing, L., Bu, L., Yuan, S., Yu, J., Zheng, S. 2019; 18 (4): 354–59

    Abstract

    Hepatic radiation injury severely restricts irradiation treatment for liver carcinoma. The purpose of this study was to investigate the clinical application of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI (EOB-MRI) in the assessment of liver function after external radiation therapy and to determine the relationship between focal liver reaction (FLR) and liver function.A total of 47 patients with liver malignancies who underwent external beam radiation therapy were enrolled. EOB-MRI was performed on each patient at approximately one month post-radiotherapy. The hepatobiliary (HPB) phase images from EOB-MRI were fused with the planning CT images, and the isodose lines from the patients' treatment plans were overlaid onto the fused images. The correlation of the EOB-MR image intensity distribution with the isodose lines was studied. We also compared liver function in patients between pre-treatment and post-treatment.Decreased uptake of Gd-EOB-DTPA, which was manifested by well-demarcated focal hypointensity of the liver parenchyma or FLR to high-dose radiation, was observed in the irradiated areas of 38 patients. The radiotherapy isodose line of decreased uptake area of Gd-EOB-DTPA was 30-46 Gy. The median corresponding dose curve of FLR was 34.4 Gy. Nine patients showed the absence of decreased uptake area of Gd-EOB-DTPA in the irradiated areas. Compared to the 38 patients with the presence of decreased uptake area of Gd-EOB-DTPA, 9 patients with the absence of decreased uptake area of Gd-EOB-DTPA showed significant higher levels of total bile acid, total bilirubin, direct bilirubin and alpha-fetoprotein (P < 0.05). There were no significant differences in alanine transaminase, aspartate aminotransferase, gamma-glutamyl transpeptidase or albumin levels between the two groups (P > 0.05).Visible uptake of Gd-EOB-DTPA by the liver parenchyma was significantly associated with liver function parameters. EOB-MRI can be a valuable imaging biomarker for the assessment of liver parenchyma function outside of radiation area.

    View details for DOI 10.1016/j.hbpd.2019.05.005

    View details for Web of Science ID 000481407500010

    View details for PubMedID 31221569

  • Incorporating imaging information from deep neural network layers into image guided radiation therapy (IGRT). Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Zhao, W., Han, B., Yang, Y., Buyyounouski, M., Hancock, S. L., Bagshaw, H., Xing, L. 2019; 140: 167–74

    Abstract

    BACKGROUND AND PURPOSE: To investigate a novel markerless prostate localization strategy using a pre-trained deep learning model to interpret routine projection kilovoltage (kV) X-ray images in image-guided radiation therapy (IGRT).MATERIALS AND METHODS: We developed a personalized region-based convolutional neural network to localize the prostate treatment target without implanted fiducials. To train the deep neural network (DNN), we used the patient's planning computed tomography (pCT) images with pre-delineated prostate target to generate a large amount of synthetic kV projection X-ray images in the geometry of onboard imager (OBI) system. The DNN model was evaluated by retrospectively studying 10 patients who underwent prostate IGRT. Three out of the ten patients who had implanted fiducials and the fiducials' positions in the OBI images acquired for treatment setup were examined to show the potential of the proposed method for prostate IGRT. Statistical analysis using Lin's concordance correlation coefficient was calculated to assess the results along with the difference between the digitally reconstructed radiographs (DRR) derived and DNN predicted locations of the prostate.RESULTS: Differences between the predicted target positions using DNN and their actual positions are (mean ± standard deviation) 1.58 ± 0.43 mm, 1.64 ± 0.43 mm, and 1.67 ± 0.36 mm in anterior-posterior, lateral, and oblique directions, respectively. Prostate position identified on the OBI kV images is also found to be consistent with that derived from the implanted fiducials.CONCLUSIONS: Highly accurate, markerless prostate localization based on deep learning is achievable. The proposed method is useful for daily patient positioning and real-time target tracking during prostate radiotherapy.

    View details for DOI 10.1016/j.radonc.2019.06.027

    View details for PubMedID 31302347

  • A radiomics approach based on support vector machine using MR images for preoperative lymph node status evaluation in intrahepatic cholangiocarcinoma. Theranostics Xu, L., Yang, P., Liang, W., Liu, W., Wang, W., Luo, C., Wang, J., Peng, Z., Xing, L., Huang, M., Zheng, S., Niu, T. 2019; 9 (18): 5374-5385

    Abstract

    Purpose: Accurate lymph node (LN) status evaluation for intrahepatic cholangiocarcinoma (ICC) patients is essential for surgical planning. This study aimed to develop and validate a prediction model for preoperative LN status evaluation in ICC patients. Methods and Materials: A group of 106 ICC patients, who were diagnosed between April 2011 and February 2016, was used for prediction model training. Image features were extracted from T1-weighted contrast-enhanced MR images. A support vector machine (SVM) model was built by using the most LN status-related features, which were selected using the maximum relevance minimum redundancy (mRMR) algorithm. The mRMR method ranked each feature according to its relevance to the LN status and redundancy with other features. An SVM score was calculated for each patient to reflect the LN metastasis (LNM) probability from the SVM model. Finally, a combination nomogram was constructed by incorporating the SVM score and clinical features. An independent group of 42 patients who were diagnosed from March 2016 to November 2017 was used to validate the prediction models. The model performances were evaluated on discrimination, calibration, and clinical utility. Results: The SVM model was constructed based on five selected image features. Significant differences were found between patients with LNM and non-LNM in SVM scores in both groups (the training group: 0.5466 (interquartile range (IQR), 0.4059-0.6985) vs. 0.3226 (IQR, 0.0527-0.4659), P<0.0001; the validation group: 0.5831 (IQR, 0.3641-0.8162) vs. 0.3101 (IQR, 0.1029-0.4661), P=0.0015). The combination nomogram based on the SVM score, the CA 19-9 level, and the MR-reported LNM factor showed better discrimination in separating patients with LNM and non-LNM, comparing to the SVM model alone (AUC: the training group: 0.842 vs. 0.788; the validation group: 0.870 vs. 0.787). Favorable clinical utility was observed using the decision curve analysis for the nomogram. Conclusion: The nomogram, incorporating the SVM score, CA 19-9 level and the MR-reported LNM factor, provided an individualized LN status evaluation and helped clinicians guide the surgical decisions.

    View details for DOI 10.7150/thno.34149

    View details for PubMedID 31410221

    View details for PubMedCentralID PMC6691572

  • Projection-domain scatter correction for cone beam computed tomography using a residual convolutional neural network MEDICAL PHYSICS Nomura, Y., Xu, Q., Shirato, H., Shimizu, S., Xing, L. 2019; 46 (7): 3142–55

    View details for DOI 10.1002/mp.13583

    View details for Web of Science ID 000475671900020

  • Self-Attention Convolutional Neural Network for Improved MR Image Reconstruction. Information sciences Wu, Y., Ma, Y., Liu, J., Du, J., Xing, L. 2019; 490: 317-328

    Abstract

    MRI is an advanced imaging modality with the unfortunate disadvantage of long data acquisition time. To accelerate MR image acquisition while maintaining high image quality, extensive investigations have been conducted on image reconstruction of sparsely sampled MRI. Recently, deep convolutional neural networks have achieved promising results, yet the local receptive field in convolution neural network raises concerns regarding signal synthesis and artifact compensation. In this study, we proposed a deep learning-based reconstruction framework to provide improved image fidelity for accelerated MRI. We integrated the self-attention mechanism, which captured long-range dependencies across image regions, into a volumetric hierarchical deep residual convolutional neural network. Basically, a self-attention module was integrated to every convolutional layer, where signal at a position was calculated as a weighted sum of the features at all positions. Furthermore, relatively dense shortcut connections were employed, and data consistency was enforced. The proposed network, referred to as SAT-Net, was applied on cartilage MRI acquired using an ultrashort TE sequence and retrospectively undersampled in a pseudo-random Cartesian pattern. The network was trained using 336 three dimensional images (each containing 32 slices) and tested with 24 images that yielded improved outcome. The framework is generic and can be extended to various applications.

    View details for DOI 10.1016/j.ins.2019.03.080

    View details for PubMedID 32817993

    View details for PubMedCentralID PMC7430761

  • Real-Time Radiation Treatment Planning with Optimality Guarantees via Cluster and Bound Methods INFORMS JOURNAL ON COMPUTING Ungun, B., Xing, L., Boyd, S. 2019; 31 (3): 544–58
  • Incorporating dosimetric features into the prediction of 3D VMAT dose distributions using deep convolutional neural network PHYSICS IN MEDICINE AND BIOLOGY Ma, M., Kovalchuk, N., Buyyounouski, M. K., Xing, L., Yang, Y. 2019; 64 (12)
  • Dose Distribution Prediction in Isodose Feature-Preserving Voxelization Domain Using Deep Convolutional Neural Network. Medical physics Ma, M., Buyyounouski, M. K., Vasudevan, V., Xing, L., Yang, Y. 2019

    Abstract

    PURPOSE: To implement a framework for dose prediction using a deep convolutional neural network (CNN) based on the concept of isodose feature-preserving voxelization (IFPV) in simplifying the representation of the dose distribution.METHODS: The concept of IFPV was introduced for concise representation of a treatment plan. IFPV is a sparse voxelization scheme that partitions the voxels into subgroups according to their geometric, anatomical and dosimetric features. In this study a deep CNN was constructed to build up a dose prediction model in IFPV domain based on 60 volumetric modulated arc therapy (VMAT) treatment plans from a database of previously treated 70 prostate cancer patients. The dose prediction model learns the contour to dose relationship and predicts the dose distribution in IFPV domain given the input contours. Additional 10 independent prostate cases were selected as testing data. DVH comparison, dose difference maps and residual analysis with the sum of absolute residual (SAR) were used to evaluate the performance of the proposed method.RESULTS: The proposed IFPV-based method achieved good prediction performance in terms of DVH comparison and dose difference maps. Statistical results of SARs showed that the IFPV-based method is comparable with voxel-based method even though the number of dose representation points used in the IFPV-based method was substantially reduced. The proposed approach achieved mean SARs of 0.029 ± 0.020 and 0.077 ±0.030 for bladder and rectum, respectively, compared with mean SARs of 0.039±0.029 and 0.069±0.028 in the conventional voxel-based method.CONCLUSIONS: A novel deep CNN-based dose prediction method in IFPV domain was proposed. The proposed approach has great potential to significantly improve the efficiency of dose prediction and facilitate the treatment planning workflow. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.13618

    View details for PubMedID 31112305

  • Incorporating dosimetric features into the prediction of 3D VMAT dose distributions using deep convolutional neural network. Physics in medicine and biology Ma, M., Kovalchuk, N., Buyyounouski, M. K., Xing, L., Yang, Y. 2019

    Abstract

    An accurate prediction of achievable dose distribution on a patient specific basis would greatly improve IMRT/VMAT planning in both efficiency and quality. Recently machine learning techniques have been proposed for IMRT dose prediction based on patient's contour information from planning CT. In these existing prediction models geometric/anatomic features were learned for building the dose prediction models and few features that characterize the dosimetric properties of the patients were utilized. In this study we propose a method to incorporate the dosimetric features in the construction of a more reliable dose prediction model based on the deep convolutional neural network (CNN). In addition to the contour information, the dose distribution from a PTV-only plan (i.e., the plan with the best PTV coverage by sacrificing the OARs sparing) is also employed as the model input to build a deep learning based dose prediction model. A database of 60 volumetric modulated arc therapy (VMAT) plans for the prostate cancer patients was used for training. The trained prediction model was then tested on a cohort of 10 cases. Dose difference maps, DVHs, dosimetric endpoints and statistical analysis of the sum of absolute residuals (SARs) were used to evaluate the proposed method. Our results showed that the mean SARs for the PTV, bladder and rectum using our method were 0.007±0.003, 0.035±0.032 and 0.067±0.037 respectively, lower than the SARs obtained with the contours-based method, indicating the potential of the proposed approach in accurately predicting dose distribution.

    View details for PubMedID 31082805

  • Attention-aware fully convolutional neural network with convolutional long short-term memory network for ultrasound-based motion tracking MEDICAL PHYSICS Huang, P., Yu, G., Lu, H., Liu, D., Xing, L., Yin, Y., Kovalchuk, N., Xing, L., Li, D. 2019; 46 (5): 2275–85

    View details for DOI 10.1002/mp.13510

    View details for Web of Science ID 000467556800032

  • Attention-aware Fully Convolutional Neural Network with Convolutional Long Short-Term Memory Network for Ultrasound-Based Motion Tracking. Medical physics Huang, P., Yu, G., Lu, H., Liu, D., Xing, L., Yin, Y., Kovalchuk, N., Xing, L., Li, D. 2019

    Abstract

    PURPOSE: One of the promising options for motion management in radiation therapy (RT) is the use of Linac-compatible robotic-arm-mounted ultrasound imaging system due to its high soft tissue contrast, real-time capability, absence of ionizing radiation and low cost. The purpose of this work is to develop a novel deep learning-based real-time motion tracking strategy for ultrasound image-guided RT.METHODS: The proposed tracker combined the attention-aware Fully Convolutional Neural Network (FCNN) and the Convolutional Long Short-Term Memory network (CLSTM) that is end-to-end trainable. The glimpse sensor module was built inside the attention-aware FCNN to discard majority of background by focusing on a region containing the object of interest. FCNN extracted discriminating spatial features of glimpse to facilitate temporal modeling for CLSTM. The saliency mask computed from CLSTM refined the features particular to the tracked landmarks. Moreover, the multi-task loss strategy including bounding box loss, localization loss, saliency loss, and adaptive loss weighting term was utilized to facilitate training convergence and avoid over/under-fitting. The tracker was tested on the databases provided by MICCAI 2015 challenges, and the ground truth data was obtained with the help of brute force-based template matching technology.RESULTS: The mean tracking error of 0.97 ± 0.52 mm and maximum tracking error of 1.94 mm were observed for 85 point-landmarks across 39 ultrasound cases compared to the ground truth annotations. The tracking speed per frame per landmark with the GPU implementation ranged from 66 and 101 frames per second, which largely exceeded the ultrasound imaging rate.CONCLUSION: The results demonstrated the robustness and accuracy of the proposed deep learning-based motion estimation, despite of the existence of some known shortcomings of ultrasound imaging such as speckle noise. The tracking speed of the system was found to be remarkable, sufficiently fast for real-time applications in RT environment. The approach provides a valuable tool to guide RT treatment with beam gating or multi leaf collimator (MLC) tracking in real time. This article is protected by copyright. All rights reserved.

    View details for PubMedID 30912590

  • Design and Preliminary Experience of a Tele-Radiotherapy System for a Medical Alliance in China. Telemedicine journal and e-health : the official journal of the American Telemedicine Association Zou, L., Chen, X., Xu, C., Xing, L., Xie, Y. 2019

    Abstract

    BACKGROUND: The medical alliance and telemedicine are considered to be important means to solve the imbalance between regions and shortage of professionals and promote the homogenization of medical services. Sichuan Provincial People's Hospital Group (SPPHG) is a network of hospitals with different levels of expertise, and all the members with radiotherapy form a radiotherapy network (RTN). Addressing the inadequacy and imbalance of radiotherapy services of Sichuan Province, China, a tele-radiotherapy system for RTN-SPPHG is designed, which includes the business model and corresponding technical implementation of an information system.MATERIALS AND METHODS: In the RTN-SPPHG, a distributed remote collaboration business model is explored and a tele-radiotherapy information system is customized for this telemedicine model. Both the business model and tailored information system were evaluated in actual use.RESULTS: Based on the tele-radiotherapy system of RTN-SPPHG, multitype hospitals are linked together and serve as a whole. Through the internet, the experience of experts of Sichuan Provincial People's Hospital is effectively deployed to member hospitals at the grassroot level.CONCLUSIONS: A close-knit medical alliance based on a tele-radiotherapy system should be a way to rapidly improve radiotherapy services and promote the homogenization of service in a region.

    View details for PubMedID 30892144

  • Design and Preliminary Experience of a Tele-Radiotherapy System for a Medical Alliance in China TELEMEDICINE AND E-HEALTH Zou, L., Chen, X., Xu, C., Xing, L., Xie, Y. 2020; 26 (2): 235–43
  • Incorporating prior knowledge via volumetric deep residual network to optimize the reconstruction of sparsely sampled MRI. Magnetic resonance imaging Wu, Y., Ma, Y., Capaldi, D. P., Liu, J., Zhao, W., Du, J., Xing, L. 2019

    Abstract

    For sparse sampling that accelerates magnetic resonance (MR) image acquisition, non-linear reconstruction algorithms have been developed, which incorporated patient specific a prior information. More generic a prior information could be acquired via deep learning and utilized for image reconstruction. In this study, we developed a volumetric hierarchical deep residual convolutional neural network, referred to as T-Net, to provide a data-driven end-to-end mapping from sparsely sampled MR images to fully sampled MR images, where cartilage MR images were acquired using an Ultra-short TE sequence and retrospectively undersampled using pseudo-random Cartesian and radial acquisition schemes. The network had a hierarchical architecture that promoted the sparsity of feature maps and increased the receptive field, which were valuable for signal synthesis and artifact suppression. Relatively dense local connections and global shortcuts were established to facilitate residual learning and compensate for details lost in hierarchical processing. Additionally, volumetric processing was adopted to fully exploit spatial continuity in three-dimensional space. Data consistency was further enforced. The network was trained with 336 three-dimensional images (each consisting of 32 slices) and tested by 24 images. The incorporation of a priori information acquired via deep learning facilitated high acceleration factors (as high as 8) while maintaining high image fidelity (quantitatively evaluated using the structural similarity index measurement). The proposed T-Net had an improved performance as compared to several state-of-the-art networks.

    View details for PubMedID 30880112

  • A novel range-verification method using ionoacoustic wave generated from spherical gold markers for particle-beam therapy: a simulation study SCIENTIFIC REPORTS Takayanagi, T., Uesaka, T., Kitaoka, M., Unlu, M., Umegaki, K., Shirato, H., Xing, L., Matsuura, T. 2019; 9: 4011

    Abstract

    This study proposes a novel alternative range-verification method for proton beam with acoustic waves generated from spherical metal markers. When proton beam is incident on metal markers, most of the resulting pressure waves are confined in the markers because of the large difference in acoustic impedance between the metal and tissue. However, acoustic waves with frequency equal to marker's resonant frequency escape this confinement; the marker briefly acts as an acoustic transmitter. Herein, this phenomenon is exploited to measure the range of the proton beam. We test the proposed strategy in 3-D simulations, combining the dose calculations with modelling of acoustic-wave propagation. A spherical gold marker of 2.0 mm diameter was placed in water with a 60 MeV proton beam incident on it. We investigated the dependence of pressure waves on the width of beam pulse and marker position. At short beam pulse, specific high-frequency acoustic waves of 1.62 MHz originating from the marker were observed in wave simulations, whose amplitude correlated with the distance between the marker and Bragg peak. Results indicate that the Bragg peak position can be estimated by measuring the acoustic wave amplitudes from the marker, using a single detector properly designed for the resonance frequency.

    View details for DOI 10.1038/s41598-019-38889-w

    View details for Web of Science ID 000460627700099

    View details for PubMedID 30850625

    View details for PubMedCentralID PMC6408528

  • A convex optimization approach to radiation treatment planning with dose constraints OPTIMIZATION AND ENGINEERING Fu, A., Ungun, B., Xing, L., Boyd, S. 2019; 20 (1): 277–300
  • A convex optimization approach to radiation treatment planning with dose constraints. Optimization and engineering Fu, A., Ungun, B., Xing, L., Boyd, S. 2019; 20 (1): 277-300

    Abstract

    We present a method for handling dose constraints as part of a convex programming framework for inverse treatment planning. Our method uniformly handles mean dose, maximum dose, minimum dose, and dose-volume (i.e., percentile) constraints as part of a convex formulation. Since dose-volume constraints are non-convex, we replace them with a convex restriction. This restriction is, by definition, conservative; to mitigate its impact on the clinical objectives, we develop a two-pass planning algorithm that allows each dose-volume constraint to be met exactly on a second pass by the solver if its corresponding restriction is feasible on the first pass. In another variant, we add slack variables to each dose constraint to prevent the problem from becoming infeasible when the user specifies an incompatible set of constraints or when the constraints are made infeasible by our restriction. Finally, we introduce ConRad, a Python-embedded open-source software package for convex radiation treatment planning. ConRad implements the methods described above and allows users to construct and plan cases through a simple interface.

    View details for DOI 10.1007/s11081-018-9409-2

    View details for PubMedID 37990749

    View details for PubMedCentralID PMC10662894

  • In Vivo Translation of the CIRPI System---Revealing Molecular Pathology of Rabbit Aortic Atherosclerotic Plaques. Journal of nuclear medicine : official publication, Society of Nuclear Medicine Zaman, R., Yousufi, S., Chibana, H., Ikeno, F., Long, S. R., Gambhir, S. S., Chin, F. T., McConnell, M. V., Xing, L., Yeung, A. 2019

    Abstract

    Introduction: Thin-cap fibro atheroma (TCFA), unstable lesions in coronary artery disease (CAD), that prones to rupture resulting in substantial morbidity and mortality worldwide. However, their small size and complex morphological/biological features make early detection and risk assessment difficult. To overcome this limitation, we tested our newly developed catheter-based Circumferential-Intravascular-Radioluminescence-Photoacoustic-Imaging (CIRPI) system in vivo rabbit abdominal aorta to detect and characterize TCFA. Methods: The CIRPI system includes a novel optical probe combining circumferential radioluminescence imaging (CRI) and photoacoustic tomography (PAT). The CIRPI system was tested in rabbit abdominal aorta in vivo (WHHL, n = 5) and controls (NZW, n = 2). Rabbits were fasted for 6 hours before 5.55*107 Bq 18F-FDG was injected one hour prior to the imaging procedure. The experiment was done under anesthetic. A bare metal stent was implanted in the dorsal abdominal aorta as landmark, followed by the 7F imaging catheters that were advanced up to the proximal stent edge (PSE). Our CIRPI and clinical OCT were performed using pullback and non-occlusive flushing techniques. Results were verified with histochemical analysis. Results: Our CIRPI system successfully detected the locations and characterized both stable and vulnerable aortic plaques in vivo among all WHHL rabbits. Calcification was detected from the stable plaque (540/560 nm), whereas TCFA exhibited phospholipids/cholesterol (1040 nm, 1210 nm). These findings were verified with clinical OCT showing an area of low attenuation filled with lipids within TCFA. PAT image illustrated broken elastic fiber/collagen that could be verified with the histochemical analysis. All WHHL rabbits exhibited sparse to severe macrophages. However, 4 WHHL rabbits showed both moderate to severe level of calcifications and cholesterol clefts. However, all rabbits exhibited broken elastic fibers and collagen deposition. Control rabbits showed normal wall thickness with no presence of plaque tissue compositions. These findings were verified with the OCT and histochemical analysis. Conclusion: Our novel multi-modality hybrid system has been successfully translated to in vivo evaluation of atherosclerotic plaque structure and biology in a pre-clinical rabbit models. This proposed a paradigm shift that unites molecular and pathologic imaging technologies. Therefore, it may enhance the clinical evaluation of TCFA, as well as expand our understanding of CAD.

    View details for PubMedID 30737298

  • Controlled Nano-Bio Interface of Functional Nanoprobes for in Vivo Monitoring Enzyme Activity in Tumors ACS NANO Sun, Z., Cheng, K., Yao, Y., Wu, F., Fung, J., Chen, H., Ma, X., Tu, Y., Xing, L., Xia, L., Cheng, Z. 2019; 13 (2): 1153–67

    Abstract

    Engineering inorganic nanoparticles with a biocompatible shell to improve their physicochemical properties is a vital step in taking advantage of their superior magnetic, optical, and photothermal properties as multifunctional molecular imaging probes for disease diagnosis and treatment. The grafting/peeling-off strategy we developed for nanoparticle surface coating can fully control the targeting capability of functional nanoprobes by changing their colloidal behaviors such as diffusion and sedimentation rates at the desired sites. We demonstrated that a cleavable coating layer initially immobilized on the surface of magnetic resonance imaging probes not only makes the nanoparticles water-soluble but also can be selectively removed by specific enzymes, thereby resulting in a significant decrease of their water solubility in an enzyme-rich environment. Upon removal of surface coating, the changes in hydrodynamic size and surface charges of nanoprobes as a result of interacting with biomolecules and proteins lead to dramatic changes in their in vivo colloidal behaviors ( i. e., slow diffusion rates, tendency to aggregate and precipitate), which were quantitatively evaluated by examining changes in their hydrodynamic sizes, magnetic properties, and count rates during the size measurement. Because the retention time of nanoprobes within the tumor tissues depends on the uptake and excretion rate of the nanoprobes through the tumors, selective activation of nanoprobes by a specific enzyme resulted in much higher tumor accumulation and longer retention time within the tumors than that of the inactive nanoprobes, which passively passed through the tumors. The imaging contrast effect of tumors using activatable nanoprobes was significantly improved over using inactive probes. Therefore, the grafting/peeling-off strategy, as a general design approach for surface modification of nanoprobes, offers a promising and highly efficient way to render the nanoparticles suitable for targeted imaging of tumors.

    View details for DOI 10.1021/acsnano.8b05825

    View details for Web of Science ID 000460199400020

    View details for PubMedID 30673268

  • Dosimetric features-driven machine learning model for DVH prediction in VMAT treatment planning MEDICAL PHYSICS Ma, M., Kovalchuk, N., Buyyounouski, M. K., Xing, L., Yang, Y. 2019; 46 (2): 857–67

    View details for DOI 10.1002/mp.13334

    View details for Web of Science ID 000459616200041

  • Prostate cancer classification with multiparametric MRI transfer learning model MEDICAL PHYSICS Yuan, Y., Qin, W., Buyyounouski, M., Ibragimov, B., Hancock, S., Han, B., Xing, L. 2019; 46 (2): 756–65

    View details for DOI 10.1002/mp.13367

    View details for Web of Science ID 000459616200032

  • Tensor framelet based iterative image reconstruction algorithm for low-dose multislice helical CT PLOS ONE Nam, H., Guo, M., Yu, H., Lee, K., Li, R., Han, B., Xing, L., Lee, R., Gao, H. 2019; 14 (1)
  • Tensor framelet based iterative image reconstruction algorithm for low-dose multislice helical CT. PloS one Nam, H., Guo, M., Yu, H., Lee, K., Li, R., Han, B., Xing, L., Lee, R., Gao, H. 2019; 14 (1): e0210410

    Abstract

    In this study, we investigate the feasibility of improving the imaging quality for low-dose multislice helical computed tomography (CT) via iterative reconstruction with tensor framelet (TF) regularization. TF based algorithm is a high-order generalization of isotropic total variation regularization. It is implemented on a GPU platform for a fast parallel algorithm of X-ray forward band backward projections, with the flying focal spot into account. The solution algorithm for image reconstruction is based on the alternating direction method of multipliers or the so-called split Bregman method. The proposed method is validated using the experimental data from a Siemens SOMATOM Definition 64-slice helical CT scanner, in comparison with FDK, the Katsevich and the total variation (TV) algorithm. To test the algorithm performance with low-dose data, ACR and Rando phantoms were scanned with different dosages and the data was equally undersampled with various factors. The proposed method is robust for the low-dose data with 25% undersampling factor. Quantitative metrics have demonstrated that the proposed algorithm achieves superior results over other existing methods.

    View details for PubMedID 30633760

  • Stochastic Primal-Dual Method for Learning Mixture Policies in Markov Decision Processes Khuzani, M., Vasudevan, V., Ren, H., Xing, L., IEEE IEEE. 2019: 1293–1300
  • A deep learning approach for dual-energy CT imaging using a single-energy CT data Zhao, W., Lv, T., Gao, P., Shen, L., Dai, X., Cheng, K., Jia, M., Chen, Y., Xing, L., Matej, S., Metzler, S. D. SPIE-INT SOC OPTICAL ENGINEERING. 2019

    View details for DOI 10.1117/12.2534433

    View details for Web of Science ID 000535354300073

  • Deep DoseNet: A deep neural network for accurate dosimetric transformation between different spatial resolutions and/or different dose calculation algorithms for precision radiation therapy. Physics in medicine and biology Dong, P. n., Xing, L. n. 2019

    Abstract

    The purpose of this work is to introduce a novel deep learning strategy to obtain highly accurate dose plan by transforming from a dose distribution calculated using a low-cost algorithm (or algorithmic settings).25,168 slices of dose distribution are calculated using Eclipse treatment planning system V15.6 (Varian Medical Systems, Palo Alto, CA) on 10 patient CTs whose treatment sites ranging from lung, brain, abdomen and pelvis, with a grid size of 1.25x1.25x1.25mm using both anisotropic analytical algorithm (AAA) in 5mm resolution and Acuros XB algorithm (AXB) in 1.25mm resolution. The AAA dose slices, and the corresponding down sampled CT slices are combined to form a tensor with a size of 2x64x64, working as the input to the deep learning-based dose calculation network (Deep DoseNet), which outputs the calculated Acuros dose with a size of 256x256. The Deep DoseNet (DDN) consists of a feature extraction component and an upscaling part. The DDN converges after ~100 epochs with a learning rate of 10^-4, using ADAM.We compared up sampled AAA dose and DDN output with that of AXB. For the evaluation set, the average mean-square-error decreased from 4.7x10^-4 between AAA and AXB to 7.0x10^-5 between DDN and AXB, with an average improvement of ~ 12 times. The average Gamma index passing rate at 3mm3% improved from 76% between AAA and AXB to 91% between DDN and AXB. The average calculation time is less than 1 milliseconds for a single slice on a NVIDIA DGX workstation.DDN, trained with a large amount of dosimetric data, can be employed as a general-purpose dose calculation acceleration engine across various dose calculation algorithms.

    View details for DOI 10.1088/1361-6560/ab652d

    View details for PubMedID 31869825

  • Upconversion Luminescence Imaging of Tumors with EGFR-Affibody Conjugated Nanophosphors MRS ADVANCES Badieirostami, M., Carpenter, C., Pratx, G., Xing, L., Sun, C. 2019; 4 (46-47): 2461–70
  • Factor 10 Expedience of Monthly Linac Quality Assurance via an Ion Chamber Array and Automation Scripts. Technology in cancer research & treatment Skinner, L. B., Yang, Y., Hsu, A., Xing, L., Yu, A. S., Niedermayr, T. 2019; 18: 1533033819876897

    Abstract

    PURPOSE: While critical for safe and accurate radiotherapy, monthly quality assurance of medical linear accelerators is time-consuming and takes physics resources away from other valuable tasks. The previous methods at our institution required 5 hours to perform the mechanical and dosimetric monthly linear accelerator quality assurance tests. An improved workflow was developed to perform these tests with higher accuracy, with fewer error pathways, in significantly less time.METHODS: A commercial ion chamber array (IC profiler, Sun Nuclear, Melbourne, Florida) is combined with automation scripts to consolidate monthly linear accelerator QA. The array was used to measure output, flatness, symmetry, jaw positions, gated dose constancy, energy constancy, collimator walkout, crosshair centering, and dosimetric leaf gap constancy. Treatment plans were combined with automation scripts that interface with Sun Nuclear's graphical user interface. This workflow was implemented on a standard Varian clinac, with no special adaptations, and can be easily applied to other C-arm linear accelerators.RESULTS: These methods enable, in 30 minutes, measurement and analysis of 20 of the 26 dosimetric and mechanical monthly tests recommended by TG-142. This method also reduces uncertainties in the measured beam profile constancy, beam energy constancy, field size, and jaw position tests, compared to our previous methods. One drawback is the increased uncertainty associated with output constancy. Output differences between IC profiler and farmer chamber in plastic water measurements over a 6-month period, across 4 machines, were found to have a 0.3% standard deviation for photons and a 0.5% standard deviation for electrons, which is sufficient for verifying output accuracy according to TG-142 guidelines. To minimize error pathways, automation scripts which apply the required settings, as well as check the exported data file integrity were employed.CONCLUSIONS: The equipment, procedure, and scripts used here reduce the time burden of routine quality assurance tests and in most instances improve precision over our previous methods.

    View details for DOI 10.1177/1533033819876897

    View details for PubMedID 31707931

  • Fully automatic segmentation of type B aortic dissection from CTA images enabled by deep learning. European journal of radiology Cao, L. n., Shi, R. n., Ge, Y. n., Xing, L. n., Zuo, P. n., Jia, Y. n., Liu, J. n., He, Y. n., Wang, X. n., Luan, S. n., Chai, X. n., Guo, W. n. 2019; 121: 108713

    Abstract

    This study sought to establish a robust and fully automated Type B aortic dissection (TBAD) segmentation method by leveraging the emerging deep learning techniques.Preoperative CTA images of 276 patients with TBAD were retrospectively collected from January 2011 to December 2018. Using a reproducible manual segmentation protocol of three labels (whole aorta, true lumen (TL), and false lumen (FL)), a ground truth database (n = 276) was established and randomly divided into training and testing sets in a rough 8:1 ratio. Three convolutional neural network (CNN) models were developed on the training set (n = 246): single one-task (CNN1), single multi-task (CNN2), and serial multi-task (CNN3) models. Performance was evaluated using the Dice coefficient score (DCS) and lumen volume accuracy on the testing set (n = 30). Pearson correlation, Intra-class correlation coefficients and Bland-Altman plots were used to evaluate the inter-observer measurement agreement.CNN3 performed the best, with mean DCSs of 0.93 ± 0.01, 0.93 ± 0.01 and 0.91 ± 0.02 for the whole aorta, TL, and FL, respectively (p < 0.05). Each label volume from CNN3 showed excellent agreement with the ground truth, with mean volume differences of -31.05 (-82.76 to 20.65) ml, 4.79 (-11.04 to 20.63) ml, and 8.67(-11.40 to 28.74) ml for the whole aorta, TL, and FL, respectively. The segmentation speed of CNN3 was 0.038 ± 0.006 s/image.Deep learning-based model provides a promising approach for accurate and efficient segmentation of TBAD and makes it possible for automated measurements of TBAD anatomical features.

    View details for DOI 10.1016/j.ejrad.2019.108713

    View details for PubMedID 31683252

  • High spatial resolution x-ray luminescence computed tomography and x-ray fluorescence computed tomography Dai, X., Sivasubramanian, K., Xing, L., Pogue, B. W., Gioux, S. SPIE-INT SOC OPTICAL ENGINEERING. 2019

    View details for DOI 10.1117/12.2511875

    View details for Web of Science ID 000492315000020

  • PROSTATE SEGMENTATION WITH ENCODER -DECODER DENSELY CONNECT CONVOLUTIONAL NETWORK (ED-DENSENET) Yuan, Y., Qin, W., Guo, X., Buyyounouski, M., Hancock, S., Hai, B., Xing, L., IEEE IEEE. 2019: 434–37
  • Harnessing the power of deep learning for volumetric CT imaging with single or limited number of projections Shen, L., Zhao, W., Xing, L., Schmidt, T. G., Chen, G. H., Bosmans, H. SPIE-INT SOC OPTICAL ENGINEERING. 2019

    View details for DOI 10.1117/12.2513032

    View details for Web of Science ID 000483585700072

  • Automatic marker-free target positioning and tracking for image-guided radiotherapy and interventions Zhao, W., Shen, L., Wu, Y., Han, B., Yang, Y., Xing, L., Fei, B., Linte, C. A. SPIE-INT SOC OPTICAL ENGINEERING. 2019

    View details for DOI 10.1117/12.2512166

    View details for Web of Science ID 000483683500010

  • Augmented Bladder Tumor Detection Using Deep Learning. European urology Shkolyar, E. n., Jia, X. n., Chang, T. C., Trivedi, D. n., Mach, K. E., Meng, M. Q., Xing, L. n., Liao, J. C. 2019

    Abstract

    Adequate tumor detection is critical in complete transurethral resection of bladder tumor (TURBT) to reduce cancer recurrence, but up to 20% of bladder tumors are missed by standard white light cystoscopy. Deep learning augmented cystoscopy may improve tumor localization, intraoperative navigation, and surgical resection of bladder cancer. We aimed to develop a deep learning algorithm for augmented cystoscopic detection of bladder cancer. Patients undergoing cystoscopy/TURBT were recruited and white light videos were recorded. Video frames containing histologically confirmed papillary urothelial carcinoma were selected and manually annotated. We constructed CystoNet, an image analysis platform based on convolutional neural networks, for automated bladder tumor detection using a development dataset of 95 patients for algorithm training and five patients for testing. Diagnostic performance of CystoNet was validated prospectively in an additional 54 patients. In the validation dataset, per-frame sensitivity and specificity were 90.9% (95% confidence interval [CI], 90.3-91.6%) and 98.6% (95% CI, 98.5-98.8%), respectively. Per-tumor sensitivity was 90.9% (95% CI, 90.3-91.6%). CystoNet detected 39 of 41 papillary and three of three flat bladder cancers. With high sensitivity and specificity, CystoNet may improve the diagnostic yield of cystoscopy and efficacy of TURBT. PATIENT SUMMARY: Conventional cystoscopy has recognized shortcomings in bladder cancer detection, with implications for recurrence. Cystoscopy augmented with artificial intelligence may improve cancer detection and resection.

    View details for DOI 10.1016/j.eururo.2019.08.032

    View details for PubMedID 31537407

  • Reduced acquisition time for L-shell x-ray fluorescence Computed tomography using polycapillary x-ray optics. Medical physics Vernekohl, D. n., Ahmad, M. n., Dai, X. n., Zhao, W. n., Cheng, K. n., Xing, L. n. 2019

    Abstract

    X-ray fluorescence computed tomography (XFCT) is an emerging molecular imaging modality for preclinical and clinical applications with high atomic number contrast agents. XFCT allows detection of molecular biomarkers at tissue depths of 4-9 mm at L-shell energies and several centimeters for K-shell energies, while maintaining highspatial resolution. This is typically not possible for other molecular imaging modalities. The purpose of this study is to demonstrate XFCT imaging with reduced acquisition times. To accomplish this, x-ray focusing polycapillary optics are utilized to simultaneously increase x-ray fluence rate and spatial resolution in L-shell XFCT imaging.A prototype imaging system using a polycapillary focusing optic was demonstrated. The optic, which was custom-designed for this prototype, provided a high fluence rate with a focal spot size of 2.6 mm at a source to isocenter distance of 3 cm with a ten times higher fluence rate compared to standard collimation. The study evaluates three different phantoms to explore different trade-offs and limitations of L-shell XFCT imaging. A low contrast gold phantom and a high contrast gold phantom, each with three target regions with gold concentrations of 60, 80, and 100 μg ml-1 for low contrast and 200, 600, and 1000 μg ml-1 for high contrast, and a mouse-sized water phantom with gold concentrations between 300-500 μg ml-1 were imaged. X-ray fluorescence photons were measured using a silicon drift detector (SDD) with an energy resolution of 180 eV FWHM at an x-ray energy of 11 keV. Images were reconstructed with an iterative image reconstruction algorithm and analyzed for contrast to noise ratio (CNR) and signal to noise ratio (SNR).The XFCT data acquisition could be reduced from 17 h to under one hour. The polycapillary x-ray optic increases the x-ray fluence rate and lowers the amount of background scatter which leads to reduced imaging time and improved sensitivity. The quantitative analysis of the reconstructed images validates that concentrations of 60 μg ml-1 of gold can be visualized with L-shell XFCT imaging. For a mouse sized phantom, a concentration of 300 μg ml-1 gold was detected within a 66 min measurement.With a high fluence rate pencil beam from a polycapillary x-ray source, a reduction in signal integration time is achieved. It is presented that subtle amounts of contrast agents can be detected with L-shell XFCT within biologically relevant time frames. Our basic measurements show that the polycapillary x-ray source technology is appropriate to realize preclinical L-shell XFCT imaging. The integration of more SDDs into the system will lower the dose and increase the sensitivity.

    View details for DOI 10.1002/mp.13822

    View details for PubMedID 31512753

  • Optimizing efficiency and safety in external beam radiotherapy using automated plan check (APC) tool and six sigma methodology. Journal of applied clinical medical physics Liu, S. n., Bush, K. K., Bertini, J. n., Fu, Y. n., Lewis, J. M., Pham, D. J., Yang, Y. n., Niedermayr, T. R., Skinner, L. n., Xing, L. n., Beadle, B. M., Hsu, A. n., Kovalchuk, N. n. 2019; 20 (8): 56–64

    Abstract

    To develop and implement an automated plan check (APC) tool using a Six Sigma methodology with the aim of improving safety and efficiency in external beam radiotherapy.The Six Sigma define-measure-analyze-improve-control (DMAIC) framework was used by measuring defects stemming from treatment planning that were reported to the departmental incidence learning system (ILS). The common error pathways observed in the reported data were combined with our departmental physics plan check list, and AAPM TG-275 identified items. Prioritized by risk priority number (RPN) and severity values, the check items were added to the APC tool developed using Varian Eclipse Scripting Application Programming Interface (ESAPI). At 9 months post-APC implementation, the tool encompassed 89 check items, and its effectiveness was evaluated by comparing RPN values and rates of reported errors. To test the efficiency gains, physics plan check time and reported error rate were prospectively compared for 20 treatment plans.The APC tool was successfully implemented for external beam plan checking. FMEA RPN ranking re-evaluation at 9 months post-APC demonstrated a statistically significant average decrease in RPN values from 129.2 to 83.7 (P < .05). After the introduction of APC, the average frequency of reported treatment-planning errors was reduced from 16.1% to 4.1%. For high-severity errors, the reduction was 82.7% for prescription/plan mismatches and 84.4% for incorrect shift note. The process shifted from 4σ to 5σ quality for isocenter-shift errors. The efficiency study showed a statistically significant decrease in plan check time (10.1 ± 7.3 min, P = .005) and decrease in errors propagating to physics plan check (80%).Incorporation of APC tool has significantly reduced the error rate. The DMAIC framework can provide an iterative and robust workflow to improve the efficiency and quality of treatment planning procedure enabling a safer radiotherapy process.

    View details for DOI 10.1002/acm2.12678

    View details for PubMedID 31423729

  • Modified fast adaptive scatter kernel superposition (mfASKS) correction and its dosimetric impact on CBCT-based proton therapy dose calculation. Medical physics Nomura, Y. n., Xu, Q. n., Peng, H. n., Takao, S. n., Shimizu, S. n., Xing, L. n., Shirato, H. n. 2019

    Abstract

    While cone beam computed tomography (CBCT) is able to provide patient anatomical information, its image quality is severely degraded due to scatter contamination, which degrades the accuracy of CBCT-based dose distribution estimation in proton therapy. In this work, we combined two existing scatter kernel correction methods: the point-spread function (PSF)-based scatter kernel derivation method and the fast adaptive scatter kernel superposition (fASKS) model, and evaluated the impact of the modified fASKS (mfASKS) correction on the accuracy of proton dose distribution estimation. To evaluate feasibility of the mfASKS approach using accurate scatter distributions, both Monte Carlo simulations and experiments were performed for an on-board CBCT machine integrated with a proton therapy machine.We developed a strategy to modify central intensity, constant intensity, and amplitude of the scatter kernels derived from PSFs for the fASKS model. A parameter required for the fASKS model was derived by optimizing uniformity in the mfASKS-corrected reconstructed images. Subsequently, the mfASKS model was used to remove scatter in CBCT imaging. We quantitatively compared the Hounsfield Unit (HU) and proton stopping power ratio (SPR) images for five different phantoms. To assess improvement of dose calculation accuracy, a series of proton treatment plans were produced using the CBCT images with and without the mfASKS correction.The accuracies of both HU and SPR intensity quantifications are improved as a result of the mfASKS correction. Mean absolute water-equivalent path length difference to the true value decreases from 10.3 to 0.934 mm for the Gammex phantom (simulation). At the same time, mfASKS is able to offer more accurate dose distributions, especially at the distal fall-off region where noticeable dose overestimation is observed in the uncorrected scenario. Mean absolute relative error of proton range in the pelvic phantom improves from 5.03% to 2.57% (experiment).mfASKS enables more accurate CBCT-based proton dose calculation. This technique has significant implications in image-guided radiotherapy and dose verifications in adaptive proton therapy.

    View details for DOI 10.1002/mp.13878

    View details for PubMedID 31661161

  • A radiomics approach based on support vector machine using MR images for preoperative lymph node status evaluation in intrahepatic cholangiocarcinoma THERANOSTICS Xu, L., Yang, P., Liang, W., Liu, W., Wang, W., Luo, C., Wang, J., Peng, Z., Xing, L., Huang, M., Zheng, S., Niu, T. 2019; 9 (18): 5374–85

    View details for DOI 10.7150/thno.34149

    View details for Web of Science ID 000474899500017

  • Simulation studies of time reversal-based protoacoustic reconstruction for range and dose verification in proton therapy. Medical physics Yu, Y. n., Li, Z. n., Zhang, D. n., Xing, L. n., Peng, H. n. 2019

    Abstract

    In vivo range verification in proton therapy is a critical step to help minimize range and dose uncertainty. We propose to employ a time reversal (TR)-based approach using proton-induced acoustics (protoacoustics) to reconstruct pressure/dose distribution in heterogeneous tissues.The dose distribution of mono-energetic proton pencil beam in a CT-based patient phantom was calculated by Monte Carlo simulation. K-wave toolbox was used to investigate protoacoustic pressurization, propagation and reconstruction in 2D. To address the tissue heterogeneity effect, a number of physical parameters, including mass density (ρ), speed of sound (c), volumetric thermal expansion coefficient (αV ), isobaric specific heat capacity (Cp ) and attenuation power law prefactor (α0 ), were empirically converted from CT number. The performance was evaluated using two figures of merit: mean square error (MSE) of pressure profiles and Bragg peak localization error (ΔBP ). The impact of six parameters of the TR inversion was examined, including number of sensors, sampling duration, sampling timestep, spill time, noise level and number of iterations.The quantitative accuracy of TR reconstruction and its dependency on the selected parameters is presented. Under optimum conditions, the positioning accuracy of the Bragg peak can be controlled below 1 mm. For instance, MSE is 0.0123 and ΔBP is 0.59 mm under the following conditions (32 sensors, sampling duration: 600 μs, sampling timestep: 40 ns, spill time: 1 μs, no noise).The feasibility of TR-based protoacoustic reconstruction in 2D for proton range verification was first demonstrated. The approach is not only applicable to pencil beam, but also has potential to be extended to passive scattering systems. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.13661

    View details for PubMedID 31199511

  • Markerless pancreatic tumor target localization enabled by deep learning. International journal of radiation oncology, biology, physics Zhao, W. n., Shen, L. n., Han, B. n., Yang, Y. n., Cheng, K. n., Toesca, D. A., Koong, A. C., Chang, D. T., Xing, L. n. 2019

    Abstract

    To estimate the impact of radiotherapy (RT) on non-breast second malignant neoplasms (SMNs) in young women survivors of stage I-IIIA breast cancer.Women aged 20-44 years diagnosed with stage I-IIIA breast cancer (1988-2008) were identified in Surveillance, Epidemiology, and End Results (SEER) 9 registries. Bootstrapping approach and competing risk proportional hazards models were used to evaluate the effect of RT on non-breast SMN risk. The analysis was repeated in racial subgroups. Radio-tolerance score (RTS) analysis of normal airway epithelium was performed using Gene Expression Omnibus (GEO) datasets.Within records of 30,003 women with primary breast cancer, 20,516 eligible patients were identified (including 2,183 African Americans [AAs] and 16,009 Caucasians). The 25-year cumulative incidences of SMN were 5.2% and 3.6% (RT vs. no-RT) for AAs with 12.8-year and 17.4-year (RT vs. no-RT) median follow-up (HR=1.81, 95% bootstrapping confidence intervals [BCIs] [1.02, 2.50], P < 0.05); and 6.4% and 5.9% (RT vs. no-RT) for Caucasians with 14.3-year and 18.1-year (RT vs. no-RT) median follow-up (HR=1.10, 95% BCI [0.61, 1.40], P > 0.05). The largest portion of excess RT-related SMN risk was lung cancer (AA: HR=2.08, 95% BCI [1.02, 5.39], P < 0.05; Caucasian: HR=1.50, 95% BCI [0.84, 5.38], P > 0.05). STEPP analysis revealed higher post-RT non-breast SMN risk essentially throughout entire age range 20-44 years, with larger HR for RT in AAs. RTS of normal airway epithelium from young AA women was significantly lower than that from young Caucasian women (P = 0.038).With a projected 25-year follow-up, RT is associated with elevated risk of non-breast SMNs, particularly second lung cancer, in young women survivors of stage I-IIIA breast cancer, especially higher in AA women than Caucasian women.

    View details for DOI 10.1016/j.ijrobp.2019.05.071

    View details for PubMedID 31201892

  • Projection-domain scatter correction for cone beam computed tomography using a residual convolutional neural network. Medical physics Nomura, Y. n., Xu, Q. n., Shirato, H. n., Shimizu, S. n., Xing, L. n. 2019

    Abstract

    Scatter is a major factor degrading the image quality of cone beam computed tomography (CBCT). Conventional scatter correction strategies require handcrafted analytical models with ad hoc assumptions, which often leads to less accurate scatter removal. This study aims to develop an effective scatter correction method using a residual convolutional neural network (CNN).A U-net based 25-layer CNN was constructed for CBCT scatter correction. The establishment of the model consists of three steps: model training, validation and testing. For model training, a total of 1,800 pairs of x-ray projection and the corresponding scatter-only distribution in non-anthropomorphic phantoms taken in full-fan scan were generated using Monte Carlo simulation of a CBCT scanner installed with a proton therapy system. An end-to-end CNN training was implemented with two major loss functions for 100 epochs with a mini-batch size of 10. Image rotations and flips were randomly applied to augment the training datasets during training. For validation, 200 projections of a digital head phantom were collected. The proposed CNN-based method was compared to a conventional projection-domain scatter correction method named fast adaptive scatter kernel superposition (fASKS) method using 360 projections of an anthropomorphic head phantom. Two different loss functions were applied for the same CNN to evaluate the impact of loss functions on the final results. Furthermore, the CNN model trained with full-fan projections was fine-tuned for scatter correction in half-fan scan by using transfer learning with additional 360 half-fan projection pairs of non-anthropomorphic phantoms. The tuned-CNN model for half-fan scan was compared with the fASKS method as well as the CNN-based method without the fine-tuning using additional lung phantom projections.The CNN-based method provides projections with significantly reduced scatter and CBCT images with more accurate Hounsfield Units (HUs) than that of the fASKS-based method. Root mean squared error of the CNN-corrected projections was improved to 0.0862 compared to 0.278 for uncorrected projections or 0.117 for the fASKS-corrected projections. The CNN-corrected reconstruction provided better HU quantification, especially in regions near the air or bone interfaces. All four image quality measures, which include mean absolute error (MAE), mean squared error (MSE), peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), indicated that the CNN-corrected images were significantly better than that of the fASKS-corrected images. Moreover, the proposed transfer learning technique made it possible for the CNN model trained with full-fan projections to be applicable to remove scatters in half-fan projections after fine-tuning with only a small number of additional half-fan training datasets. SSIM value of the tuned-CNN-corrected images was 0.9993 compared to 0.9984 for the non-tuned-CNN-corrected images or 0.9990 for the fASKS-corrected images. Finally, the CNN-based method is computationally efficient - the correction time for the 360 projections only took less than 5 seconds in the reported experiments on a PC (4.20 GHz Intel Core-i7 CPU) with a single NVIDIA GTX 1070 GPU.The proposed deep learning-based method provides an effective tool for CBCT scatter correction and holds significant value for quantitative imaging and image guided radiation therapy (IGRT). This article is protected by copyright. All rights reserved.

    View details for PubMedID 31077390

  • Self-attention convolutional neural network for improved MR image reconstruction Information Science Wu, Y. 2019: 317-328
  • Neural networks for deep radiotherapy dose analysis and prediction of liver SBRT outcomes. IEEE journal of biomedical and health informatics Ibragimov, B. n., Toesca, D. n., Yuan, Y. n., Koong, A. n., Daniel, C. n., Xing, L. n. 2019

    Abstract

    Stereotactic body radiation therapy (SBRT) is a relatively novel treatment modality, with little post-treatment prognostic information reported. This study proposes a novel neural network-based paradigm for accurate prediction of liver SBRT outcomes. We assembled a database of patients treated with liver SBRT at our institution. Together with a 3D dose delivery plans for each SBRT treatment, other variables such as patients' demographics, quantified abdominal anatomy, history of liver comorbidities, other liver-directed therapies and liver function tests were collected. We developed a multi-path neural network with the convolutional path for 3D dose plan analysis and fully-connected path for other variables analysis, where the network was trained to predict post-SBRT survival and local cancer progression. To enhance the network robustness, it was initially pre-trained on a large database of CT images. Following n-fold cross-validation, the network automatically identified patients that are likely to have longer survival or late cancer recurrence, i.e. patients with the positive predicted outcome (PPO) of SBRT, and vice versa, i.e. negative predicted outcome (NPO). The predicted results agreed with actual SBRT outcomes with 56% of PPO patients and 0% NPO patients with primary liver cancer survived more than 2-years after SBRT. Similarly, 82% of PPO patients and 0% of NPO patients with metastatic liver cancer survived 2-year threshold. The obtained results were superior to the performance of support vector machine and random forest classifiers. Furthermore, the network was able to identify the critical-to-spare liver regions, and the critical clinical features associated with the highest risks of negative SBRT outcomes.

    View details for DOI 10.1109/JBHI.2019.2904078

    View details for PubMedID 30869633

  • Deep Generative Adversarial Neural Networks for Compressive Sensing MRI IEEE TRANSACTIONS ON MEDICAL IMAGING Mardani, M., Gong, E., Cheng, J. Y., Vasanawala, S. S., Zaharchuk, G., Xing, L., Pauly, J. M. 2019; 38 (1): 167–79

    Abstract

    Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require tradeoffs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the image diagnostic quality. To address these challenges, we propose a novel CS framework that uses generative adversarial networks (GAN) to model the (low-dimensional) manifold of high-quality MR images. Leveraging a mixture of least-squares (LS) GANs and pixel-wise l1/l2 cost, a deep residual network with skip connections is trained as the generator that learns to remove the aliasing artifacts by projecting onto the image manifold. The LSGAN learns the texture details, while the l1/l2 cost suppresses high-frequency noise. A discriminator network, which is a multilayer convolutional neural network (CNN), plays the role of a perceptual cost that is then jointly trained based on high-quality MR images to score the quality of retrieved images. In the operational phase, an initial aliased estimate (e.g., simply obtained by zero-filling) is propagated into the trained generator to output the desired reconstruction. This demands a very low computational overhead. Extensive evaluations are performed on a large contrast-enhanced MR dataset of pediatric patients. Images rated by expert radiologists corroborate that GANCS retrieves higher quality images with improved fine texture details compared with conventional Wavelet-based and dictionary-learning-based CS schemes as well as with deep-learning-based schemes using pixel-wise training. In addition, it offers reconstruction times of under a few milliseconds, which are two orders of magnitude faster than the current state-of-the-art CS-MRI schemes.

    View details for DOI 10.1109/TMI.2018.2858752

    View details for Web of Science ID 000455110500017

    View details for PubMedID 30040634

  • Prostate Cancer Classification with Multi-parametric MRI Transfer Learning Model. Medical physics Yuan, Y., Qin, W., Buyyounouski, M., Ibragimov, B., Hancock, S., Han, B., Xing, L. 2018

    Abstract

    PURPOSE: Prostate cancer classification has significantly impact on the prognosis and treatment planning of patients. Currently, the classifying is based on the Gleason score analysis of biopsied tissues, which is neither accurate nor risk-free. This study aims to learn discriminative features for prostate images and assist physicians to classify prostate cancer automatically.METHODS: We develop a novel multi-parametric magnetic resonance transfer learning (MPTL) method to automatically stage prostate cancer. We first establish a deep convolutional neural network with three branch architectures, which transfer pre-trained model to compute features from multi-parametric MRI images (mp-MRI) : T2w transaxial, T2w sagittal and apparent diffusion coefficient (ADC). The learned features are concatenated to represent information of mp-MRI sequences. A new image similarity constraint is then proposed to enable the distribution of the features within the same category in a narrow angle region. With the joint constraints of softmax loss and image similarity loss in the fine-tuning process, the MPTL can provide descriptive features with intraclass compactness and interclass separability.RESULTS: Two cohorts: 132 cases from our institutional review board approved patient database and 112 cases from the PROSTATEx-2 Challenge are utilized to evaluate the robustness and effectiveness of the proposed MPTL model. Our model achieved high accuracy of prostate cancer classification (accuracy of 86.92%). Moreover, the comparison results demonstrate that our method outperforms both hand-crafted feature based methods and existing deep learning models in prostate cancer classification with higher accuracy.CONCLUSION: The experiment results showed that the proposed method can learn discriminative features for prostate images and classify the cancer accurately. Our MPTL model could be further applied in the clinical practice to provide valuable information for cancer treatment and precision medicine. This article is protected by copyright. All rights reserved.

    View details for PubMedID 30597561

  • Dosimetric Features-Driven Machine Learning Model for DVHs Prediction in VMAT Treatment Planning. Medical physics Ma, M., Kovalchuk, N., Buyyounouski, M. K., Xing, L., Yang, Y. 2018

    Abstract

    PURPOSE: Few features characterizing the dosimetric properties of the patients are included in currently available dose-volume histogram (DVH) prediction models, making it intractable to build a correlative relationship between the input and output parameters. Here we use PTV-only treatment plans of the patients (i.e., the achievable dose distribution in the absence of organs-at-risks (OARs) constraints) to estimate the potentially achievable quality of treatment plans and establish a machine learning-based DVH prediction framework with the use of the dosimetric metric as model input parameters.METHODS: A support vector regression (SVR) approach was used as the backbone of our machine learning model. A database containing volumetric modulated arc therapy (VMAT) plans of 63 prostate cancer patients were used. For each patient, the PTV-only plan was generated first. A correlative relationship between the OAR DVH of the PTV-only plan (model input) and the corresponding DVH of the clinical treatment plan (CTP) (model output) was then established with the 53 training cases. The prediction model was tested by the validation cohort of 10 cases.RESULTS: For the training cohort, the checks of dosimetric endpoints (DEs) indicated that 52 out of 53 plans (98%) were within 10% error bound for bladder, and 45 out of 53 plans (85%) were within 10% error bound for rectum. In the validation tests, 92% and 96% of the DEs were within the 10% error bounds for bladder and rectum respectively, and 8 out of 10 validation plans (80%) were within 10% error bound for both bladder and rectum. The sum of absolute residuals (SAR) achieved mean 0.034 ± 0.028 and 0.046 ± 0.021 for the bladder and rectum, respectively.CONCLUSIONS: A novel dosimetric features-driven machine learning model with the use of PTV-only plan has been established for DVH prediction. The framework is capable of efficiently generating best achievable DVHs for VMAT planning. This article is protected by copyright. All rights reserved.

    View details for PubMedID 30536442

  • Learning deconvolutional deep neural network for high resolution medical image reconstruction INFORMATION SCIENCES Liu, H., Xu, J., Wu, Y., Guo, Q., Ibragimov, B., Xing, L. 2018; 468: 142–54
  • Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT MEDICAL PHYSICS Ibragimov, B., Toesca, D., Chang, D., Yuan, Y., Koong, A., Xing, L. 2018; 45 (10): 4763–74

    View details for DOI 10.1002/mp.13122

    View details for Web of Science ID 000446995000056

  • Editorial: Machine Learning With Radiation Oncology Big Data FRONTIERS IN ONCOLOGY Deng, J., El Naqa, I., Xing, L. 2018; 8: 416

    View details for DOI 10.3389/fonc.2018.00416

    View details for Web of Science ID 000445744800001

    View details for PubMedID 30319978

    View details for PubMedCentralID PMC6170661

  • Rare-Earth-Doped Nanoparticles for Short-Wave Infrared Fluorescence Bioimaging and Molecular Targeting of alpha(V)beta(3)-Expressing Tumors MOLECULAR IMAGING Naczynski, D., Stafford, J. H., Tuerkcan, S., Jenkins, C., Koh, A., Sun, C., Xing, L. 2018; 17
  • Cumulative dose of radiation therapy of hepatocellular carcinoma patients and its deterministic relation to radiation-induced liver disease MEDICAL DOSIMETRY Huang, P., Yu, G., Kapp, D. S., Bian, X., Ma, C., Li, H., Chen, J., Liang, Y., Zhang, Y., Qin, S., Xie, Y., Yang, Y., Yin, Y., Xing, L., Li, D. 2018; 43 (3): 258–66

    Abstract

    This study aimed to investigate the relationship between dose and radiation-induced liver disease (RILD) in patients with hepatocellular carcinoma (HCC) receiving 3-dimensional conformal radiotherapy (3DCRT). Twenty-three patients with HCC who received conventional fractionated 3DCRT, including 7 who were diagnosed with classic RILD, were enrolled in this retrospective investigation. Cone-beam computed tomography (CBCT) scans were acquired at the time of treatment for each patient. The beams from each patient's treatment plan were applied to each pretreatment CBCT (the modified CBCT or mCBCT) to construct the delivered dose distribution of the day considering inter-treatment anatomy changes. The daily doses were summed together with the help of deformable image registration (DIR) to obtain the adjusted cumulative dose (Dadjusted). The dose changes to the normal liver between the original planned dose (Dplan) and Dadjusted were evaluated by V20, V30, V40, and the mean dose to normal liver (MDTNL). Univariate analysis was performed to identify the significant dose changes. Among the 23 patients, the liver V20, V30, V40, and MDTNL showed significant differences between Dplan and Dadjusted, with average values of these parameters increased by 4.1%, 4.7%, 4.5%, and 3.9 Gy, respectively (p < 0.05). The adjusted liver dose in 21 patients (91%) was higher than the planned value. For patients without and with RILD,the MDTNL was increased on average by 3.5 Gy and 4.7 Gy, and normal tissue complication probability (NTCP) increased on average by 2.8% and 7.5%, respectively. Our study found that the adjusted cumulative dose based on calculations using pretreatment mCBCT differs significantly from planned dose; the use of the dosimetric results of the initial plan was found to be less predictive of RILD as compared with Dadjusted. Determination of a reconstructed Dadjusted using the mCBCT scans are more accurate in predicting RILD and has the potential to reduce the risk of RILD.

    View details for PubMedID 29198389

  • Generalized Adaptive Gaussian Markov Random Field for X-Ray Luminescence Computed Tomography IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Zhang, G., Tzoumas, S., Cheng, K., Liu, F., Liu, J., Luo, J., Bai, J., Xing, L. 2018; 65 (9): 2130–33

    Abstract

    X-ray luminescence computed tomography (XLCT) is an emerging and promising modality, but suffers from inferior reconstructions and smoothed target shapes. This work aims to improve the image quality with new mathematical framework.We present a Bayesian local regularization framework to tackle the ill-conditioness of XLCT. Different from traditional overall regularization strategies, the proposed method utilizes correlations of neighboring voxels to regularize the solution locally based on generalized adaptive Gaussian Markov random field (GAGMRF), and provides an adjustable parameter to facilitate the edge-preserving property.Numerical simulations and phantom experiments show that the GAGMRF method yields both high image quality and accurate target shapes.Compared to conventional L2 and L1 regularizations, GAGMRF provides a new and efficient model for high quality imaging based on the Bayesian framework.The GAGMRF method offers a flexible regularization framework to adapt to a wide range of biomedical applications.

    View details for DOI 10.1109/TBME.2017.2785364

    View details for Web of Science ID 000442349500023

    View details for PubMedID 29989945

  • Feasibility of optimizing intensity-modulated radiation therapy plans based on measured mucosal dose adjacent to dental fillings and toxicity outcomes JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS Seol, S., Aggarwal, S., von Eyben, R., Wang, Z., Chan, C., Say, C., Xing, L., Hara, W., Yang, Y., Quynh Thu Le 2018; 19 (5): 444–52

    Abstract

    We prospectively investigated the feasibility of IMRT treatment plan optimization based on dosimeter measurements of lateral tongue mucosal dose adjacent to the dental fillings and evaluated dose-toxicity relationship and factors affecting oral mucositis (OM) in head and neck cancer patients. Twenty-nine head and neck cancer patients with metallic dental fillings who were scheduled to undergo fractionated external beam radiation therapy (RT) ± chemotherapy were enrolled. The lateral tongue dose was measured and if the calculated dose for the entire treatment was ≥35 Gy, a re-plan was generated to reduce the lateral tongue mucosal dose. OM was graded weekly according to Common Terminology Criteria for Adverse Events version 4.0 and the patients completed the Oral Mucositis Weekly Questionnaire-Head and Neck Cancer. The result showed that it was not feasible to optimize the IMRT plan based on measured tongue dose in most of the patients who needed re-plan as re-planning compromised the target coverage in 60% of these patients. The duration of grade (Gr) 2 OM was correlated with measured lateral tongue dose (P = 0.050). Concurrent cetuximab was significantly associated with faster onset of Gr2 OM than concurrent cisplatin (P = 0.006) and with longer duration of OM (P = 0.041) compared to concurrent cisplatin or IMRT-alone. The pattern of reported pain over time was significantly different for each treatment type (RT and cetuximab, RT and cisplatin and RT-alone) and depending on the dose level (P = 0.006). In conclusion, optimizing the IMRT plan based on measured lateral tongue dose was not feasible. Measured lateral tongue dose was significantly correlated with longer duration of OM ≥Gr2, and concurrent cetuximab was associated with earlier onset and longer duration of OM ≥Gr2.

    View details for PubMedID 29984915

  • Development of deep neural network forindividualized hepatobiliary toxicity prediction after liver SBRT. Medical physics Ibragimov, B., Toesca, D., Chang, D., Yuan, Y., Koong, A., Xing, L. 2018

    Abstract

    BACKGROUND: Accurate prediction of radiation toxicity of healthy organs-at-risks (OARs) critically determines the radiation therapy (RT) success. The existing dose volume histogram-based metric may grossly under/over-estimate the therapeutic toxicity after 27% in liver RT and 50% in head-and-neck RT. We propose the novel paradigm for toxicity prediction by leveraging the enormous potential of deep learning and go beyond the existing dose/volume histograms.EXPERIMENTAL DESIGN: We employed a database of 125 liver stereotactic body RT (SBRT) cases with follow-up data to train deep learning-based toxicity predictor. Convolutional neural networks (CNNs) were applied to discover the consistent patterns in 3D dose plans associated with toxicities. To enhance the predicting power, we first pre-train the CNNs with transfer learning from 3D CT images of 2644 human organs. CNNs were then trained on liver SBRT cases. Furthermore, non-dosimetric pre-treatment features, such as patients' demographics, underlying liver diseases, liver-directed therapies, were inputted into the fully-connected neural network for more comprehensive prediction. The saliency maps of CNNs were used to estimate the toxicity risks associated with irradiation of anatomical regions of specific OARs. In addition, we applied machine learning solutions to map numerical pre-treatment features with hepatobiliary toxicity manifestation.RESULTS: Among 125 liver SBRT patients, 58 were treated for liver metastases, 36 for hepatocellular carcinoma, 27 for cholangiocarcinoma and 4 for other histologies. We observed that CNN we able to achieve accurate hepatobiliary toxicity prediction with the AUC of 0.79, whereas combining CNN for 3D dose plan analysis and fully-connected neural networks for numerical feature analysis resulted in AUC of 0.85. Deep learning produces almost 2 times fewer false positive toxicity predictions in comparison to DVH-based predictions, when the number of false negatives, i.e. missed toxicities, was minimized. The CNN saliency maps automatically estimated the toxicity risks for portal vein (PV) regions. We discovered that irradiation of the proximal portal vein is associated with two-times higher toxicity risks (risk score: 0.66) that irradiation of the left portal vein (risk score: 0.31).CONCLUSIONS: The framework offers clinically accurate tools for hepatobiliary toxicity prediction and automatic identification of anatomical regions that are critical to spare during SBRT. This article is protected by copyright. All rights reserved.

    View details for PubMedID 30098025

  • Coded-Aperture Compressed Sensing X-Ray Luminescence Tomography IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Tzoumas, S., Vernekohl, D., Xing, L. 2018; 65 (8): 1892–95

    Abstract

    The purpose of this work is to introduce and study a novel imaging geometry for X-ray luminescence computed tomography (XLCT), termed coded aperture compressive X-ray luminescence tomography (CAC-XLCT).CAC-XLCT is studied through simulations of X-ray and diffuse light propagation and the implementation of a compressed sensing image reconstruction algorithm.CAC-XLCT is compared against cone beam XLCT considering simulated targets with varying complexity, and it is found to offer a remarkable enhancement in spatial resolution and image quality with only a small overhead in image acquisition time.XLCT has been mainly investigated so far in pencil beam and cone beam excitation geometries which suffer from either very long image acquisition time or low spatial resolution and accuracy. CAC-XLCT presents a very promising alternative, which can offer simultaneously high spatial resolution, high image quality, and fast image acquisition, appropriate for in vivo imaging.

    View details for DOI 10.1109/TBME.2017.2770148

    View details for Web of Science ID 000439382300023

    View details for PubMedID 29989958

  • Polarized x-ray excitation for scatter reduction in x-ray fluorescence computed tomography MEDICAL PHYSICS Vernekohl, D., Tzoumas, S., Zhao, W., Xing, L. 2018; 45 (8): 3741–48

    View details for DOI 10.1002/mp.12997

    View details for Web of Science ID 000441292000027

  • Segmentation of parotid glands from registered CT and MR images PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS Mocnik, D., Ibragimov, B., Xing, L., Strojan, P., Likar, B., Pernus, F., Vrtovec, T. 2018; 52: 33–41

    Abstract

    To develop an automatic multimodal method for segmentation of parotid glands (PGs) from pre-registered computed tomography (CT) and magnetic resonance (MR) images and compare its results to the results of an existing state-of-the-art algorithm that segments PGs from CT images only.Magnetic resonance images of head and neck were registered to the accompanying CT images using two different state-of-the-art registration procedures. The reference domains of registered image pairs were divided on the complementary PG regions and backgrounds according to the manual delineation of PGs on CT images, provided by a physician. Patches of intensity values from both image modalities, centered around randomly sampled voxels from the reference domain, served as positive or negative samples in the training of the convolutional neural network (CNN) classifier. The trained CNN accepted a previously unseen (registered) image pair and classified its voxels according to the resemblance of its patches to the patches used for training. The final segmentation was refined using a graph-cut algorithm, followed by the dilate-erode operations.Using the same image dataset, segmentation of PGs was performed using the proposed multimodal algorithm and an existing monomodal algorithm, which segments PGs from CT images only. The mean value of the achieved Dice overlapping coefficient for the proposed algorithm was 78.8%, while the corresponding mean value for the monomodal algorithm was 76.5%.Automatic PG segmentation on the planning CT image can be augmented with the MR image modality, leading to an improved RT planning of head and neck cancer.

    View details for PubMedID 30139607

    View details for PubMedCentralID PMC6110103

  • Isodose feature-preserving voxelization (IFPV) for radiation therapy treatment planning MEDICAL PHYSICS Liu, H., Xing, L. 2018; 45 (7): 3321–29

    Abstract

    Inverse planning involves iterative optimization of a large number of parameters and is known to be a labor-intensive procedure. To reduce the scale of computation and improve characterization of isodose plan, this paper presents an isodose feature-preserving voxelization (IFPV) framework for radiation therapy applications and demonstrates an implementation of inverse planning in the IFPV domain.A dose distribution in IFPV scheme is characterized by partitioning the voxels into subgroups according to their geometric and dosimetric values. Computationally, the isodose feature-preserving (IFP) clustering combines the conventional voxels that are spatially and dosimetrically close into physically meaningful clusters. A K-means algorithm and support vector machine (SVM) runs sequentially to group the voxels into IFP clusters. The former generates initial clusters according to the geometric and dosimetric information of the voxels and SVM is invoked to improve the connectivity of the IFP clusters. To illustrate the utility of the formalism, an inverse planning framework in the IFPV domain is implemented, and the resultant plans of three prostate IMRT and one head-and-neck cases are compared quantitatively with that obtained using conventional inverse planning technique.The IFPV generates models with significant dimensionality reduction without compromising the spatial resolution seen in traditional downsampling schemes. The implementation of inverse planning in IFPV domain is demonstrated. In addition to the improved computational efficiency, it is found that, for the cases studied here, the IFPV-domain inverse planning yields better treatment plans than that of DVH-based planning, primarily because of more effective use of both geometric and dose information of the system during plan optimization.The proposed IFPV provides a low parametric representation of isodose plan without compromising the essential characteristics of the plan, thus providing a practically valuable framework for various applications in radiation therapy.

    View details for PubMedID 29772065

    View details for PubMedCentralID PMC6041150

  • A unified material decomposition framework for quantitative dual- and triple-energy CT imaging MEDICAL PHYSICS Zhao, W., Vernekohl, D., Han, F., Han, B., Peng, H., Yang, Y., Xing, L., Min, J. K. 2018; 45 (7): 2964–77

    View details for DOI 10.1002/mp.12933

    View details for Web of Science ID 000438211400013

  • Monte Carlo tree search -based non-coplanar trajectory design for station parameter optimized radiation therapy (SPORT) PHYSICS IN MEDICINE AND BIOLOGY Dong, P., Liu, H., Xing, L. 2018; 63 (13)
  • Burst and continuous high frequency irreversible electroporation protocols evaluated in a 3D tumor model PHYSICS IN MEDICINE AND BIOLOGY Sano, M. B., Fesmire, C. C., DeWitt, M. R., Xing, L. 2018; 63 (13): 135022

    Abstract

    High frequency irreversible electroporation (H-FIRE) is an emerging cancer therapy which uses bursts of alternating polarity pulses to target and destroy the membranes of cells within a predictable volume. Typically, 2 µs pulses are rapidly repeated 24-50 times to create a 48-100 µs long energy burst. Bursts are repeated 100×  at 1 Hz, resulting in an integrated energized time of 0.01 s per treatment. A 3D in vitro tumor model was used to investigate H-FIRE parameters in search of optimal energy timing protocols. Monopolar IRE treatments (100  ×  100 µs positive polarity pulses) resulted in a lethal electric field threshold of 423 V cm-1. Baseline H-FIRE treatments (100  ×  100 µs bursts of 2 µs pulses) resulted in a lethal threshold of 818 V cm-1. Increasing the number of H-FIRE bursts from 100×  to 1000×  reduced the lethal threshold to 535 V cm-1. An alternative diffuse H-FIRE protocol, which delivers 4 µs pulse cycles (one positive and one negative 2 µs pulse) continuously at 100 Hz, resulted in the lowest H-FIRE lethal threshold of 476 V cm-1. Finite element simulations using 5 kV pulses predict an IRE ablation volume of 3.9 cm3 (1.7 cm diameter) and a maximum H-FIRE ablation volume of 5.3 cm3 (2.4 cm diameter) when a clinical electrode and grounding pad configuration is used. Ablations as large as 15.7 cm3 (3.3 cm diameter) are predicted for H-FIRE treatments with 10 kV pulses. These results combine to demonstrate the importance of electrode geometry, pulse timing, and clinical delivery protocols for the creation of large clinically meaningful ablations.

    View details for DOI 10.1088/1361-6560/aacb62

    View details for Web of Science ID 000437823200004

    View details for PubMedID 29978834

  • A Dual-Modality Hybrid Imaging System Harnesses Radioluminescence and Sound to Reveal Molecular Pathology of Atherosclerotic Plaques SCIENTIFIC REPORTS Zaman, R. T., Yousefi, S., Long, S. R., Saito, T., Mandella, M., Qiu, Z., Chen, R., Contag, C. H., Gambhir, S. S., Chin, F. T., Khuri-Yakub, B. T., McConnell, M. V., Shung, K., Xing, L. 2018; 8: 8992

    Abstract

    Atherosclerosis is a progressive inflammatory condition caused by an unstable lesion, called thin-cap fibro atheromata (TCFA) that underlies coronary artery disease (CAD)-one of the leading causes of death worldwide. Therefore, early clinical diagnosis and effective risk stratification is important for CAD management as well as preventing progression to catastrophic events. However, early detection could be difficult due to their small size, motion, obscuring 18F-FDG uptake by adjacent myocardium, and complex morphological/biological features. To overcome these limitations, we developed a catheter-based Circumferential-Intravascular-Radioluminescence-Photoacoustic-Imaging (CIRPI) system that can detect vulnerable plaques in coronary arteries and characterizes them with respect to pathology and biology. Our CIRPI system combined two imaging modalities: Circumferential Radioluminescence Imaging (CRI) and PhotoAcoustic Tomography (PAT) within a novel optical probe. The probe's CaF2:Eu based scintillating imaging window provides a 360° view of human (n = 7) and murine carotid (n = 10) arterial plaques by converting β-particles into visible photons during 18F-FDG decay. A 60× and 63× higher radioluminescent signals were detected from the human and murine plaque inflammations, respectively, compared to the control. The system's photoacoustic imaging provided a comprehensive analysis of the plaque compositions and its morphologic information. These results were further verified with IVIS-200, immunohistochemical analysis, and autoradiography.

    View details for PubMedID 29895966

  • Monte Carlo tree search -based non-coplanar trajectory design for station parameter optimized radiation therapy (SPORT). Physics in medicine and biology Dong, P., Liu, H., Xing, L. 2018

    Abstract

    PURPOSE: An important yet challenging problem in LINAC-based rotational arc radiation therapy is the design of beam trajectory, which requires simultaneous consideration of delivery efficiency and final dose distribution. In this work, we propose a novel trajectory selection strategy by developing a Monte Carlo tree search (MCTS) algorithm during the beam trajectory selection process. Methods: To search through the vast number of possible trajectories, MCTS algorithm was implemented. In this approach, a candidate trajectory is explored by starting from a leaf node and sequentially examining the next level of linked nodes with consideration of geometric and physical constraints. The maximum Upper Confidence Bounds for Trees, which is a function of average objective function value and the number of times the node under testing has been visited, was employed to intelligently select the trajectory. For each candidate trajectory, we run an inverse fluence map optimization with an infinity norm regularization. The ranking of the plan as measured by the corresponding objective function value was then fed back to update the statistics of the nodes on the trajectory. The method was evaluated with a chest wall and a brain case, and the results were compared with the coplanar and noncoplanar 4pi beam configurations. Results: For both clinical cases, the MCTS method found effective and easy-to-deliver trajectories within an hour. As compared with the coplanar plans, it offers much better sparing of the OARs while maintaining the PTV coverage. The quality of the MCTS-generated plan is found to be comparable to the 4pi plans. Conclusion: AI based on MCTS is valuable to facilitate the design of beam trajectory and paves the way for future clinical use of non-coplanar treatment delivery. .

    View details for PubMedID 29863493

  • A New Registration Method Based On Surface Image Guided Radiation Therapy Zhao, J., Huang, P., Xing, L., Yi, S., Zhang, B., Meng, F., Li, D. WILEY. 2018: E395
  • Reduction of Muscle Contractions during Irreversible Electroporation Therapy Using High-Frequency Bursts of Alternating Polarity Pulses: A Laboratory Investigation in an Ex Vivo Swine Model JOURNAL OF VASCULAR AND INTERVENTIONAL RADIOLOGY Sano, M. B., Fan, R. E., Cheng, K., Saenz, Y., Sonn, G. A., Hwang, G. L., Xing, L. 2018; 29 (6): 893–98
  • Polarized X-ray excitation for scatter reduction in X-ray fluorescence computed tomography. Medical physics Vernekohl, D., Tzoumas, S., Zhao, W., Xing, L. 2018

    Abstract

    PURPOSE: X-ray fluorescence computer tomography (XFCT) is a new molecular imaging modality which uses X-ray excitation to stimulate the emission of fluorescent photons in high atomic number contrast agents. Scatter contamination is one of the main challenges in XFCT imaging which limits the molecular sensitivity. When polarized X-rays are used, it is possible to reduce the scatter contamination significantly by placing detectors perpendicular to the polarization direction. This study quantifies scatter contamination for polarized and unpolarized X-ray excitation and determines the advantages of scatter reduction.METHODS: The amount of scatter in preclinical XFCT is quantified in Monte Carlo simulations. The fluorescent X-rays are emitted isotropically, while scattered X-rays propagate in polarization direction. The magnitude of scatter contamination is studied in XFCT simulations of a mouse phantom. In this study, the contrast agent gold is examined as an example but a scatter reduction from polarized excitation is also expected for other elements. The scatter reduction capability is examined for different polarization intensities with a monoenergetic X-ray excitation energy of 82 keV. The study evaluates two different geometrical shapes of CZT detectors which are modeled with an energy resolution of 1 keV FWHM at an X-ray energy of 80 keV. Benefits of a detector placement perpendicular to the polarization direction are shown in iterative and analytic image reconstruction including scatter correction. The contrast to noise ratio (CNR) and the normalized mean square error (NMSE) are analyzed and compared for the reconstructed images.RESULTS: A substantial scatter reduction for common detector sizes was achieved for 100% and 80% linear polarization while lower polarization intensities provide a decreased scatter reduction. By placing the detector perpendicular to the polarization direction, a scatter reduction by factor up to 5.5 can be achieved for common detector sizes. The image reconstruction showed that for a scatter magnitude decrease by a factor of 2.4, the molecular sensitivity could almost be doubled.CONCLUSION: Scatter reduction lowers the amount of noise in the projection datasets and reconstructed images which enhances molecular sensitivity at equal dose. The results support the use of linear polarized X-rays to reduce scatter in XFCT imaging. This article is protected by copyright. All rights reserved.

    View details for PubMedID 29800510

  • Artificial intelligence will soon change the landscape of medical physics research and practice MEDICAL PHYSICS Xing, L., Krupinski, E. A., Cai, J. 2018; 45 (5): 1791–93

    View details for DOI 10.1002/mp.12831

    View details for Web of Science ID 000432023100003

    View details for PubMedID 29476545

  • Synergistically Enhancing the Therapeutic Effect of Radiation Therapy with Radiation Activatable and Reactive Oxygen Species-Releasing Nanostructures ACS NANO Cheng, K., Sano, M., Jenkins, C. H., Zhang, G., Vernekohl, D., Zhao, W., Wei, C., Zhang, Y., Zhang, Z., Liu, Y., Cheng, Z., Xing, L. 2018; 12 (5): 4946–58

    Abstract

    Nanoparticle-based radio-sensitizers can amplify the effects of radiation therapy on tumor tissue even at relatively low concentrations while reducing the potential side effects to healthy surrounding tissues. In this study, we investigated a hybrid anisotropic nanostructure, composed of gold (Au) and titanium dioxide (TiO2), as a radio-sensitizer for radiation therapy of triple-negative breast cancer (TNBC). In contrast to other gold-based radio sensitizers, dumbbell-like Au-TiO2 nanoparticles (DATs) show a synergistic therapeutic effect on radiation therapy, mainly because of strong asymmetric electric coupling between the high atomic number metals and dielectric oxides at their interfaces. The generation of secondary electrons and reactive oxygen species (ROS) from DATs triggered by X-ray irradiation can significantly enhance the radiation effect. After endocytosed by cancer cells, DATs can generate a large amount of ROS under X-ray irradiation, eventually inducing cancer cell apoptosis. Significant tumor growth suppression and overall improvement in survival rate in a TNBC tumor model have been successfully demonstrated under DAT uptake for a radio-sensitized radiation therapy.

    View details for DOI 10.1021/acsnano.8b02038

    View details for Web of Science ID 000433404500095

    View details for PubMedID 29689158

  • A unified material decomposition framework for quantitative dual- and triple-energy CT imaging. Medical physics Zhao, W., Vernekohl, D., Han, F., Han, B., Peng, H., Yang, Y., Xing, L., Min, J. K. 2018

    Abstract

    PURPOSE: Many clinical applications depend critically on the accurate differentiation and classification of different types of materials in patient anatomy. This work introduces a unified framework for accurate nonlinear material decomposition and applies it, for the first time, in the concept of triple-energy CT (TECT) for enhanced material differentiation and classification as well as dual-energy CT (DECT).METHODS: We express polychromatic projection into a linear combination of line integrals of material-selective images. The material decomposition is then turned into a problem of minimizing the least-squares difference between measured and estimated CT projections. The optimization problem is solved iteratively by updating the line integrals. The proposed technique is evaluated by using several numerical phantom measurements under different scanning protocols. The triple-energy data acquisition is implemented at the scales of micro-CT and clinical CT imaging with commercial "TwinBeam" dual-source DECT configuration and a fast kV switching DECT configuration. Material decomposition and quantitative comparison with a photon counting detector and with the presence of a bow-tie filter are also performed.RESULTS: The proposed method provides quantitative material- and energy-selective images examining realistic configurations for both DECT and TECT measurements. Compared to the polychromatic kV CT images, virtual monochromatic images show superior image quality. For the mouse phantom, quantitative measurements show that the differences between gadodiamide and iodine concentrations obtained using TECT and idealized photon counting CT (PCCT) are smaller than 8 and 1mg/mL, respectively. TECT outperforms DECT for multicontrast CT imaging and is robust with respect to spectrum estimation. For the thorax phantom, the differences between the concentrations of the contrast map and the corresponding true reference values are smaller than 7mg/mL for all of the realistic configurations.CONCLUSIONS: A unified framework for both DECT and TECT imaging has been established for the accurate extraction of material compositions using currently available commercial DECT configurations. The novel technique is promising to provide an urgently needed solution for several CT-based diagnostic and therapy applications, especially for the diagnosis of cardiovascular and abdominal diseases where multicontrast imaging is involved.

    View details for PubMedID 29679500

  • Optimization of a single insertion electrode array for the creation of clinically relevant ablations using high-frequency irreversible electroporation COMPUTERS IN BIOLOGY AND MEDICINE Sano, M. B., DeWitt, M. R., Teeter, S. D., Xing, L. 2018; 95: 107–17

    Abstract

    High-frequency irreversible electroporation (H-FIRE) is an emerging ablation modality, delivering rapid bursts of bipolar microsecond-duration electrical pulses to non-thermally ablate tissue including tumors. With advantages over current electroporation techniques including mitigation of muscle stimulation and reduced susceptibility to heterogeneous tissue properties, H-FIRE may produce more uniform and predictable ablations and can potentially be delivered with a single applicator device. However, the resulting ablations tend to be smaller than those provided with equivalent energy monopolar pulse protocols. Here, we develop numerical simulations that demonstrate the potential for clinically relevant ablations with H-FIRE delivered via a single insertion technique comprised of an expandable array and a distally placed grounding pad. Based on existing in vivo data and new in vitro results, delivery of H-FIRE with a clinical IRE single electrode probe (1 cm long) is predicted to produce a 2.2 cm3 ablation while an optimized eight tine array produces a 3.2 cm3 ablation when the same H-FIRE bursts are delivered (5000 V). We demonstrate that alternative pulse protocols can be used to increase ablation volumes with this optimized array and these results indicate that in vivo investigation of a single insertion array and grounding pad are warranted.

    View details for DOI 10.1016/j.compbiomed.2018.02.009

    View details for Web of Science ID 000430768000011

    View details for PubMedID 29486332

  • Treatment of Cancer In Vitro Using Radiation and High-Frequency Bursts of Submicrosecond Electrical Pulses IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Sano, M. B., Volotskova, O., Xing, L. 2018; 65 (4): 928–35

    Abstract

    High-frequency irreversible electroporation (H-FIRE) is an emerging cancer therapy, which uses bursts of short duration, alternating polarity, high-voltage electrical pulses to focally ablate tumors. Here, we present a preliminary investigation of the combinatorial effects of H-FIRE and ionizing radiation. In vitro cell cultures were exposed to bursts of 500 ns pulses and single radiation doses of 2 or 20 Gy then analyzed for 14 days. H-FIRE and radiation therapy (RT) appear to induce different delayed cell death mechanisms and in all treatment groups combinatorial therapy resulted in lower overall viabilities. These results indicate that in vivo investigation of the antitumor efficacy of combined H-FIRE and RT is warranted.

    View details for DOI 10.1109/TBME.2017.2734887

    View details for Web of Science ID 000428526000023

    View details for PubMedID 28783621

  • Selection of external beam radiotherapy approaches for precise and accurate cancer treatment JOURNAL OF RADIATION RESEARCH Shirato, H., Quynh-Thu Le, Kobashi, K., Prayongrat, A., Takao, S., Shimizu, S., Giaccia, A., Xing, L., Umegaki, K. 2018; 59: I2–I10

    Abstract

    Physically precise external-beam radiotherapy (EBRT) technologies may not translate to the best outcome in individual patients. On the other hand, clinical considerations alone are often insufficient to guide the selection of a specific EBRT approach in patients. We examine the ways in which to compare different EBRT approaches based on physical, biological and clinical considerations, and how they can be enhanced with the addition of biophysical models and machine-learning strategies. The process of selecting an EBRT modality is expected to improve in tandem with knowledge-based treatment planning.

    View details for PubMedID 29373709

    View details for PubMedCentralID PMC5868193

  • Radiomics and radiogenomics for precision radiotherapy JOURNAL OF RADIATION RESEARCH Wu, J., Tha, K., Xing, L., Li, R. 2018; 59: I25–I31

    Abstract

    Imaging plays an important role in the diagnosis and staging of cancer, as well as in radiation treatment planning and evaluation of therapeutic response. Recently, there has been significant interest in extracting quantitative information from clinical standard-of-care images, i.e. radiomics, in order to provide a more comprehensive characterization of image phenotypes of the tumor. A number of studies have demonstrated that a deeper radiomic analysis can reveal novel image features that could provide useful diagnostic, prognostic or predictive information, improving upon currently used imaging metrics such as tumor size and volume. Furthermore, these imaging-derived phenotypes can be linked with genomic data, i.e. radiogenomics, in order to understand their biological underpinnings or further improve the prediction accuracy of clinical outcomes. In this article, we will provide an overview of radiomics and radiogenomics, including their rationale, technical and clinical aspects. We will also present some examples of the current results and some emerging paradigms in radiomics and radiogenomics for clinical oncology, with a focus on potential applications in radiotherapy. Finally, we will highlight the challenges in the field and suggest possible future directions in radiomics to maximize its potential impact on precision radiotherapy.

    View details for PubMedID 29385618

    View details for PubMedCentralID PMC5868194

  • Imaging cellular pharmacokinetics of F-18-FDG and 6-NBDG uptake by inflammatory and stem cells PLOS ONE Zaman, R. T., Tuerkcan, S., Mahmoudi, M., Saito, T., Yang, P. C., Chin, F. T., McConnell, M. V., Xing, L. 2018; 13 (2): e0192662

    Abstract

    Myocardial infarction (MI) causes significant loss of cardiomyocytes, myocardial tissue damage, and impairment of myocardial function. The inability of cardiomyocytes to proliferate prevents the heart from self-regeneration. The treatment for advanced heart failure following an MI is heart transplantation despite the limited availability of the organs. Thus, stem-cell-based cardiac therapies could ultimately prevent heart failure by repairing injured myocardium that reverses cardiomyocyte loss. However, stem-cell-based therapies lack understanding of the mechanisms behind a successful therapy, including difficulty tracking stem cells to provide information on cell migration, proliferation and differentiation. In this study, we have investigated the interaction between different types of stem and inflammatory cells and cell-targeted imaging molecules, 18F-FDG and 6-NBDG, to identify uptake patterns and pharmacokinetics in vitro.Macrophages (both M1 and M2), human induced pluripotent stem cells (hiPSCs), and human amniotic mesenchymal stem cells (hAMSCs) were incubated with either 18F-FDG or 6-NBDG. Excess radiotracer and fluorescence were removed and a 100 μm-thin CdWO4 scintillator plate was placed on top of the cells for radioluminescence microscopy imaging of 18F-FDG uptake, while no scintillator was needed for fluorescence imaging of 6-NBDG uptake. Light produced following beta decay was imaged with a highly sensitive inverted microscope (LV200, Olympus) and an Electron Multiplying Charge-Couple Device (EM-CCD) camera. Custom-written software was developed in MATLAB for image processing.The average cellular activity of 18F-FDG in a single cell of hAMSCs (0.670±0.028 fCi/μm2, P = 0.001) was 20% and 36% higher compared to uptake in hiPSCs (0.540±0.026 fCi/μm2, P = 0.003) and macrophages (0.430±0.023 fCi/μm2, P = 0.002), respectively. hAMSCs exhibited the slowest influx (0.210 min-1) but the fastest efflux (0.327 min-1) rate compared to the other tested cell lines for 18F-FDG. This cell line also has the highest phosphorylation but exhibited the lowest rate of de-phosphorylation. The uptake pattern for 6-NBDG was very different in these three cell lines. The average cellular activity of 6-NBDG in a single cell of macrophages (0.570±0.230 fM/μm2, P = 0.004) was 38% and 14% higher compared to hiPSCs (0.350±0.160 fM/μm2, P = 0.001) and hAMSCs (0.490±0.028 fM/μm2, P = 0.006), respectively. The influx (0.276 min-1), efflux (0.612 min-1), phosphorylation (0.269 min-1), and de-phosphorylation (0.049 min-1) rates were also highest for macrophages compared to the other two tested cell lines.hAMSCs were found to be 2-3× more sensitive to 18F-FDG molecule compared to hiPSCs/macrophages. However, macrophages exhibited the most sensitivity towards 6-NBDG. Based on this result, hAMSCs targeted with 18F-FDG could be more suitable for understanding the mechanisms behind successful therapy for treating MI patients by gathering information on cell migration, proliferation and differentiation.

    View details for PubMedID 29462173

  • Strategies for prediction and mitigation of radiation-induced liver toxicity. Journal of radiation research Toesca, D. A., Ibragimov, B. n., Koong, A. J., Xing, L. n., Koong, A. C., Chang, D. T. 2018

    Abstract

    Although well described in the 1960s, liver toxicity secondary to radiation therapy, commonly known as radiation-induced liver disease (RILD), remains a major challenge. RILD encompasses two distinct clinical entities, a 'classic' form, composed of anicteric hepatomegaly, ascites and elevated alkaline phosphatase; and a 'non-classic' form, with liver transaminases elevated to more than five times the reference value, or worsening of liver metabolic function represented as an increase of 2 or more points in the Child-Pugh score classification. The risk of occurrence of RILD has historically limited the applicability of radiation for the treatment of liver malignancies. With the development of 3D conformal radiation therapy, which allowed for partial organ irradiation based on computed tomography treatment planning, there has been a resurgence of interest in the use of liver irradiation. Since then, a large body of evidence regarding the liver tolerance to conventionally fractionated radiation has been produced, but severe liver toxicities has continued to be reported. More recently, improvements in diagnostic imaging, radiation treatment planning technology and delivery systems have prompted the development of stereotactic body radiotherapy (SBRT), by which high doses of radiation can be delivered with high target accuracy and a steep dose gradient at the tumor - normal tissue interface, offering an opportunity of decreasing toxicity rates while improving tumor control. Here, we present an overview of the role SBRT has played in the management of liver tumors, addressing the challenges and opportunities to reduce the incidence of RILD, such as adaptive approaches and machine-learning-based predictive models.

    View details for PubMedID 29432550

  • RIIS-DenseNet: Rotation-Invariant and Image Similarity Constrained Densely Connected Convolutional Network for Polyp Detection Yuan, Y., Qin, W., Ibragimov, B., Han, B., Xing, L., Frangi, A. F., Schnabel, J. A., Davatzikos, C., AlberolaLopez, C., Fichtinger, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 620–28
  • Line-Enhanced Deformable Registration of Pulmonary Computed Tomography Images Before and After Radiation Therapy With Radiation-Induced Fibrosis TECHNOLOGY IN CANCER RESEARCH & TREATMENT King, M., Sensakovic, W. F., Maxim, P., Diehn, M., Loo, B. W., Xing, L. 2018; 17
  • Rare-Earth-Doped Nanoparticles for Short-Wave Infrared Fluorescence Bioimaging and Molecular Targeting of alphaVbeta3-Expressing Tumors. Molecular imaging Naczynski, D. J., Stafford, J. H., Turkcan, S., Jenkins, C., Koh, A. L., Sun, C., Xing, L. 2018; 17: 1536012118799131

    Abstract

    The use of short-wave infrared (SWIR) light for fluorescence bioimaging offers the advantage of reduced photon scattering and improved tissue penetration compared to traditional shorter wavelength imaging approaches. While several nanomaterials have been shown capable of generating SWIR emissions, rare-earth-doped nanoparticles (REs) have emerged as an exceptionally bright and biocompatible class of SWIR emitters. Here, we demonstrate SWIR imaging of REs for several applications, including lymphatic mapping, real-time monitoring of probe biodistribution, and molecular targeting of the alphavbeta3 integrin in a tumor model. We further quantified the resolution and depth penetration limits of SWIR light emitted by REs in a customized imaging unit engineered for SWIR imaging of live small animals. Our results indicate that SWIR light has broad utility for preclinical biomedical imaging and demonstrates the potential for molecular imaging using targeted REs.

    View details for PubMedID 30246593

  • A computation study on an integrated alternating direction method of multipliers for large scale optimization OPTIMIZATION LETTERS Zarepisheh, M., Xing, L., Ye, Y. 2018; 12 (1): 3–15
  • Line-Enhanced Deformable Registration of Pulmonary Computed Tomography Images Before and After Radiation Therapy With Radiation-Induced Fibrosis. Technology in cancer research & treatment King, M., Sensakovic, W. F., Maxim, P., Diehn, M., Loo, B. W., Xing, L. 2018; 17: 1533034617749419

    Abstract

    PURPOSE: The deformable registration of pulmonary computed tomography images before and after radiation therapy is challenging due to anatomic changes from radiation fibrosis. We hypothesize that a line-enhanced registration algorithm can reduce landmark error over the entire lung, including the irradiated regions, when compared to an intensity-based deformable registration algorithm.MATERIALS: Two intensity-based B-spline deformable registration algorithms of pre-radiation therapy and post-radiation therapy images were compared. The first was a control intensity-based algorithm that utilized computed tomography images without modification. The second was a line enhancement algorithm that incorporated a Hessian-based line enhancement filter prior to deformable image registration. Registrations were evaluated based on the landmark error between user-identified landmark pairs and the overlap ratio.RESULTS: Twenty-one patients with pre-radiation therapy and post-radiation therapy scans were included. The median time interval between scans was 1.2 years (range: 0.3-3.3 years). Median landmark errors for the line enhancement algorithm were significantly lower than those for the control algorithm over the entire lung (1.67 vs 1.83 mm; P < .01), as well as within the 0 to 5 Gy (1.40 vs 1.57; P < .01) and >5 Gy (2.25 vs 3.31; P < .01) dose intervals. The median lung mask overlap ratio for the line enhancement algorithm (96.2%) was greater than that for the control algorithm (95.8%; P < .01). Landmark error within the >5 Gy dose interval demonstrated a significant inverse relationship with post-radiation therapy fibrosis enhancement after line enhancement filtration (Pearson correlation coefficient = -0.48; P = .03).CONCLUSION: The line enhancement registration algorithm is a promising method for registering images before and after radiation therapy.

    View details for PubMedID 29343206

  • Reduction of Muscle Contractions during Irreversible Electroporation Therapy Using High-Frequency Bursts of Alternating Polarity Pulses: A Laboratory Investigation in an ExVivo Swine Model. Journal of vascular and interventional radiology : JVIR Sano, M. B., Fan, R. E., Cheng, K., Saenz, Y., Sonn, G. A., Hwang, G. L., Xing, L. 2018; 29 (6): 893

    Abstract

    PURPOSE: To compare the intensity of muscle contractions in irreversible electroporation (IRE) treatments when traditional IRE and high-frequency IRE (H-FIRE) waveforms are used in combination with a single applicator and distal grounding pad (A+GP) configuration.MATERIALS AND METHODS: An exvivo in situ porcine model was used to compare muscle contractions induced by traditional monopolar IRE waveforms vs high-frequency bipolar IRE waveforms. Pulses with voltages between 200 and 5,000 V were investigated, and muscle contractions were recorded by using accelerometers placed on or near the applicators.RESULTS: H-FIRE waveforms reduced the intensity of muscle contractions in comparison with traditional monopolar IRE pulses. A high-energy burst of 2-mus alternating-polarity pulses energized for 200 mus at 4,500 V produced less intense muscle contractions than traditional IRE pulses, which were 25-100 mus in duration at 3,000 V.CONCLUSIONS: H-FIRE appears to be an effective technique to mitigate the muscle contractions associated with traditional IRE pulses. This may enable the use of voltages greater than 3,000 V necessary for the creation of large ablations invivo.

    View details for PubMedID 29628296

  • Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation. Physics in medicine and biology Qin, W. n., Wu, J. n., Han, F. n., Yuan, Y. n., Zhao, W. n., Ibragimov, B. n., Gu, J. n., Xing, L. n. 2018; 63 (9): 095017

    Abstract

    Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast between liver and its surrounding organs, and its highly deformable shape. The purpose of this work is to develop a novel superpixel-based and boundary sensitive convolutional neural network (SBBS-CNN) pipeline for automated liver segmentation. The entire CT images were first partitioned into superpixel regions, where nearby pixels with similar CT number were aggregated. Secondly, we converted the conventional binary segmentation into a multinomial classification by labeling the superpixels into three classes: interior liver, liver boundary, and non-liver background. By doing this, the boundary region of the liver was explicitly identified and highlighted for the subsequent classification. Thirdly, we computed an entropy-based saliency map for each CT volume, and leveraged this map to guide the sampling of image patches over the superpixels. In this way, more patches were extracted from informative regions (e.g. the liver boundary with irregular changes) and fewer patches were extracted from homogeneous regions. Finally, deep CNN pipeline was built and trained to predict the probability map of the liver boundary. We tested the proposed algorithm in a cohort of 100 patients. With 10-fold cross validation, the SBBS-CNN achieved mean Dice similarity coefficients of 97.31  ±  0.36% and average symmetric surface distance of 1.77  ±  0.49 mm. Moreover, it showed superior performance in comparison with state-of-art methods, including U-Net, pixel-based CNN, active contour, level-sets and graph-cut algorithms. SBBS-CNN provides an accurate and effective tool for automated liver segmentation. It is also envisioned that the proposed framework is directly applicable in other medical image segmentation scenarios.

    View details for PubMedID 29633960

  • Synthesis, Characterization, and Biomedical Applications of a Targeted Dual-Modal Near-Infrared-II Fluorescence and Photoacoustic Imaging Nanoprobe ACS NANO Cheng, K., Chen, H., Jenkins, C. H., Zhang, G., Zhao, W., Zhang, Z., Han, F., Fung, J., Yang, M., Jiang, Y., Xing, L., Cheng, Z. 2017; 11 (12): 12276–91

    Abstract

    Our development of multifunctional dual-modal imaging probes aims to integrate the benefits from both second near-infrared (NIR-II) fluorescence (1000-1700 nm) and photoacoustic imaging with an ultimate goal of improving overall cancer diagnosis efficacy. Herein we designed a donor-acceptor chromophore based nanoparticle (DAP) as a dual-modal image contrast agent has strong absorption in the NIR-I window and a strong fluorescence emission peak in the NIR-II region. The dual-modal DAPs composed of D-π-A-π-D-type chromophores were PEGylated through nanoprecipitation. The multifunctional DAP surface was thus available for subsequent bioconjugation of EGFR Affibody (Ac-Cys-ZEGFR:1907) to target EGFR-positive cancers. The Affibody-conjugated DAPs appeared as highly monodisperse nanoparticles (∼30 nm) with strong absorption in the NIR-I window (at ca. 680 nm) and an extremely high fluorescence in the NIR-II region (maximum peak at 1000 nm). Consequently, the Affibody-DAPs show significantly enhanced photoacoustic and NIR-II fluorescence contrast effects in both in vitro and in vivo experiments. Moreover, the Affibody-DAPs have the capability to selectively target EGFR-positive tumors in an FTC-133 subcutaneous mouse model with relatively high photoacoustic and fluorescent signals. By taking advantage of high spatial resolution and excellent temporal resolution, photoacoustic/NIR-II fluorescence imaging with targeted dual-modal contrast agents allows us to specifically image and detect various cancers and diseases in an accurate manner.

    View details for PubMedID 29202225

  • A new sparse optimization scheme for simultaneous beam angle and fluence map optimization in radiotherapy planning PHYSICS IN MEDICINE AND BIOLOGY Liu, H., Dong, P., Xing, L. 2017; 62 (16): 6428–45

    Abstract

    [Formula: see text]-minimization-based sparse optimization was employed to solve the beam angle optimization (BAO) in intensity-modulated radiation therapy (IMRT) planning. The technique approximates the exact BAO formulation with efficiently computable convex surrogates, leading to plans that are inferior to those attainable with recently proposed gradient-based greedy schemes. In this paper, we alleviate/reduce the nontrivial inconsistencies between the [Formula: see text]-based formulations and the exact BAO model by proposing a new sparse optimization framework based on the most recent developments in group variable selection. We propose the incorporation of the group-folded concave penalty (gFCP) as a substitution to the [Formula: see text]-minimization framework. The new formulation is then solved by a variation of an existing gradient method. The performance of the proposed scheme is evaluated by both plan quality and the computational efficiency using three IMRT cases: a coplanar prostate case, a coplanar head-and-neck case, and a noncoplanar liver case. Involved in the evaluation are two alternative schemes: the [Formula: see text]-minimization approach and the gradient norm method (GNM). The gFCP-based scheme outperforms both counterpart approaches. In particular, gFCP generates better plans than those obtained using the [Formula: see text]-minimization for all three cases with a comparable computation time. As compared to the GNM, the gFCP improves both the plan quality and computational efficiency. The proposed gFCP-based scheme provides a promising framework for BAO and promises to improve both planning time and plan quality.

    View details for PubMedID 28726687

  • Low-dose 4D cone-beam CT via joint spatiotemporal regularization of tensor framelet and nonlocal total variation PHYSICS IN MEDICINE AND BIOLOGY Han, H., Gao, H., Xing, L. 2017; 62 (16): 6408–27

    Abstract

    Excessive radiation exposure is still a major concern in 4D cone-beam computed tomography (4D-CBCT) due to its prolonged scanning duration. Radiation dose can be effectively reduced by either under-sampling the x-ray projections or reducing the x-ray flux. However, 4D-CBCT reconstruction under such low-dose protocols is prone to image artifacts and noise. In this work, we propose a novel joint regularization-based iterative reconstruction method for low-dose 4D-CBCT. To tackle the under-sampling problem, we employ spatiotemporal tensor framelet (STF) regularization to take advantage of the spatiotemporal coherence of the patient anatomy in 4D images. To simultaneously suppress the image noise caused by photon starvation, we also incorporate spatiotemporal nonlocal total variation (SNTV) regularization to make use of the nonlocal self-recursiveness of anatomical structures in the spatial and temporal domains. Under the joint STF-SNTV regularization, the proposed iterative reconstruction approach is evaluated first using two digital phantoms and then using physical experiment data in the low-dose context of both under-sampled and noisy projections. Compared with existing approaches via either STF or SNTV regularization alone, the presented hybrid approach achieves improved image quality, and is particularly effective for the reconstruction of low-dose 4D-CBCT data that are not only sparse but noisy.

    View details for PubMedID 28726684

  • Segmentation of Pathological Structures by Landmark-Assisted Deformable Models IEEE TRANSACTIONS ON MEDICAL IMAGING Ibragimov, B., Korez, R., Likar, B., Pernus, F., Xing, L., Vrtovec, T. 2017; 36 (7): 1457–69

    Abstract

    Computerized segmentation of pathological structures in medical images is challenging, as, in addition to unclear image boundaries, image artifacts, and traces of surgical activities, the shape of pathological structures may be very different from the shape of normal structures. Even if a sufficient number of pathological training samples are collected, statistical shape modeling cannot always capture shape features of pathological samples as they may be suppressed by shape features of a considerably larger number of healthy samples. At the same time, landmarking can be efficient in analyzing pathological structures but often lacks robustness. In this paper, we combine the advantages of landmark detection and deformable models into a novel supervised multi-energy segmentation framework that can efficiently segment structures with pathological shape. The framework adopts the theory of Laplacian shape editing, that was introduced in the field of computer graphics, so that the limitations of statistical shape modeling are avoided. The performance of the proposed framework was validated by segmenting fractured lumbar vertebrae from 3-D computed tomography images, atrophic corpora callosa from 2-D magnetic resonance (MR) cross-sections and cancerous prostates from 3D MR images, resulting respectively in a Dice coefficient of 84.7 ± 5.0%, 85.3 ± 4.8% and 78.3 ± 5.1%, and boundary distance of 1.14 ± 0.49mm, 1.42 ± 0.45mm and 2.27 ± 0.52mm. The obtained results were shown to be superior in comparison to existing deformable model-based segmentation algorithms.

    View details for DOI 10.1109/TMI.2017.2667578

    View details for Web of Science ID 000404981000008

    View details for PubMedID 28207388

  • Using a handheld stereo depth camera to overcome limited field-of-view in simulation imaging for radiation therapy treatment planning MEDICAL PHYSICS Jenkins, C., Xing, L., Yu, A. 2017; 44 (5): 1857-1864

    Abstract

    A correct body contour is essential for reliable treatment planning in radiation therapy. While modern medical imaging technologies provide highly accurate patient modeling, there are times when a patient's anatomy cannot be fully captured or there is a lack of easy access to computed tomography (CT) simulation. Here, we provide a practical solution to the surface contour truncation problem by using a handheld stereo depth camera (HSDC) to obtain the missing surface anatomy and a surface-surface image registration to stich the surface data into the CT dataset for treatment planning.For a subject with truncated simulation CT images, a HSDC is used to capture the surface information of the truncated anatomy. A mesh surface model is created using a software tool provided by the camera manufacturer. A surface-to-surface registration technique is used to merge the mesh model with the CT and fill in the missing surface information thereby obtaining a complete surface model of the subject. To evaluate the accuracy of the proposed approach, experiments were performed with the following steps. First, we selected three previously treated patients and fabricated a phantom mimicking each patient using the corresponding CT images and a 3D printer. Second, we removed part of the CT images of each patient to create hypothetical cases with image truncations. Next, a HSDC was used to image the 3D-printed phantoms and the HSDC-derived surface models were registered with the hypothetically truncated CT images. The contours obtained using the approach were then compared with the ground truth contours derived from the original simulation CT without image truncation. The distance between the two contours was calculated in order to evaluate the accuracy of the method. Finally, the dosimetric impact of the approach is assessed by comparing the volume within the 95% isodose line and global maximum dose (Dmax ) computed based on the two surface contours for the breast case that exhibited the largest contour variation in the treated breast.A systematic strategy of using a 3D HSDC to compensate for missing surface information caused by the truncation of CT images was established. Our study showed that the proposed technique was able to reliably provide the full contours for treatment planning in the case of severe CT image truncation(s). The root-mean-square error for the registration between the aligned HDSC surface model and the ground truth data was found to be 2.1 mm. The average distance between the two models was 0.4 ± 1.7 mm (mean ± SD). Maximum deviations occurred in areas of high concavity or when the skin was close to the couch. The breast tissue covered by 95% isodose line decreased by 3% and Dmax increased by 0.2% with the use of the HSDC model.The use of HSDC for obtaining missing surface data during simulation has a number of advantages, such as, ease of use, low cost, and no additional ionizing radiation. It may provide a clinically practical solution to deal with the longstanding problem of CT image truncations in radiation therapy treatment planning.

    View details for DOI 10.1002/mp.12207

    View details for Web of Science ID 000401154000024

    View details for PubMedID 28295413

  • Harnessing Radioluminescence and Sound to Reveal Molecular Pathology of Atherosclerotic Plaques Zaman, R., Yousefi, S., Long, S., Contag, C., Gambhir, S., Khuri-Yakub, B., Xing, L. SOC NUCLEAR MEDICINE INC. 2017
  • Using measurable dosimetric quantities to characterize the inter-structural tradeoff in inverse planning. Physics in medicine and biology Liu, H., Dong, P., Xing, L. 2017

    Abstract

    Traditional inverse planning relies on the use of weighting factors to balance the conflicting requirements of different structures. The manual trial-and-error determination of the weighting factors has long been recognized as a time-consuming part of treatment planning. The purpose of this work is to develop an inverse planning framework that parameterizes the dosimetric tradeoff among the structures with physically meaningful quantities to simplify the search for clinically sensible plans. In this formalism, instead of using the weighting factors, permissible variation range of the prescription dose or dose volume histogram (DVH) of the involved structures are used to characterize the "importance" of the structures. The inverse planning is then formulated into a convex feasibility problem, called the dosimetric variation-controlled model (DVCM), whose goal is to generate plans with dosimetric or DVH variations of the structures consistent with the pre-specified values. For simplicity, the dosimetric variation range for a structure is extracted from a library of previous cases who possess similar anatomy and prescription. A two-phase procedure (TPP) is designed to solve the model. The first Phase identifies a physically feasible plan to satisfy the prescribed dosimetric variation, and the second phase automatically improves the plan in case there is room for further improvement. The proposed technique is applied to plan two prostate cases and two head-and-neck cases and the results are compared with that obtained using a conventional CVaR approach and with a moment-based optimization scheme. Our results show that the strategy is able to generate clinically sensible plans with little trial and error. In all cases, the TPP generates a very competitive plan as compared to those obtained using the alternative approaches. Particularly, in the planning of one of the head-and-neck case, the TPP leads to a non-trivial improvement in the resultant dose distribution - the fractional volumes receiving a dose above 20 Gy for spinal cord are reduced by more than 40% when compared to the alternative schemes, while maintaining the same PTV coverage. With physically more meaningful modeling of the inter-structural tradeoff, the reported technique enables us to substantially reduce the need for trial-and-error adjustment of the model parameters. The new formalism also opens new opportunities for incorporating prior knowledge to facilitate the treatment planning process.

    View details for DOI 10.1088/1361-6560/aa6fcb

    View details for PubMedID 28447959

  • Segmentation-free x-ray energy spectrum estimation for computed tomography using dual-energy material decomposition JOURNAL OF MEDICAL IMAGING Zhao, W., Xing, L., Zhang, Q., Xie, Q., Niu, T. 2017; 4 (2): 023506

    Abstract

    An x-ray energy spectrum plays an essential role in computed tomography (CT) imaging and related tasks. Because of the high photon flux of clinical CT scanners, most of the spectrum estimation methods are indirect and usually suffer from various limitations. In this study, we aim to provide a segmentation-free, indirect transmission measurement-based energy spectrum estimation method using dual-energy material decomposition. The general principle of this method is to minimize the quadratic error between the polychromatic forward projection and the raw projection to calibrate a set of unknown weights, which are used to express the unknown spectrum together with a set of model spectra. The polychromatic forward projection is performed using material-specific images, which are obtained using dual-energy material decomposition. The algorithm was evaluated using numerical simulations, experimental phantom data, and realistic patient data. The results show that the estimated spectrum matches the reference spectrum quite well and the method is robust. Extensive studies suggest that the method provides an accurate estimate of the CT spectrum without dedicated physical phantom and prolonged workflow. This paper may be attractive for CT dose calculation, artifacts reduction, polychromatic image reconstruction, and other spectrum-involved CT applications.

    View details for DOI 10.1117/1.JMI.4.2.023506

    View details for Web of Science ID 000405944600014

    View details for PubMedID 28680909

    View details for PubMedCentralID PMC5492812

  • Binary moving-blocker-based scatter correction in cone-beam computed tomography with width-truncated projections: proof of concept PHYSICS IN MEDICINE AND BIOLOGY Lee, H., Fahimian, B. P., Xing, L. 2017; 62 (6): 2176-2193

    Abstract

    This paper proposes a binary moving-blocker (BMB)-based technique for scatter correction in cone-beam computed tomography (CBCT). In concept, a beam blocker consisting of lead strips, mounted in front of the x-ray tube, moves rapidly in and out of the beam during a single gantry rotation. The projections are acquired in alternating phases of blocked and unblocked cone beams, where the blocked phase results in a stripe pattern in the width direction. To derive the scatter map from the blocked projections, 1D B-Spline interpolation/extrapolation is applied by using the detected information in the shaded regions. The scatter map of the unblocked projections is corrected by averaging two scatter maps that correspond to their adjacent blocked projections. The scatter-corrected projections are obtained by subtracting the corresponding scatter maps from the projection data and are utilized to generate the CBCT image by a compressed-sensing (CS)-based iterative reconstruction algorithm. Catphan504 and pelvis phantoms were used to evaluate the method's performance. The proposed BMB-based technique provided an effective method to enhance the image quality by suppressing scatter-induced artifacts, such as ring artifacts around the bowtie area. Compared to CBCT without a blocker, the spatial nonuniformity was reduced from 9.1% to 3.1%. The root-mean-square error of the CT numbers in the regions of interest (ROIs) was reduced from 30.2 HU to 3.8 HU. In addition to high resolution, comparable to that of the benchmark image, the CS-based reconstruction also led to a better contrast-to-noise ratio in seven ROIs. The proposed technique enables complete scatter-corrected CBCT imaging with width-truncated projections and allows reducing the acquisition time to approximately half. This work may have significant implications for image-guided or adaptive radiation therapy, where CBCT is often used.

    View details for DOI 10.1088/1361-6560/aa5913

    View details for Web of Science ID 000395801000002

    View details for PubMedID 28079527

  • Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach. International journal of radiation oncology, biology, physics Ren, S., Hara, W., Wang, L., Buyyounouski, M. K., Le, Q., Xing, L., Li, R. 2017; 97 (4): 849-857

    Abstract

    To develop a reliable method to estimate electron density based on anatomic magnetic resonance imaging (MRI) of the brain.We proposed a unifying multi-atlas approach for electron density estimation based on standard T1- and T2-weighted MRI. First, a composite atlas was constructed through a voxelwise matching process using multiple atlases, with the goal of mitigating effects of inherent anatomic variations between patients. Next we computed for each voxel 2 kinds of conditional probabilities: (1) electron density given its image intensity on T1- and T2-weighted MR images; and (2) electron density given its spatial location in a reference anatomy, obtained by deformable image registration. These were combined into a unifying posterior probability density function using the Bayesian formalism, which provided the optimal estimates for electron density. We evaluated the method on 10 patients using leave-one-patient-out cross-validation. Receiver operating characteristic analyses for detecting different tissue types were performed.The proposed method significantly reduced the errors in electron density estimation, with a mean absolute Hounsfield unit error of 119, compared with 140 and 144 (P<.0001) using conventional T1-weighted intensity and geometry-based approaches, respectively. For detection of bony anatomy, the proposed method achieved an 89% area under the curve, 86% sensitivity, 88% specificity, and 90% accuracy, which improved upon intensity and geometry-based approaches (area under the curve: 79% and 80%, respectively).The proposed multi-atlas approach provides robust electron density estimation and bone detection based on anatomic MRI. If validated on a larger population, our work could enable the use of MRI as a primary modality for radiation treatment planning.

    View details for DOI 10.1016/j.ijrobp.2016.11.053

    View details for PubMedID 28244422

  • Development of an autonomous treatment planning strategy for radiation therapy with effective use of population-based prior data. Medical physics Wang, H., Dong, P., Liu, H., Xing, L. 2017; 44 (2): 389-396

    Abstract

    Current treatment planning remains a costly and labor intensive procedure and requires multiple trial-and-error adjustments of system parameters such as the weighting factors and prescriptions. The purpose of this work is to develop an autonomous treatment planning strategy with effective use of prior knowledge and in a clinically realistic treatment planning platform to facilitate radiation therapy workflow.Our technique consists of three major components: (i) a clinical treatment planning system (TPS); (ii) a formulation of decision-function constructed using an assemble of prior treatment plans; (iii) a plan evaluator or decision-function and an outer-loop optimization independent of the clinical TPS to assess the TPS-generated plan and to drive the search toward a solution optimizing the decision-function. Microsoft (MS) Visual Studio Coded UI is applied to record some common planner-TPS interactions as subroutines for querying and interacting with the TPS. These subroutines are called back in the outer-loop optimization program to navigate the plan selection process through the solution space iteratively. The utility of the approach is demonstrated by using clinical prostate and head-and-neck cases.An autonomous treatment planning technique with effective use of an assemble of prior treatment plans is developed to automatically maneuver the clinical treatment planning process in the platform of a commercial TPS. The process mimics the decision-making process of a human planner and provides a clinically sensible treatment plan automatically, thus reducing/eliminating the tedious manual trial-and-errors of treatment planning. It is found that the prostate and head-and-neck treatment plans generated using the approach compare favorably with that used for the patients' actual treatments.Clinical inverse treatment planning process can be automated effectively with the guidance of an assemble of prior treatment plans. The approach has the potential to significantly improve the radiation therapy workflow.

    View details for DOI 10.1002/mp.12058

    View details for PubMedID 28133746

  • Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Medical physics Ibragimov, B., Xing, L. 2017; 44 (2): 547-557

    Abstract

    Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs in HaN CT images, and compared its performance against state-of-the-art automated segmentation algorithms, commercial software, and interobserver variability.Convolutional neural networks (CNNs)-a concept from the field of deep learning-were used to study consistent intensity patterns of OARs from training CT images and to segment the OAR in a previously unseen test CT image. For CNN training, we extracted a representative number of positive intensity patches around voxels that belong to the OAR of interest in training CT images, and negative intensity patches around voxels that belong to the surrounding structures. These patches then passed through a sequence of CNN layers that captured local image features such as corners, end-points, and edges, and combined them into more complex high-order features that can efficiently describe the OAR. The trained network was applied to classify voxels in a region of interest in the test image where the corresponding OAR is expected to be located. We then smoothed the obtained classification results by using Markov random fields algorithm. We finally extracted the largest connected component of the smoothed voxels classified as the OAR by CNN, performed dilate-erode operations to remove cavities of the component, which resulted in segmentation of the OAR in the test image.The performance of CNNs was validated on segmentation of spinal cord, mandible, parotid glands, submandibular glands, larynx, pharynx, eye globes, optic nerves, and optic chiasm using 50 CT images. The obtained segmentation results varied from 37.4% Dice coefficient (DSC) for chiasm to 89.5% DSC for mandible. We also analyzed the performance of state-of-the-art algorithms and commercial software reported in the literature, and observed that CNNs demonstrate similar or superior performance on segmentation of spinal cord, mandible, parotid glands, larynx, pharynx, eye globes, and optic nerves, but inferior performance on segmentation of submandibular glands and optic chiasm.We concluded that convolution neural networks can accurately segment most of OARs using a representative database of 50 HaN CT images. At the same time, inclusion of additional information, for example, MR images, may be beneficial to some OARs with poorly visible boundaries.

    View details for DOI 10.1002/mp.12045

    View details for PubMedID 28205307

    View details for PubMedCentralID PMC5383420

  • Asymmetric Waveforms Decrease Lethal Thresholds in High Frequency Irreversible Electroporation Therapies SCIENTIFIC REPORTS Sano, M. B., Fan, R. E., Xing, L. 2017; 7

    Abstract

    Irreversible electroporation (IRE) is a promising non-thermal treatment for inoperable tumors which uses short (50-100 μs) high voltage monopolar pulses to disrupt the membranes of cells within a well-defined volume. Challenges with IRE include complex treatment planning and the induction of intense muscle contractions. High frequency IRE (H-FIRE) uses bursts of ultrashort (0.25-5 μs) alternating polarity pulses to produce more predictable ablations and alleviate muscle contractions associated with IRE. However, H-FIRE generally ablates smaller volumes of tissue than IRE. This study shows that asymmetric H-FIRE waveforms can be used to create ablation volumes equivalent to standard IRE treatments. Lethal thresholds (LT) of 505 V/cm and 1316 V/cm were found for brain cancer cells when 100 μs IRE and 2 μs symmetric H-FIRE waveforms were used. In contrast, LT as low as 536 V/cm were found for 2 μs asymmetric H-FIRE waveforms. Reversible electroporation thresholds were 54% lower than LTs for symmetric waveforms and 33% lower for asymmetric waveforms indicating that waveform symmetry can be used to tune the relative sizes of reversible and irreversible ablation zones. Numerical simulations predicted that asymmetric H-FIRE waveforms are capable of producing ablation volumes which were 5.8-6.3x larger than symmetric H-FIRE waveforms indicating that in vivo investigation of asymmetric waveforms is warranted.

    View details for DOI 10.1038/srep40747

    View details for Web of Science ID 000392345600001

    View details for PubMedID 28106146

    View details for PubMedCentralID PMC5247773

  • Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours PHYSICS IN MEDICINE AND BIOLOGY Li, D., Liu, L., Chen, J., Li, H., Yin, Y., Ibragimov, B., Xing, L. 2017; 62 (1): 272-288

    Abstract

    Atlas-based segmentation utilizes a library of previously delineated contours of similar cases to facilitate automatic segmentation. The problem, however, remains challenging because of limited information carried by the contours in the library. In this studying, we developed a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. This study presented a new concept of atlas based segmentation method. Instead of using the complete volume of the target organs, only information along the organ contours from the atlas images was used for guiding segmentation of the new image. In setting up an atlas-based library, we included not only the coordinates of contour points, but also the image features adjacent to the contour. In this work, 139 CT images with normal appearing livers collected for radiotherapy treatment planning were used to construct the library. The CT images within the library were first registered to each other using affine registration. The nonlinear narrow shell was generated alongside the object contours of registered images. Matching voxels were selected inside common narrow shell image features of a library case and a new case using a speed-up robust features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the new image by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy optimization within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by physicians. A novel atlas-based segmentation technique with inclusion of neighborhood image features through the introduction of a narrow-shell surrounding the target objects was established. Application of the technique to 30 liver cases suggested that the technique was capable to reliably segment liver cases from CT, 4D-CT, and CBCT images with little human interaction. The accuracy and speed of the proposed method are quantitatively validated by comparing automatic segmentation results with the manual delineation results. The Jaccard similarity metric between the automatically generated liver contours obtained by the proposed method and the physician delineated results are on an average 90%-96% for planning images. Incorporation of image features into the library contours improves the currently available atlas-based auto-contouring techniques and provides a clinically practical solution for auto-segmentation. The proposed mountainous narrow shell atlas based method can achieve efficient automatic liver propagation for CT, 4D-CT and CBCT images with following treatment planning and should find widespread application in future treatment planning systems.

    View details for DOI 10.1088/1361-6560/62/1/272

    View details for PubMedID 27991439

  • Pixel response-based EPID dosimetry for patient specific QA JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS Han, B., Ding, A., Lu, M., Xing, L. 2017; 18 (1): 9-17

    Abstract

    Increasing use of high dose rate, flattening filter free (FFF), and/or small-sized field beams presents a significant challenge to the medical physics community. In this work, we develop a strategy of using a high spatial resolution and high frame rate amorphous silicon flat panel electronic portal imaging device (EPID) for dosimetric measurements of these challenging cases, as well as for conventional external beam therapy. To convert a series of raw EPID-measured radiation field images into water-based dose distribution, a pixel-to-pixel dose-response function of the EPID specific to the linac is essential. The response function was obtained by using a Monte Carlo simulation of the photon transport in the EPID with a comprehensive calibration. After the raw image was converted into the primary incident photon fluence, the fluence was further convolved into a water-based dose distribution of the dynamic field by using a pregenerated pencil-beam kernel. The EPID-based dosimetric measurement technique was validated using beams with and without flattening filter of all energies available in Varian TrueBeam STx™. Both regularly and irregularly shaped fields measured using a PTW 729 ion chamber array in plastic water phantom. The technique was also applied to measure the distribution for a total of 23 treatment plans of different energies to evaluate the accuracy of the proposed approach. The EPID measurements of square fields of 4 × 4 cm2 to 20 × 20 cm2, circular fields of 2-15 cm diameters, rectangular fields of various sizes, and irregular MLC fields were in accordance with measurements using a Farmer chamber and/or ion chamber array. The 2D absolute dose maps generated from EPID raw images agreed with ion chamber measurements to within 1.5% for all fields. For the 23 patient cases examined in this work, the average γ-index passing rate were found to be 99.2 ± 0.6%, 97.4 ± 2.4%, and 72.6 ± 8.4%, respectively, for criterions of 3 mm/3%, 2 mm/2%, and 1 mm/1%. The high spatial resolution and high frame rate EPID provides an accurate and efficient dosimetric tool for QA of modern radiation therapy. Accurate absolute 2D dose maps can be generated from the system for an independent dosimetric verification of treatment delivery.

    View details for DOI 10.1002/acm2.12007

    View details for Web of Science ID 000393176200002

    View details for PubMedCentralID PMC5393354

  • Fully automated quantitative cephalometry using convolutional neural networks. Journal of medical imaging (Bellingham, Wash.) Arik, S. Ö., Ibragimov, B., Xing, L. 2017; 4 (1): 014501-?

    Abstract

    Quantitative cephalometry plays an essential role in clinical diagnosis, treatment, and surgery. Development of fully automated techniques for these procedures is important to enable consistently accurate computerized analyses. We study the application of deep convolutional neural networks (CNNs) for fully automated quantitative cephalometry for the first time. The proposed framework utilizes CNNs for detection of landmarks that describe the anatomy of the depicted patient and yield quantitative estimation of pathologies in the jaws and skull base regions. We use a publicly available cephalometric x-ray image dataset to train CNNs for recognition of landmark appearance patterns. CNNs are trained to output probabilistic estimations of different landmark locations, which are combined using a shape-based model. We evaluate the overall framework on the test set and compare with other proposed techniques. We use the estimated landmark locations to assess anatomically relevant measurements and classify them into different anatomical types. Overall, our results demonstrate high anatomical landmark detection accuracy ([Formula: see text] to 2% higher success detection rate for a 2-mm range compared with the top benchmarks in the literature) and high anatomical type classification accuracy ([Formula: see text] average classification accuracy for test set). We demonstrate that CNNs, which merely input raw image patches, are promising for accurate quantitative cephalometry.

    View details for DOI 10.1117/1.JMI.4.1.014501

    View details for PubMedID 28097213

    View details for PubMedCentralID PMC5220585

  • Recurrent Generative Adversarial Neural Networks for Compressive Imaging Mardani, M., Gong, E., Cheng, J. Y., Pauly, J., Xing, L., IEEE IEEE. 2017
  • Combining deep learning with anatomy analysis for segmentation of portal vein for liver SBRT planning. Physics in medicine and biology Ibragimov, B. n., Toesca, D. n., Chang, D. n., Koong, A. n., Xing, L. n. 2017

    Abstract

    Automated segmentation of portal vein (PV) for liver radiotherapy planning is a challenging task due to potentially low vasculature contrast, complex PV anatomy and image artifacts originated from fiducial markers and vasculature stents. In this paper, we propose a novel framework for automated PV segmentation from computed tomography (CT) images. We apply convolutional neural networks (CNN) to learn consistent appearance patterns of PV using a training set of CT images with reference annotations and then enhance PV in previously unseen CT images. Markov Random Fields (MRF) were further used to smooth the CNN enhancement results and remove isolated mis-segmented regions. Finally, CNN-MRF-based enhancement was augmented with PV centerline detection that was based on PV anatomical properties such as tubularity and branch composition. The framework was validated on a clinical database with 72 CT images of patients scheduled to liver stereotactic body radiation therapy. The obtained segmentation accuracy was DSC = 0.83 and η = 1.08 in terms of the median Dice coefficient and mean symmetric surface distance, respectively, when segmentation is encompassed into the PV region of interest. The obtained results indicate that CNN can be used for accurate segmentation of PV and potentially integrated into liver radiation therapy planning.

    View details for PubMedID 28994665

  • Cone Beam X-ray Luminescence Computed Tomography Based on Bayesian Method IEEE TRANSACTIONS ON MEDICAL IMAGING Zhang, G., Liu, F., Liu, J., Luo, J., Xie, Y., Bai, J., Xing, L. 2017; 36 (1): 225-235

    Abstract

    X-ray luminescence computed tomography (XLCT), which aims to achieve molecular and functional imaging by X-rays, has recently been proposed as a new imaging modality. Combining the principles of X-ray excitation of luminescence-based probes and optical signal detection, XLCT naturally fuses functional and anatomical images and provides complementary information for a wide range of applications in biomedical research. In order to improve the data acquisition efficiency of previously developed narrow-beam XLCT, a cone beam XLCT (CB-XLCT) mode is adopted here to take advantage of the useful geometric features of cone beam excitation. Practically, a major hurdle in using cone beam X-ray for XLCT is that the inverse problem here is seriously ill-conditioned, hindering us to achieve good image quality. In this paper, we propose a novel Bayesian method to tackle the bottleneck in CB-XLCT reconstruction. The method utilizes a local regularization strategy based on Gaussian Markov random field to mitigate the ill-conditioness of CB-XLCT. An alternating optimization scheme is then used to automatically calculate all the unknown hyperparameters while an iterative coordinate descent algorithm is adopted to reconstruct the image with a voxel-based closed-form solution. Results of numerical simulations and mouse experiments show that the self-adaptive Bayesian method significantly improves the CB-XLCT image quality as compared with conventional methods.

    View details for DOI 10.1109/TMI.2016.2603843

    View details for Web of Science ID 000392418000020

    View details for PubMedID 27576245

    View details for PubMedCentralID PMC5391999

  • Feasibility study of Compton cameras for x-ray fluorescence computed tomography with humans PHYSICS IN MEDICINE AND BIOLOGY Vernekohl, D., Ahmad, M., Chinn, G., Xing, L. 2016; 61 (24): 8521-8540

    Abstract

    X-ray fluorescence imaging is a promising imaging technique able to depict the spatial distributions of low amounts of molecular agents in vivo. Currently, the translation of the technique to preclinical and clinical applications is hindered by long scanning times as objects are scanned with flux-limited narrow pencil beams. The study presents a novel imaging approach combining x-ray fluorescence imaging with Compton imaging. Compton cameras leverage the imaging performance of XFCT and abolish the need for pencil beam excitation. The study examines the potential of this new imaging approach on the base of Monte-Carlo simulations. In the work, it is first presented that the particular option of slice/fan-beam x-ray excitation has advantages in image reconstruction in regard of processing time and image quality compared to traditional volumetric Compton imaging. In a second experiment, the feasibility of the approach for clinical applications with tracer agents made from gold nano-particles is examined in a simulated lung scan scenario. The high energy of characteristic x-ray photons from gold is advantageous for deep tissue penetration and has lower angular blurring in the Compton camera. It is found that Doppler broadening in the first detector stage of the Compton camera adds the largest contribution on the angular blurring; physically limiting the spatial resolution. Following the analysis of the results from the spatial resolution test, resolutions in the order of one centimeter are achievable with the approach in the center of the lung. The concept of Compton imaging allows one to distinguish to some extent between scattered photons and x-ray fluorescent photons based on their difference in emission position. The results predict that molecular sensitivities down to 240 pM l(-1) for 5 mm diameter lesions at 15 mGy for 50 nm diameter gold nano-particles are achievable. A 45-fold speed up time for data acquisition compared to traditional pencil beam XFCT could be achieved for lung imaging at the cost of a small sensitivity decrease.

    View details for DOI 10.1088/0031-9155/61/24/8521

    View details for Web of Science ID 000388688800007

    View details for PubMedID 27845933

  • A depth-sensing technique on 3D-printed compensator for total body irradiation patient measurement and treatment planning. Medical physics Lee, M., Han, B., Jenkins, C., Xing, L., Suh, T. 2016; 43 (11): 6137-?

    Abstract

    The purpose of total body irradiation (TBI) techniques is to deliver a uniform radiation dose to the entire volume of a patient's body. Due to variations in the thickness of the patient, it is difficult to produce such a uniform dose distribution throughout the body. In many techniques, a compensator is used to adjust the dose delivered to various sections of the patient. The current study aims to develop and validate an innovative method of using depth-sensing cameras and 3D printing techniques for TBI treatment planning and compensator fabrication.A tablet with an integrated depth-sensing camera and motion tracking sensors was used to scan a RANDO™ phantom positioned in a TBI treatment booth to detect and store the 3D surface in a point cloud format. The accuracy of the detected surface was evaluated by comparing extracted body thickness measurements with corresponding measurements from computed tomography (CT) scan images. The thickness, source to surface distance, and off-axis distance of the phantom at different body section were measured for TBI treatment planning. A detailed compensator design was calculated to achieve a uniform dose distribution throughout the phantom. The compensator was fabricated using a 3D printer, silicone molding, and a mixture of wax and tungsten powder. In vivo dosimetry measurements were performed using optically stimulated luminescent detectors.The scan of the phantom took approximately 30 s. The mean error for thickness measurements at each section of phantom relative to CT was 0.48 ± 0.27 cm. The average fabrication error for the 3D-printed compensator was 0.16 ± 0.15 mm. In vivo measurements for an end-to-end test showed that overall dose differences were within 5%.A technique for planning and fabricating a compensator for TBI treatment using a depth camera equipped tablet and a 3D printer was demonstrated to be sufficiently accurate to be considered for further investigation.

    View details for PubMedID 27806603

  • Automatic deformable surface registration for medical applications by radial basis function-based robust point-matching COMPUTERS IN BIOLOGY AND MEDICINE Kim, Y., Na, Y. H., Xing, L., Lee, R., Park, S. 2016; 77: 173-181

    Abstract

    Deformable surface mesh registration is a useful technique for various medical applications, such as intra-operative treatment guidance and intra- or inter-patient study. In this paper, we propose an automatic deformable mesh registration technique. The proposed method iteratively deforms a source mesh to a target mesh without manual feature extraction. Each iteration of the registration consists of two steps, automatic correspondence finding using robust point-matching (RPM) and local deformation using a radial basis function (RBF). The proposed RBF-based RPM algorithm solves the interlocking problems of correspondence and deformation using a deterministic annealing framework with fuzzy correspondence and RBF interpolation. Simulation tests showed promising results, with the average deviations decreasing by factors of 21.2 and 11.9, respectively. In the human model test, the average deviation decreased from 1.72±1.88mm to 0.57±0.66mm. We demonstrate the effectiveness of the proposed method by presenting some medical applications.

    View details for DOI 10.1016/j.compbiomed.2016.07.013

    View details for Web of Science ID 000384866000017

    View details for PubMedID 27567399

    View details for PubMedCentralID PMC5035630

  • Flexible radioluminescence imaging for FDG-guided surgery MEDICAL PHYSICS King, M. T., Jenkins, C. H., Sun, C., Carpenter, C. M., Ma, X., Cheng, K., Quynh-Thu Le, Q. T., Sunwoo, J. B., Cheng, Z., Pratx, G., Xing, L. 2016; 43 (10)

    Abstract

    Flexible radioluminescence imaging (Flex-RLI) is an optical method for imaging (18)F-fluorodeoxyglucose (FDG)-avid tumors. The authors hypothesize that a gadolinium oxysulfide: terbium (GOS:Tb) flexible scintillator, which loosely conforms to the body contour, can enhance tumor signal-to-background ratio (SBR) compared with RLI, which utilizes a flat scintillator. The purpose of this paper is to characterize flex-RLI with respect to alternative modalities including RLI, beta-RLI (RLI with gamma rejection), and Cerenkov luminescence imaging (CLI).The photon sensitivity, spatial resolution, and signal linearity of flex-RLI were characterized with in vitro phantoms. In vivo experiments utilizing 13 nude mice inoculated with the head and neck (UMSCC1-Luc) cell line were then conducted in accordance with the institutional Administrative Panel on Laboratory Animal Care. After intravenous injection of (18)F-FDG, the tumor SBR values for flex-RLI were compared to those for RLI, beta-RLI, and CLI using the Wilcoxon signed rank test.With respect to photon sensitivity, RLI, beta-RLI, and flex-RLI produced 1216.2, 407.0, and 98.6 times more radiance per second than CLI. Respective full-width half maximum values across a 0.5 mm capillary tube were 6.9, 6.4, 2.2, and 1.5 mm, respectively. Flex-RLI demonstrated a near perfect correlation with (18)F activity (r = 0.99). Signal uniformity for flex-RLI improved after more aggressive homogenization of the GOS powder with the silicone elastomer during formulation. In vivo, the SBR value for flex-RLI (median 1.29; interquartile range 1.18-1.36) was statistically greater than that for RLI (1.08; 1.02-1.14; p < 0.01) by 26%. However, there was no statistically significant difference in SBR values between flex-RLI and beta-RLI (p = 0.92). Furthermore, there was no statistically significant difference in SBR values between flex-RLI and CLI (p = 0.11) in a more limited dataset.Flex-RLI provides high quality images with SBRs comparable to those from CLI and beta-RLI in a single 10 s acquisition.

    View details for DOI 10.1118/1.4961745

    View details for PubMedID 27782732

  • Automatic multiorgan segmentation in CT images of the male pelvis using region-specific hierarchical appearance cluster models MEDICAL PHYSICS Li, D., Zang, P., Chai, X., Cui, Y., Li, R., Xing, L. 2016; 43 (10)

    Abstract

    Accurate segmentation of pelvic organs in CT images is of great importance in external beam radiotherapy for prostate cancer. The aim of this studying is to develop a novel method for automatic, multiorgan segmentation of the male pelvis.The authors' segmentation method consists of several stages. First, a pretreatment includes parameterization, principal component analysis (PCA), and an established process of region-specific hierarchical appearance cluster (RSHAC) model which was executed on the training dataset. After the preprocessing, online automatic segmentation of new CT images is achieved by combining the RSHAC model with the PCA-based point distribution model. Fifty pelvic CT from eight prostate cancer patients were used as the training dataset. From another 20 prostate cancer patients, 210 CT images were used for independent validation of the segmentation method.In the training dataset, 15 PCA modes were needed to represent 95% of shape variations of pelvic organs. When tested on the validation dataset, the authors' segmentation method had an average Dice similarity coefficient and mean absolute distance of 0.751 and 0.371 cm, 0.783 and 0.303 cm, 0.573 and 0.604 cm for prostate, bladder, and rectum, respectively. The automated segmentation process took on average 5 min on a personal computer equipped with Core 2 Duo CPU of 2.8 GHz and 8 GB RAM.The authors have developed an efficient and reliable method for automatic segmentation of multiple organs in the male pelvis. This method should be useful for treatment planning and adaptive replanning for prostate cancer radiotherapy. With this method, the physicist can improve the work efficiency and stability.

    View details for DOI 10.1118/1.4962468

    View details for PubMedID 27782723

  • Automating quality assurance of digital linear accelerators using a radioluminescent phosphor coated phantom and optical imaging. Physics in medicine and biology Jenkins, C. H., Naczynski, D. J., Yu, S. S., Yang, Y., Xing, L. 2016; 61 (17): L29-37

    Abstract

    Performing mechanical and geometric quality assurance (QA) tests for medical linear accelerators (LINAC) is a predominantly manual process that consumes significant time and resources. In order to alleviate this burden this study proposes a novel strategy to automate the process of performing these tests. The autonomous QA system consists of three parts: (1) a customized phantom coated with radioluminescent material; (2) an optical imaging system capable of visualizing the incidence of the radiation beam, light field or lasers on the phantom; and (3) software to process the captured signals. The radioluminescent phantom, which enables visualization of the radiation beam on the same surface as the light field and lasers, is placed on the couch and imaged while a predefined treatment plan is delivered from the LINAC. The captured images are then processed to self-calibrate the system and perform measurements for evaluating light field/radiation coincidence, jaw position indicators, cross-hair centering, treatment couch position indicators and localizing laser alignment. System accuracy is probed by intentionally introducing errors and by comparing with current clinical methods. The accuracy of self-calibration is evaluated by examining measurement repeatability under fixed and variable phantom setups. The integrated system was able to automatically collect, analyze and report the results for the mechanical alignment tests specified by TG-142. The average difference between introduced and measured errors was 0.13 mm. The system was shown to be consistent with current techniques. Measurement variability increased slightly from 0.1 mm to 0.2 mm when the phantom setup was varied, but no significant difference in the mean measurement value was detected. Total measurement time was less than 10 minutes for all tests as a result of automation. The system's unique features of a phosphor-coated phantom and fully automated, operator independent self-calibration offer the potential to streamline the QA process for modern LINACs.

    View details for DOI 10.1088/0031-9155/61/17/L29

    View details for PubMedID 27514654

  • Optimization of rotational arc station parameter optimized radiation therapy. Medical physics Dong, P., Ungun, B., Boyd, S., Xing, L. 2016; 43 (9): 4973-?

    Abstract

    To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of matching VMAT in both plan quality and delivery efficiency by using three clinical cases of different disease sites.The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based proximal operator graph solver. To avoid being trapped in a local minimum in beamlet-based aperture selection using the gradient descent algorithm, a stochastic gradient descent was employed here. Apertures with zero or low weight were thrown out. To find out whether there was room to further improve the plan by adding more apertures or SPs, the authors repeated the above procedure with consideration of the existing dose distribution from the last iteration. At the end of the second iteration, the weights of all the apertures were reoptimized, including those of the first iteration. The above procedure was repeated until the plan could not be improved any further. The optimization technique was assessed by using three clinical cases (prostate, head and neck, and brain) with the results compared to that obtained using conventional VMAT in terms of dosimetric properties, treatment time, and total MU.Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. For the prostate case, the volume of the 50% prescription dose was decreased by 22% for the rectum and 6% for the bladder. For the head and neck case, SPORT improved the mean dose for the left and right parotids by 15% each. The maximum dose was lowered from 72.7 to 71.7 Gy for the mandible, and from 30.7 to 27.3 Gy for the spinal cord. The mean dose for the pharynx and larynx was reduced by 8% and 6%, respectively. For the brain case, the doses to the eyes, chiasm, and inner ears were all improved. SPORT shortened the treatment time by ∼1 min for the prostate case, ∼0.5 min for brain case, and ∼0.2 min for the head and neck case.The dosimetric quality and delivery efficiency presented here indicate that SPORT is an intriguing alternative treatment modality. With the widespread adoption of digital linac, SPORT should lead to improved patient care in the future.

    View details for DOI 10.1118/1.4960000

    View details for PubMedID 27587028

    View details for PubMedCentralID PMC4975754

  • Fabrication of a customized bone scaffold using a homemade medical 3D printer for comminuted fractures JOURNAL OF THE KOREAN PHYSICAL SOCIETY Yoon, D., Jung, J., Shin, H., Kim, M., Choe, B., Kim, S., Suh, T. S., Lee, K. S., Xing, L. 2016; 69 (5): 852-857
  • Production of Spherical Ablations Using Nonthermal Irreversible Electroporation: A Laboratory Investigation Using a Single Electrode and Grounding Pad. Journal of vascular and interventional radiology Sano, M. B., Fan, R. E., Hwang, G. L., Sonn, G. A., Xing, L. 2016; 27 (9): 1432-1440 e3

    Abstract

    To mathematically model and test ex vivo a modified technique of irreversible electroporation (IRE) to produce large spherical ablations by using a single probe.Computed simulations were performed by using varying voltages, electrode exposure lengths, and tissue types. A vegetable (potato) tissue model was then used to compare ablations created by conventional and high-frequency IRE protocols by using 2 probe configurations: a single probe with two collinear electrodes (2EP) or a single electrode configured with a grounding pad (P+GP). The new P+GP electrode configuration was evaluated in ex vivo liver tissue.The P+GP configuration produced more spherical ablation volumes than the 2EP configuration in computed simulations and tissue models. In prostate tissue, computed simulations predicted ablation volumes at 3,000 V of 1.6 cm(3) for the P+GP configurations, compared with 0.94 cm(3) for the 2EP configuration; in liver tissue, the predicted ablation volumes were 4.7 times larger than those in the prostate. Vegetable model studies verify that the P+GP configuration produces larger and more spherical ablations than those produced by the 2EP. High-frequency IRE treatment of ex vivo liver with the P+GP configuration created a 2.84 × 2.21-cm ablation zone.Computer modeling showed that P+GP configuration for IRE procedures yields ablations that are larger than the 2EP configuration, creating substantial ablation zones with a single electrode placement. When tested in tissue models and an ex vivo liver model, the P+GP configuration created ablation zones that appear to be of clinically relevant size and shape.

    View details for DOI 10.1016/j.jvir.2016.05.032

    View details for PubMedID 27478129

  • Early Change in Metabolic Tumor Heterogeneity during Chemoradiotherapy and Its Prognostic Value for Patients with Locally Advanced Non-Small Cell Lung Cancer PLOS ONE Dong, X., Sun, X., Sun, L., Maxim, P. G., Xing, L., Huang, Y., Li, W., Wan, H., Zhao, X., Xing, L., Yu, J. 2016; 11 (6)

    Abstract

    To observe the early change of metabolic tumor heterogeneity during chemoradiotherapy and to determine its prognostic value for patients with locally advanced non-small cell lung cancer (NSCLC).From January 2007 to March 2010, 58 patients with NSCLC were included who were received 18F-fluorodeoxyglucose (18F-FDG) PET/CT before and following 40 Gy radiotherapy with the concurrent cisplatin-based chemotherapy (CCRT). Primary tumor FDG uptake heterogeneity was determined using global and local scale textural features extracted from standardized uptake value (SUV) histogram analysis (coefficient of variation [COV], skewness, kurtosis, area under the curve of the cumulative SUV histogram [AUC-CSH]) and normalized gray-level co-occurrence matrix (contrast, dissimilarity, entropy, homogeneity). SUVmax and metabolic tumor volume (MTV) were also evaluated. Correlations were analyzed between parameters on baseline or during treatments with tumor response, progression-free survival (PFS), and overall survival (OS).Compared with non-responders, responders showed significantly greater pre-treatment COV, contrast and MTV (AUC = 0.781, 0.804, 0.686, respectively). Receiver-operating-characteristic curve analysis showed that early change of tumor textural analysis serves as a response predictor with higher sensitivity (73.2%~92.1%) and specificity (80.0%~83.6%) than baseline parameters. Change in AUC-CSH and dissimilarity during CCRT could also predict response with optimal cut-off values (33.0% and 28.7%, respectively). The patients with greater changes in contrast and AUC-CSH had significantly higher 5-year OS (P = 0.008, P = 0.034) and PFS (P = 0.007, P = 0.039). In multivariate analysis, only change in contrast was found as the independent prognostic factor of PFS (HR 0.476, P = 0.021) and OS (HR 0.519, P = 0.015).The metabolic tumor heterogeneity change during CCRT characterized by global and local scale textural features may be valuable for predicting treatment response and survival for patients with locally advanced NSCLC.

    View details for DOI 10.1371/journal.pone.0157836

    View details for Web of Science ID 000378212000047

    View details for PubMedID 27322376

    View details for PubMedCentralID PMC4913903

  • High Resolution X-ray-Induced Acoustic Tomography SCIENTIFIC REPORTS Xiang, L., Tang, S., Ahmad, M., Xing, L. 2016; 6

    Abstract

    Absorption based CT imaging has been an invaluable tool in medical diagnosis, biology, and materials science. However, CT requires a large set of projection data and high radiation dose to achieve superior image quality. In this letter, we report a new imaging modality, X-ray Induced Acoustic Tomography (XACT), which takes advantages of high sensitivity to X-ray absorption and high ultrasonic resolution in a single modality. A single projection X-ray exposure is sufficient to generate acoustic signals in 3D space because the X-ray generated acoustic waves are of a spherical nature and propagate in all directions from their point of generation. We demonstrate the successful reconstruction of gold fiducial markers with a spatial resolution of about 350 μm. XACT reveals a new imaging mechanism and provides uncharted opportunities for structural determination with X-ray.

    View details for DOI 10.1038/srep26118

    View details for Web of Science ID 000376061600001

    View details for PubMedID 27189746

    View details for PubMedCentralID PMC4870558

  • Dosimetric analysis of isocentrically shielded volumetric modulated arc therapy for locally recurrent nasopharyngeal cancer SCIENTIFIC REPORTS Lu, J., Huang, B., Xing, L., Chang, D. T., Peng, X., Xie, L., Lin, Z., Li, M. 2016; 6

    Abstract

    This study aimed to investigate the dosimetric characteristics of an isocentrically shielded RapidArc (IS-RA) technique for treatment of locally recurrent nasopharyngeal cancer (lrNPC). In IS-RA, the isocenter was placed at the center of the pre-irradiated brainstem (BS)/spinal cord (SC) and the jaws were set to shield the BS/SC while ensuring the target coverage during the whole gantry rotation. For fifteen patients, the IS-RA plans were compared with the conventional RapidArc (C-RA) regarding target coverage, organ-at-risk (OAR) sparing and monitor units (MUs). The relationship between the dose reduction of BS/SC and some geometric parameters including the angle extended by the target with respect to the axis of BS/SC (Ang_BSSC), the minimum distance between the target and BS/SC (Dist_Min) and the target volume were evaluated. The IS-RA reduced the BS/SC doses by approximately 1-4 Gy on average over the C-RA, with more MUs. The IS-RA demonstrated similar target coverage and sparing of other OARs except for slightly improved sparing of optic structures. More dose reduction in the isocentric region was observed in the cases with larger Ang_BSSC or smaller Dist_Min. Our results indicated that the IS-RA significantly improves the sparing of BS/SC without compromising dosimetric requirements of other involved structures for lrNPC.

    View details for DOI 10.1038/srep25959

    View details for PubMedID 27173670

  • Dual-Modal NIR-II Fluorescence and Photoacoustic Imaging of Thyroid Carcinoma Using EGFR-targeted Donor-Acceptor Chromophore Based Nanoprobes Cheng, K., Chen, H., Jenkins, C., Zhang, Z., Sun, Z., Fung, J., Hong, X., Xing, L., Cheng, Z. SOC NUCLEAR MEDICINE INC. 2016
  • A model-based scatter artifacts correction for cone beam CT MEDICAL PHYSICS Zhao, W., Vernekohl, D., Zhu, J., Wang, L., Xing, L. 2016; 43 (4): 1736-1753

    Abstract

    Due to the increased axial coverage of multislice computed tomography (CT) and the introduction of flat detectors, the size of x-ray illumination fields has grown dramatically, causing an increase in scatter radiation. For CT imaging, scatter is a significant issue that introduces shading artifact, streaks, as well as reduced contrast and Hounsfield Units (HU) accuracy. The purpose of this work is to provide a fast and accurate scatter artifacts correction algorithm for cone beam CT (CBCT) imaging.The method starts with an estimation of coarse scatter profiles for a set of CBCT data in either image domain or projection domain. A denoising algorithm designed specifically for Poisson signals is then applied to derive the final scatter distribution. Qualitative and quantitative evaluations using thorax and abdomen phantoms with Monte Carlo (MC) simulations, experimental Catphan phantom data, and in vivo human data acquired for a clinical image guided radiation therapy were performed. Scatter correction in both projection domain and image domain was conducted and the influences of segmentation method, mismatched attenuation coefficients, and spectrum model as well as parameter selection were also investigated.Results show that the proposed algorithm can significantly reduce scatter artifacts and recover the correct HU in either projection domain or image domain. For the MC thorax phantom study, four-components segmentation yields the best results, while the results of three-components segmentation are still acceptable. The parameters (iteration number K and weight β) affect the accuracy of the scatter correction and the results get improved as K and β increase. It was found that variations in attenuation coefficient accuracies only slightly impact the performance of the proposed processing. For the Catphan phantom data, the mean value over all pixels in the residual image is reduced from -21.8 to -0.2 HU and 0.7 HU for projection domain and image domain, respectively. The contrast of the in vivo human images is greatly improved after correction.The software-based technique has a number of advantages, such as high computational efficiency and accuracy, and the capability of performing scatter correction without modifying the clinical workflow (i.e., no extra scan/measurement data are needed) or modifying the imaging hardware. When implemented practically, this should improve the accuracy of CBCT image quantitation and significantly impact CBCT-based interventional procedures and adaptive radiation therapy.

    View details for DOI 10.1118/1.4943796

    View details for Web of Science ID 000373711000015

    View details for PubMedID 27036571

    View details for PubMedCentralID PMC4798999

  • Using edge-preserving algorithm with non-local mean for significantly improved image-domain material decomposition in dual-energy CT PHYSICS IN MEDICINE AND BIOLOGY Zhao, W., Niu, T., Xing, L., Xie, Y., Xiong, G., Elmore, K., Zhu, J., Wang, L., Min, J. K. 2016; 61 (3): 1332-1351

    Abstract

    Increased noise is a general concern for dual-energy material decomposition. Here, we develop an image-domain material decomposition algorithm for dual-energy CT (DECT) by incorporating an edge-preserving filter into the Local HighlY constrained backPRojection reconstruction (HYPR-LR) framework. With effective use of the non-local mean, the proposed algorithm, which is referred to as HYPR-NLM, reduces the noise in dual-energy decomposition while preserving the accuracy of quantitative measurement and spatial resolution of the material-specific dual-energy images. We demonstrate the noise reduction and resolution preservation of the algorithm with an iodine concentrate numerical phantom by comparing the HYPR-NLM algorithm to the direct matrix inversion, HYPR-LR and iterative image-domain material decomposition (Iter-DECT). We also show the superior performance of the HYPR-NLM over the existing methods by using two sets of cardiac perfusing imaging data. The DECT material decomposition comparison study shows that all four algorithms yield acceptable quantitative measurements of iodine concentrate. Direct matrix inversion yields the highest noise level, followed by HYPR-LR and Iter-DECT. HYPR-NLM in an iterative formulation significantly reduces image noise and the image noise is comparable to or even lower than that generated using Iter-DECT. For the HYPR-NLM method, there are marginal edge effects in the difference image, suggesting the high-frequency details are well preserved. In addition, when the search window size increases from to , there are no significant changes or marginal edge effects in the HYPR-NLM difference images. The reference drawn from the comparison study includes: (1) HYPR-NLM significantly reduces the DECT material decomposition noise while preserving quantitative measurements and high-frequency edge information, and (2) HYPR-NLM is robust with respect to parameter selection.

    View details for DOI 10.1088/0031-9155/61/3/1332

    View details for Web of Science ID 000369517000025

  • Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images RADIOLOGY Cui, Y., Tha, K. K., Terasaka, S., Yamaguchi, S., Wang, J., Kudo, K., Xing, L., Shirato, H., Li, R. 2016; 278 (2): 546-553

    Abstract

    Purpose To develop and independently validate prognostic imaging biomarkers for predicting survival in patients with glioblastoma on the basis of multiregion quantitative image analysis. Materials and Methods This retrospective study was approved by the local institutional review board, and informed consent was waived. A total of 79 patients from two independent cohorts were included. The discovery and validation cohorts consisted of 46 and 33 patients with glioblastoma from the Cancer Imaging Archive (TCIA) and the local institution, respectively. Preoperative T1-weighted contrast material-enhanced and T2-weighted fluid-attenuation inversion recovery magnetic resonance (MR) images were analyzed. For each patient, we semiautomatically delineated the tumor and performed automated intratumor segmentation, dividing the tumor into spatially distinct subregions that demonstrate coherent intensity patterns across multiparametric MR imaging. Within each subregion and for the entire tumor, we extracted quantitative imaging features, including those that fully capture the differential contrast of multimodality MR imaging. A multivariate sparse Cox regression model was trained by using TCIA data and tested on the validation cohort. Results The optimal prognostic model identified five imaging biomarkers that quantified tumor surface area and intensity distributions of the tumor and its subregions. In the validation cohort, our prognostic model achieved a concordance index of 0.67 and significant stratification of overall survival by using the log-rank test (P = .018), which outperformed conventional prognostic factors, such as age (concordance index, 0.57; P = .389) and tumor volume (concordance index, 0.59; P = .409). Conclusion The multiregion analysis presented here establishes a general strategy to effectively characterize intratumor heterogeneity manifested at multimodality imaging and has the potential to reveal useful prognostic imaging biomarkers in glioblastoma. (©) RSNA, 2015 Online supplemental material is available for this article.

    View details for DOI 10.1148/radiol.2015150358

    View details for PubMedID 26348233

  • Novel benzo-bis(1,2,5-thiadiazole) fluorophores for in vivo NIR-II imaging of cancer CHEMICAL SCIENCE Sun, Y., Qu, C., Chen, H., He, M., Tang, C., Shou, K., Hong, S., Yang, M., Jiang, Y., Ding, B., Xiao, Y., Xing, L., Hong, X., Cheng, Z. 2016; 7 (9): 6203-6207

    Abstract

    Optical imaging of diseases represents a highly dynamic and multidisciplinary research area, and second near-infrared window (NIR-II, 1000-1700 nm) imaging is at the forefront of the research on optical imaging techniques. Small-molecule based NIR-II (1000-1700 nm) dyes are highly promising candidates for in vivo molecular imaging because of their high biocompatibility, fast excretion, and high clinical translation ability. However, research reports on small-molecule based NIR-II dyes and probes are rare. Herein, we designed a series of fluorescent compounds (Q1, Q2, Q3, and Q4) and investigated the relationships between their structures and absorption/fluorescence properties. Q4 (maximum emission at 1100 nm) stood out as the dye with the best physical properties and thus was selected as a scaffold for the facile construction of two types of water-soluble and biocompatible NIR-II probes (Q4NPs and SCH1100). Highly specific gastrin-releasing peptide receptor (GRPR) targeted NIR-II imaging of prostate cancer in living mice was achieved using the small-molecule probe SCH1100, which represents the first small peptide based NIR-II probe for targeted cancer imaging. The attractive imaging properties of Q4-based NIR-II probes open up many opportunities for molecular imaging and clinical translation in the unique NIR-II window.

    View details for DOI 10.1039/c6sc01561a

    View details for Web of Science ID 000382488500072

    View details for PubMedCentralID PMC6024204

  • Comparison of a Large Area CZT Detector to a Spectroscopic CdTe Detector for X-ray Fluorescence Computed 'Tomography Vernekohl, D., Streicher, M., Ahmad, M., Xing, L., He, Z., IEEE IEEE. 2016
  • X-ray Fluorescence Computed Tomography with a Compton Camera for a Clinical Application Vernekohl, D., Ahmad, M., Chinn, G., Xing, L., IEEE IEEE. 2016
  • 4DCT and 4D Cone-Beam CT Reconstruction Using Temporal Regularizations GRAPHICS PROCESSING UNIT-BASED HIGH PERFORMANCE COMPUTING IN RADIATION THERAPY Gao, H., Guo, M., Li, R., Xing, L., Jia, Jiang, S. B. 2016: 63–82
  • Minimizing normal tissue dose spillage via broad-range optimization of hundreds of intensity modulated beams for treating multiple brain targets JOURNAL OF RADIOSURGERY AND SBRT Dong, P., Hossain, S., Keeling, V., Ahmad, S., Xing, L., Ma, L. 2016; 4 (2): 107–15

    Abstract

    Variable normal tissue dose and inter-target dose interplay effects have been reported in volumetric modulated arc therapy (VMAT) of multiple brain metastases. In order to minimize such adverse effects, a Broad-Range Optimization of Modulated Beam Approach (BROOMBA) was developed whereby hundreds of intensity-modulated beams surrounding the central axis of the skull were progressively selected and optimized. To investigate technical feasibility and potential dosimetric benefits of BROOMBA, we first developed such an approach on a standalone workstation and then implemented it for a multi-center benchmark case involving 3 to 12 multiple brain metastases. The BROOMBA planning results was compared with VMAT treatment plans of the same case using coplanar and non-coplanar arc beams. We have found that BROOMBA consistently outperformed VMAT plans in terms of low-level normal brain sparing and reduction in the dose interplay effects among the targets. For example, when planning simultaneous treatment of 12 targets, BROOMBA lowered the normal brain dose by as much as 65% versus conventional VMAT treatment plans and the dose interplay effects across 8 Gy to 12 Gy levels was reduced to be negligible. In conclusion, we have demonstrated BROOMBA as a powerful tool for improving the planning quality of multiple brain metastases treatments via modern high-output linear accelerators.

    View details for PubMedID 29296435

  • Application programming in C# environment with recorded user software interactions and its application in autopilot of VMAT/IMRT treatment planning JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS Wang, H., Xing, L. 2016; 17 (6): 189-203

    Abstract

    An autopilot scheme of volumetric-modulated arc therapy (VMAT)/intensity-modulated radiation therapy (IMRT) planning with the guidance of prior knowl-edge is established with recorded interactions between a planner and a commercial treatment planning system (TPS). Microsoft (MS) Visual Studio Coded UI is applied to record some common planner-TPS interactions as subroutines. The TPS used in this study is a Windows-based Eclipse system. The interactions of our application program with Eclipse TPS are realized through a series of subrou-tines obtained by prerecording the mouse clicks or keyboard strokes of a planner in operating the TPS. A strategy to autopilot Eclipse VMAT/IMRT plan selection process is developed as a specific example of the proposed "scripting" method. The autopiloted planning is navigated by a decision function constructed with a reference plan that has the same prescription and similar anatomy with the case at hand. The calculation proceeds by alternating between the Eclipse optimization and the outer-loop optimization independent of the Eclipse. In the C# program, the dosimetric characteristics of a reference treatment plan are used to assess and modify the Eclipse planning parameters and to guide the search for a clinically sensible treatment plan. The approach is applied to plan a head and neck (HN) VMAT case and a prostate IMRT case. Our study demonstrated the feasibility of application programming method in C# environment with recorded interactions of planner-TPS. The process mimics a planner's planning process and automatically provides clinically sensible treatment plans that would otherwise require a large amount of manual trial and error of a planner. The proposed technique enables us to harness a commercial TPS by application programming via the use of recorded human computer interactions and provides an effective tool to greatly facilitate the treatment planning process.

    View details for Web of Science ID 000388927500017

    View details for PubMedCentralID PMC5690512

  • Automatic liver contouring for radiotherapy treatment planning PHYSICS IN MEDICINE AND BIOLOGY Li, D., Liu, L., Kapp, D. S., Xing, L. 2015; 60 (19): 7461-7483

    Abstract

    To develop automatic and efficient liver contouring software for planning 3D-CT and four-dimensional computed tomography (4D-CT) for application in clinical radiation therapy treatment planning systems.The algorithm comprises three steps for overcoming the challenge of similar intensities between the liver region and its surrounding tissues. First, the total variation model with the L1 norm (TV-L1), which has the characteristic of multi-scale decomposition and an edge-preserving property, is used for removing the surrounding muscles and tissues. Second, an improved level set model that contains both global and local energy functions is utilized to extract liver contour information sequentially. In the global energy function, the local correlation coefficient (LCC) is constructed based on the gray level co-occurrence matrix both of the initial liver region and the background region. The LCC can calculate the correlation of a pixel with the foreground and background regions, respectively. The LCC is combined with intensity distribution models to classify pixels during the evolutionary process of the level set based method. The obtained liver contour is used as the candidate liver region for the following step. In the third step, voxel-based texture characterization is employed for refining the liver region and obtaining the final liver contours.The proposed method was validated based on the planning CT images of a group of 25 patients undergoing radiation therapy treatment planning. These included ten lung cancer patients with normal appearing livers and ten patients with hepatocellular carcinoma or liver metastases. The method was also tested on abdominal 4D-CT images of a group of five patients with hepatocellular carcinoma or liver metastases. The false positive volume percentage, the false negative volume percentage, and the dice similarity coefficient between liver contours obtained by a developed algorithm and a current standard delineated by the expert group are on an average 2.15-2.57%, 2.96-3.23%, and 91.01-97.21% for the CT images with normal appearing livers, 2.28-3.62%, 3.15-4.33%, and 86.14-93.53% for the CT images with hepatocellular carcinoma or liver metastases, and 2.37-3.96%, 3.25-4.57%, and 82.23-89.44% for the 4D-CT images also with hepatocellular carcinoma or liver metastases, respectively.The proposed three-step method can achieve efficient automatic liver contouring for planning CT and 4D-CT images with follow-up treatment planning and should find widespread applications in future treatment planning systems.

    View details for DOI 10.1088/0031-9155/60/19/7461

    View details for Web of Science ID 000364661100006

    View details for PubMedID 26352291

  • Theoretical detection threshold of the proton-acoustic range verification technique. Medical physics Ahmad, M., Xiang, L., Yousefi, S., Xing, L. 2015; 42 (10): 5735-?

    Abstract

    Range verification in proton therapy using the proton-acoustic signal induced in the Bragg peak was investigated for typical clinical scenarios. The signal generation and detection processes were simulated in order to determine the signal-to-noise limits.An analytical model was used to calculate the dose distribution and local pressure rise (per proton) for beams of different energy (100 and 160 MeV) and spot widths (1, 5, and 10 mm) in a water phantom. In this method, the acoustic waves propagating from the Bragg peak were generated by the general 3D pressure wave equation implemented using a finite element method. Various beam pulse widths (0.1-10 μs) were simulated by convolving the acoustic waves with Gaussian kernels. A realistic PZT ultrasound transducer (5 cm diameter) was simulated with a Butterworth bandpass filter with consideration of random noise based on a model of thermal noise in the transducer. The signal-to-noise ratio on a per-proton basis was calculated, determining the minimum number of protons required to generate a detectable pulse. The maximum spatial resolution of the proton-acoustic imaging modality was also estimated from the signal spectrum.The calculated noise in the transducer was 12-28 mPa, depending on the transducer central frequency (70-380 kHz). The minimum number of protons detectable by the technique was on the order of 3-30 × 10(6) per pulse, with 30-800 mGy dose per pulse at the Bragg peak. Wider pulses produced signal with lower acoustic frequencies, with 10 μs pulses producing signals with frequency less than 100 kHz.The proton-acoustic process was simulated using a realistic model and the minimal detection limit was established for proton-acoustic range validation. These limits correspond to a best case scenario with a single large detector with no losses and detector thermal noise as the sensitivity limiting factor. Our study indicated practical proton-acoustic range verification may be feasible with approximately 5 × 10(6) protons/pulse and beam current.

    View details for DOI 10.1118/1.4929939

    View details for PubMedID 26429247

    View details for PubMedCentralID PMC4567582

  • Experimental validation of L-shell x-ray fluorescence computed tomography imaging: phantom study. Journal of medical imaging (Bellingham, Wash.) Bazalova-Carter, M., Ahmad, M., Xing, L., Fahrig, R. 2015; 2 (4): 043501-?

    Abstract

    Thanks to the current advances in nanoscience, molecular biochemistry, and x-ray detector technology, x-ray fluorescence computed tomography (XFCT) has been considered for molecular imaging of probes containing high atomic number elements, such as gold nanoparticles. The commonly used XFCT imaging performed with K-shell x rays appears to have insufficient imaging sensitivity to detect the low gold concentrations observed in small animal studies. Low energy fluorescence L-shell x rays have exhibited higher signal-to-background ratio and appeared as a promising XFCT mode with greatly enhanced sensitivity. The aim of this work was to experimentally demonstrate the feasibility of L-shell XFCT imaging and to assess its achievable sensitivity. We built an experimental L-shell XFCT imaging system consisting of a miniature x-ray tube and two spectrometers, a silicon drift detector (SDD), and a CdTe detector placed at [Formula: see text] with respect to the excitation beam. We imaged a 28-mm-diameter water phantom with 4-mm-diameter Eppendorf tubes containing gold solutions with concentrations of 0.06 to 0.1% Au. While all Au vials were detectable in the SDD L-shell XFCT image, none of the vials were visible in the CdTe L-shell XFCT image. The detectability limit of the presented L-shell XFCT SDD imaging setup was 0.007% Au, a concentration observed in small animal studies.

    View details for DOI 10.1117/1.JMI.2.4.043501

    View details for PubMedID 26839910

    View details for PubMedCentralID PMC4729219

  • ß-Radioluminescence Imaging: A Comparative Evaluation with Cerenkov Luminescence Imaging. Journal of nuclear medicine : official publication, Society of Nuclear Medicine King, M. T., Carpenter, C. M., Sun, C., Ma, X., Le, Q., Sunwoo, J. B., Cheng, Z., Pratx, G., Xing, L. 2015; 56 (9): 1458-1464

    Abstract

    Cerenkov luminescence imaging (CLI) can provide high-resolution images of (18)F-FDG-avid tumors but requires prolonged acquisition times because of low photon sensitivity. In this study, we proposed a new modality, termed β-radioluminescence imaging (β-RLI), which incorporates a scintillator with a γ-rejection strategy for imaging β particles. We performed a comparative evaluation of β-RLI with CLI in both in vitro and in vivo systems.Using in vitro phantoms, we characterized the photon sensitivity and resolution of CLI and β-RLI. We also conducted a series of in vivo experiments with xenograft mouse models using both amelanotic (A375, UMSCC1-Luc) and melanotic (B16F10-Luc) cell lines. The B16F10 and UMSCC1 cell lines were transfected with the luciferase gene (Luc). CLI was acquired over 300 s, and β-RLI was acquired using two 10-s acquisitions. We correlated (18)F -: FDG activities, as assessed by PET, with tumor radiances for both β-RLI and CLI. We also compared tumor signal-to-background ratios (SBRs) between these modalities for amelanotic and melanotic tumors.For in vitro experiments, the photon sensitivity for β-RLI was 560-fold greater than that for CLI. However, the spatial resolution for β-RLI (4.4 mm) was inferior to that of CLI (1.0 mm). For in vivo experiments, correlations between (18)F-FDG activity and tumor radiance were 0.52 (P < 0.01) for β-RLI, 0.81 (P = 0.01) for amelanotic lesions with CLI, and -0.08 (negative contrast; P = 0.80) for melanotic lesions with CLI. Nine of 13 melanotic lesions had an SBR less than 1 for CLI, despite an SBR greater than 1 among all lesions for β-RLI.β-RLI can produce functional images of both amelanotic and melanotic tumors in a shorter time frame than CLI. Further engineering developments are needed to realize the full clinical potential of this modality.

    View details for DOI 10.2967/jnumed.115.158337

    View details for PubMedID 26205301

  • beta-Radioluminescence Imaging: A Comparative Evaluation with Cerenkov Luminescence Imaging JOURNAL OF NUCLEAR MEDICINE King, M. T., Carpenter, C. M., Sun, C., Ma, X., Quynh-Thu Le, Q. T., Sunwoo, J. B., Cheng, Z., Pratx, G., Xing, L. 2015; 56 (9): 1458-1464

    Abstract

    Cerenkov luminescence imaging (CLI) can provide high-resolution images of (18)F-FDG-avid tumors but requires prolonged acquisition times because of low photon sensitivity. In this study, we proposed a new modality, termed β-radioluminescence imaging (β-RLI), which incorporates a scintillator with a γ-rejection strategy for imaging β particles. We performed a comparative evaluation of β-RLI with CLI in both in vitro and in vivo systems.Using in vitro phantoms, we characterized the photon sensitivity and resolution of CLI and β-RLI. We also conducted a series of in vivo experiments with xenograft mouse models using both amelanotic (A375, UMSCC1-Luc) and melanotic (B16F10-Luc) cell lines. The B16F10 and UMSCC1 cell lines were transfected with the luciferase gene (Luc). CLI was acquired over 300 s, and β-RLI was acquired using two 10-s acquisitions. We correlated (18)F -: FDG activities, as assessed by PET, with tumor radiances for both β-RLI and CLI. We also compared tumor signal-to-background ratios (SBRs) between these modalities for amelanotic and melanotic tumors.For in vitro experiments, the photon sensitivity for β-RLI was 560-fold greater than that for CLI. However, the spatial resolution for β-RLI (4.4 mm) was inferior to that of CLI (1.0 mm). For in vivo experiments, correlations between (18)F-FDG activity and tumor radiance were 0.52 (P < 0.01) for β-RLI, 0.81 (P = 0.01) for amelanotic lesions with CLI, and -0.08 (negative contrast; P = 0.80) for melanotic lesions with CLI. Nine of 13 melanotic lesions had an SBR less than 1 for CLI, despite an SBR greater than 1 among all lesions for β-RLI.β-RLI can produce functional images of both amelanotic and melanotic tumors in a shorter time frame than CLI. Further engineering developments are needed to realize the full clinical potential of this modality.

    View details for DOI 10.2967/jnumed.115.158337

    View details for Web of Science ID 000361153000036

  • Efficient Radioisotope Energy Transfer by Gold Nanoclusters for Molecular Imaging SMALL Volotskova, O., Sun, C., Stafford, J. H., Koh, A. L., Ma, X., Cheng, Z., Cui, B., Pratx, G., Xing, L. 2015; 11 (32): 4002-4008

    Abstract

    Beta-emitting isotopes Fluorine-18 and Yttrium-90 are tested for their potential to stimulate gold nanoclusters conjugated with blood serum proteins (AuNCs). AuNCs excited by either medical radioisotope are found to be highly effective ionizing radiation energy transfer mediators, suitable for in vivo optical imaging. AuNCs synthesized with protein templates convert beta-decaying radioisotope energy into tissue-penetrating optical signals between 620 and 800 nm. Optical signals are not detected from AuNCs incubated with Technetium-99m, a pure gamma emitter that is used as a control. Optical emission from AuNCs is not proportional to Cerenkov radiation, indicating that the energy transfer between the radionuclide and AuNC is only partially mediated by Cerenkov photons. A direct Coulombic interaction is proposed as a novel and significant mechanism of energy transfer between decaying radionuclides and AuNCs.

    View details for DOI 10.1002/smll.201500907

    View details for Web of Science ID 000360226300016

  • Analysis of Long-Term 4-Dimensional Computed Tomography Regional Ventilation After Radiation Therapy INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS King, M. T., Maxim, P. G., Diehn, M., Loo, B. W., Xing, L. 2015; 92 (3): 683-690

    Abstract

    To determine whether regional ventilation, as measured using 4-dimensional computed tomography (4D-CT), declines after radiation therapy (RT).We analyzed pretreatment 4D-CT scans associated with 2 RT courses. We quantified regional pulmonary function over equivalent dose in 2 Gy (EQD2α/β=3) intervals of 0 to 5 Gy, 5 to 20 Gy, 20 to 40 Gy, and >40 Gy using percentile-normalized intensity-based (VentInt) and Jacobian-based (VentJac) ventilation metrics. We modeled the impact of dose on mean ventilation (Vent¯) and regional tidal volume (rTV: tidal volume [TV] within a dose interval normalized to total lung TV). We also identified clinical and dosimetric factors that affected regional ventilation changes (ΔVent¯ and ΔrTV) after RT for the >20 Gy dose interval.After RT, Vent¯Int exhibited statistically significant dose-dependent declines within the 20 to 40 Gy (-5.0%; P=.03) and >40 Gy (-6.8%; P<.01) intervals. Vent¯Jac exhibited a declining trend after RT only for the >40 Gy interval (-4.6%; P=.07). Factors associated with ΔVent¯Int for the >20 Gy dose interval included airway stenosis progression (P=.03) and gross tumor volume (P=.09). Both rTVInt and rTVJac were associated with small (<2%) but significant declines after RT for 20 to 40 Gy and >40 Gy intervals. Factors associated with declining rTVInt (P<.05) for the >20 Gy dose interval included airway stenosis progression, greater V20 (volume of lung receiving >20 Gy), and smaller fraction of emphysema in V20. The association between the absence of chronic obstructive pulmonary disease and declining rTV trended toward significance (P=.09).Regional ventilation, as measured using 4D-CT, demonstrates a dose-dependent decline after RT. Our results support the use of 4D-CT ventilation imaging for monitoring regional pulmonary function change after RT.

    View details for DOI 10.1016/j.ijrobp.2015.02.037

    View details for Web of Science ID 000355636800032

    View details for PubMedID 25936813

  • Development of an accurate EPID-based output measurement and dosimetric verification tool for electron beam therapy. Medical physics Ding, A., Xing, L., Han, B. 2015; 42 (7): 4190-?

    Abstract

    To develop an efficient and robust tool for output measurement and absolute dose verification of electron beam therapy by using a high spatial-resolution and high frame-rate amorphous silicon flat panel electronic portal imaging device (EPID).The dosimetric characteristics of the EPID, including saturation, linearity, and ghosting effect, were first investigated on a Varian Clinac 21EX accelerator. The response kernels of the individual pixels of the EPID to all available electron energies (6, 9, 12, 16, and 20 MeV) were calculated by using Monte Carlo (MC) simulations, which formed the basis to deconvolve an EPID raw images to the incident electron fluence map. The two-dimensional (2D) dose distribution at reference depths in water was obtained by using the constructed fluence map with a MC simulated pencil beam kernel with consideration of the geometric and structural information of the EPID. Output factor measurements were carried out with the EPID at a nominal source-surface distance of 100 cm for 2 × 2, 3 × 3, 6 × 6, 10 × 10, and 15 × 15 cm(2) fields for all available electron energies, and the results were compared with that measured in a solid water phantom using film and a Farmer-type ion chamber. The dose distributions at a reference depth specific to each energy and the flatness and symmetry of the 10 × 10 cm(2) electron beam were also measured using EPID, and the results were compared with ion chamber array and water scan measurements. Finally, three patient cases with various field sizes and irregular cutout shapes were also investigated.EPID-measured dose changed linearly with the monitor units and showed little ghosting effect for dose rate up to 600 MU/min. The flatness and symmetry measured with the EPID were found to be consistent with ion chamber array and water scan measurements. The EPID-measured output factors for standard square fields of 2 × 2, 3 × 3, 6 × 6, 10 × 10, 15 × 15 cm(2) agreed with film and ion chamber measurements. The average discrepancy between EPID and ion chamber/film measurements was 0.81% ± 0.60% (SD) and 1.34% ± 0.75%, respectively. For the three clinical cases, the difference in output between the EPID- and ion chamber array measured values was found to be 1.13% ± 0.11%, 0.54% ± 0.10%, and 0.74% ± 0.11%, respectively. Furthermore, the γ-index analysis showed an excellent agreement between the EPID- and ion chamber array measured dose distributions: 100% of the pixels passed the criteria of 3%/3 mm. When the γ-index was set to be 2%/2 mm, the pass rate was found to be 99.0% ± 0.07%, 98.2% ± 0.14%, and 100% for the three cases.The EPID dosimetry system developed in this work provides an accurate and reliable tool for routine output measurement and dosimetric verification of electron beam therapy. Coupled with its portability and ease of use, the proposed system promises to replace the current film-based approach for fast and reliable assessment of small and irregular electron field dosimetry.

    View details for DOI 10.1118/1.4922400

    View details for PubMedID 26133618

    View details for PubMedCentralID PMC4474956

  • TU-CD-304-01: FEATURED PRESENTATION and BEST IN PHYSICS (THERAPY): Trajectory Modulated Arc Therapy: Development of Novel Arc Delivery Techniques Integrating Dynamic Table Motion for Extended Volume Treatments. Medical physics Chin, E., Otto, K., Hoppe, R., Million, L., Loo, B., Koong, A., Xing, L., Hsu, A., Fahimian, B. 2015; 42 (6): 3598-?

    Abstract

    Integration of coordinated robotic table motion with inversely-planned arc delivery has the potential to resolve table-top delivery limitations of large-field treatments such as Total Body Irradiation (TBI), Total Lymphoid Irradiation (TLI), and Cranial-Spinal Irradiation (CSI). We formulate the foundation for Trajectory Modulated Arc Therapy (TMAT), and using Varian Developer Mode capabilities, experimentally investigate its practical implementation for such techniques.A MATLAB algorithm was developed for inverse planning optimization of the table motion, MLC positions, and gantry motion under extended-SSD geometry. To maximize the effective field size, delivery trajectories for TMAT TBI were formed with the table rotated at 270° IEC and dropped vertically to 152.5cm SSD. Preliminary testing of algorithm parameters was done through retrospective planning analysis. Robotic delivery was programmed using custom XML scripting on the TrueBeam Developer Mode platform. Final dose was calculated using the Eclipse AAA algorithm. Initial verification of delivery accuracy was measured using OSLDs on a solid water phantom of varying thickness.A comparison of DVH curves demonstrated that dynamic couch motion irradiation was sufficiently approximated by static control points spaced in intervals of less than 2cm. Optimized MLC motion decreased the average lung dose to 68.5% of the prescription dose. The programmed irradiation integrating coordinated table motion was deliverable on a TrueBeam STx linac in 6.7 min. With the couch translating under an open 10cmx20cm field angled at 10°, OSLD measurements along the midline of a solid water phantom at depths of 3, 5, and 9cm were within 3% of the TPS AAA algorithm with an average deviation of 1.2%.A treatment planning and delivery system for Trajectory Modulated Arc Therapy of extended volumes has been established and experimentally demonstrated for TBI. Extension to other treatment techniques such as TLI and CSI is readily achievable through the developed platform. Grant Funding by Varian Medical Systems.

    View details for DOI 10.1118/1.4925570

    View details for PubMedID 26128865

  • Augmenting Atlas-Based Segmentation by Incorporating Image Features Proximal to the Atlas Contours Li, D., Liu, L., Kapp, D. S., Xing, L. AMER ASSOC PHYSICISTS MEDICINE AMER INST PHYSICS. 2015: 3294–95
  • Automating Liver Segmentation Via Combined Global and Local Optimization Li, D., Wang, J., Kapp, D. S., Xing, L. AMER ASSOC PHYSICISTS MEDICINE AMER INST PHYSICS. 2015: 3294
  • WE-AB-BRB-02: Development of a Micro-Sized Dosimeter for Real-Time Dose Monitoring and Small Field Dosimetry. Medical physics Volotskova, O., Jenkins, C., Fahimian, B., Xing, L. 2015; 42 (6): 3649-?

    Abstract

    To investigate a miniature optical dosimeter for real-time, high-resolution dosimetry, and explore its potential applications for in vivo measurements and small field dosimetry.A micro-sized hemispherical (400 µm radius) scintillating detector was constructed from lanthanide activated phosphors doped with Europium (GOS:Eu) and encapsulated in a 17 gauge plastic catheter. A photon counting PMT and CCD-chip spectrometer were used to detect signals emitted from the detector. A single band-passing spectral approach (630nm) was implemented to discriminate the micro-phosphor optical signal from background signals (Cerenkov radiation) in the optical fiber. To test real-time monitoring capabilities, a 3D-printed phantom was used to detect an 192Ir HDR brachytherapy source at locations ranging from 1 to 4 cm radially and 12 cm along the travel axis of the HDR wire. To test the application of the micro-sized detector for small field dosimetry, the linearity of detector was characterized through irradiation of 6MV photon beam at dose-rates ranging from 100 to 600 MU, and the effect of field size was characterized through detections of beams ranging from 30×30 to 1×1 cm2 size.With a 1 second integration time for the spectrometer, the recorded measurements indicated that the micro-sized detector allowed accurate detection of source position at distances of up to 6 cm along the axis of travel in water. EB measurements showed that the detected signal was linearly correlated with dose rate (R^2 = 0.99). The crossbeam profile was determined with a step size of ∼500 µm.Miniaturization of optical dosimeters is shown to be possible through the construction of lanthanide activated doped phosphors detectors. The small size of the detector makes it amenable to a variety of applications, including real-time dose delivery verification during HDR brachytherapy and EB beam calibrations in small fields.

    View details for DOI 10.1118/1.4925843

    View details for PubMedID 26129140

  • SU-F-BRB-11: An Integrated Alternating Direction Method of Multipliers for Treatment Planning Optimization. Medical physics Zarepisheh, M., Ye, Y., Xing, L. 2015; 42 (6): 3532-?

    Abstract

    To propose a new parallel-friendly optimization algorithm, based on alternating direction method of multipliers (ADMM), for solving optimization problems emerging in treatment planning and imagingADMM, emerging as a powerful tool for distributed optimization, is employed here for IMRT treatment planning. We modify the existing ADMM by integrating that with two optimization techniques known as Barzilai-Borwein gradient method and line search. We apply original and integrated ADMM, with various penalty parameter values, on three IMRT treatment planning cases and we compare their performance. Each algorithm terminates once it found a solution within the relative error of 1E-4 from the optimal solution. We also compare the performance of original and integrated ADMM against the commercial optimization software CPLEX.Our experiments on three different cancer sites (prostate, head and neck, and GYN) indicate that integrated ADMM is much faster than original ADMM (5.8 times faster on average). Moreover, while the performance of original ADMM heavily depends on the selected penalty parameter, integrated ADMM performs very robust against the penalty parameter. Compared to the commercial software CPLEX, integrated ADMM turns out to be around 4-7 times faster.Integrated ADMM is a very efficient optimization algorithm to cope with the large-scale optimization problems. It is a parallel-friendly optimization algorithm and provides an idea platform for cloud-based treatment planning and imaging.

    View details for DOI 10.1118/1.4925206

    View details for PubMedID 26128506

  • TH-AB-204-09: High-Sensitivity L-Shell X-Ray Fluorescence CT Imaging of Gold. Medical physics Bazalova-Carter, M., Ahmad, M., Xing, L., Fahrig, R. 2015; 42 (6): 3715-?

    Abstract

    To experimentally demonstrate the feasibility of L-shell x-ray fluorescence CT (XFCT) imaging of gold contrast.We have built an experimental L-shell XFCT imaging system consisting of two photon-counting detectors, a silicon drift detector (SDD) and a CdTe detector, a miniature x-ray tube, and a programmable translation/rotation stage. A 2.8-mm diameter water phantom containing 4-mm vials with gold solutions of 0.06%, 0.08%, and 0.1% Au located at 4mm depth was constructed. The phantom was imaged with the L-shell XFCT system in 1st generation CT geometry with a 1-mm 50-kV x-ray beam. XFCT data was acquired with both detectors placed at ±120° with respect to the excitation beam at 30 translation and 36 rotation steps. L-shell XFCT images were reconstructed with maximum-likelihood expectation-maximization using gold Lα and Lβ fluorescence x-rays for both detectors.SDD L- shell x-ray fluorescence (XRF) signal was approximately 13 times higher than CdTe XRF signal due to the higher measured SDD energy resolution of 220eV @ 14keV compared to the CdTe energy resolution of 660eV. While all 0.06-0.1% Au vials were detectable in the SDD L-shell XFCT image, none of the vials were visible in the CdTe L-shell XFCT image. The contrast-to-noise ratio of the 0.1% Au vial was 87.1 and 3.2 in the SDD and CdTe L-shell XFCT images, respectively. SDD L-shell XFCT signal was linear with Au concentration (R2>0.99). The detectability limits of the presented L-shell XFCT imaging setup were 0.007% and 0.126% Au for L-shell XFCT imaging performed with the SDD and CdTe detector, respectively.We have demonstrated the feasibility of L- shell XFCT imaging of gold located at shallow depths inside a small animal sized phantom. The very high sensitivity of L-shell XFCT, permitting detection of Au concentrations as low as 0.06%, has not previously been achieved experimentally using conventional K-shell XFCT.

    View details for DOI 10.1118/1.4926176

    View details for PubMedID 26129475

  • TH-AB-204-11: X-Ray Fluorescence CT Induced by Proton Beam: Experiments and Simulations. Medical physics Bazalova-Carter, M., Ahmad, M., Matsuura, T., Takao, S., Matsuo, Y., Fahrig, R., Shirato, H., Umegaki, K., Xing, L. 2015; 42 (6): 3716-?

    Abstract

    To demonstrate the feasibility of x-ray fluorescence computed tomography induced with proton beams (pXFCT) for imaging of gold contrast agent by means of experiments and Monte Carlo (MC) simulations.A 7-cm diameter water phantom containing 2.2-cm diameter vials filled with gold solutions of 3-5% Au (percent weight concentration) was imaged with pXFCT using a 7-mm FWHM 220-MeV proton beam and a 3×3mm(2) CdTe photon-counting detector. The phantom was imaged in 1st generation CT scanner geometry using a programmable rotation/translation stage and 21 translation steps separated by 3.3 mm and 36 rotation steps in 10° intervals. Each of the 756 x-ray spectra was acquired for 20 s using 5×10(1)⁰ incident protons with the CdTe detector placed at 45 cm from the isocenter and at 90° with respect to the proton beam. The 220 MeV proton beam was stopped in a solid water beam dump and the total imaging time was 4.2 hours. The experimental pXFCT data acquisition geometry was modeled based on the actual and a simplified geometry with the TOPAS MC code. pXFCT images were reconstructed based on experimental and MC-simulated x-ray spectra with filtered back-projection using Kα peaks of gold.All gold vials were visible in both the experimental and simulated pXFCT images. Contrast-to-noise ratio (CNR) of the 3% Au vial was 5.8 and 11.5 in the experimental and simulated pXFCT image, respectively. pXFCT detection limit of the experimental setup was determined to be 1.8% Au, which was twice as high as the MC-simulated detection limit. Further MC simulations revealed that x-ray scatter from the beam dump was the main contribution to x-ray fluorescence signal contamination.We have demonstrated the feasibility of proton-induced XFCT imaging of gold. We anticipate that pXFCT imaging sensitivity will be improved in an optimized pXFCT imaging system utilizing beam collimation.

    View details for DOI 10.1118/1.4926178

    View details for PubMedID 26129479

  • MO-FG-303-01: FEATURED PRESENTATION and BEST IN PHYSICS (THERAPY): Automating LINAC QA: Design and Testing of An Image Acquisition and Processing System Utilizing a Combination of Radioluminescent Phosphors, Embedded X-Ray Markers and Optical Measurements. Medical physics Jenkins, C., Naczynski, D., Yu, S., Xing, L. 2015; 42 (6): 3566-?

    Abstract

    The recent development of phosphors to visualize radiation beams from linear accelerators (LINAC) offers a unique opportunity for evaluating radiation fields within the context of the treatment space. The purpose of this study was to establish an automated, self-calibrating prototype system for performing quality assurance (QA) measurements.A thin layer of Gd₂O₂S:Tb phosphor and fiducial markers were embedded on several planar faces of a custom-designed phantom. The phantom was arbitrarily placed near iso-center on the couch of a LINAC equipped with on-board megavoltage (MV) and kilovoltage (kV) imagers. A plan consisting of several beams and integrated image acquisitions was delivered. Images of the phantom were collected throughout the delivery. Salient features, such as fiducials, crosshairs and beam edges were then extracted from these images used to calibrate the system, adjust for variations in phantom placement, and perform measurements. Beam edges were visualized by imaging the light generated by the phosphor on the phantom enabling direct comparison with the light field and laser locations. Registration of MV, kV and optical image data was performed using the embedded fiducial markers, enabling comparison of imaging center locations. Measurements specified by TG-142 were calculated and compared with those obtained from a commercially available QA system.The system was able to automatically extract the location of the fiducials, lasers, light field and radiation field from the acquired images regardless of phantom positioning. It was also able to automatically identify the locations of fiducial markers on kV and MV images. All collected measurements were within TG-142 guidelines. The difference between the prototype and commercially available system were less than 0.2 mm.The prototype system demonstrated the capability of accurately and autonomously evaluating various TG-142 parameters independent of operator input and phantom setup.

    View details for DOI 10.1118/1.4925414

    View details for PubMedID 26128711

  • Strained Cyclooctyne as a Molecular Platform for Construction of Multimodal Imaging Probes ANGEWANDTE CHEMIE-INTERNATIONAL EDITION Sun, Y., Ma, X., Cheng, K., Wu, B., Duan, J., Chen, H., Bu, L., Zhang, R., Hu, X., Deng, Z., Xing, L., Hong, X., Cheng, Z. 2015; 54 (20): 5981-5984

    Abstract

    Small-molecule-based multimodal and multifunctional imaging probes play prominent roles in biomedical research and have high clinical translation ability. A novel multimodal imaging platform using base-catalyzed double addition of thiols to a strained internal alkyne such as bicyclo[6.1.0]nonyne has been established in this study, thus allowing highly selective assembly of various functional units in a protecting-group-free manner. Using this molecular platform, novel dual-modality (PET and NIRF) uPAR-targeted imaging probe: (64)Cu-CHS1 was prepared and evaluated in U87MG cells and tumor-bearing mice models. The excellent PET/NIRF imaging characteristics such as good tumor uptake (3.69%ID/g at 2 h post-injection), high tumor contrast, and specificity were achieved in the small-animal models. These attractive imaging properties make (64)Cu-CHS1 a promising probe for clinical use.

    View details for DOI 10.1002/anie.201500941

    View details for Web of Science ID 000354255400031

    View details for PubMedID 25800807

  • Scintillating balloon-enabled fiber-optic system for radionuclide imaging of atherosclerotic plaques. Journal of nuclear medicine Zaman, R. T., Kosuge, H., Carpenter, C., Sun, C., McConnell, M. V., Xing, L. 2015; 56 (5): 771-777

    Abstract

    Atherosclerosis underlies coronary artery disease, the leading cause of death in the United States and worldwide. Detection of coronary plaque inflammation remains challenging. In this study, we developed a scintillating balloon-enabled fiber-optic radionuclide imaging (SBRI) system to improve the sensitivity and resolution of plaque imaging using (18)F-FDG, a marker of vascular inflammation, and tested it in a murine model.The fiber-optic system uses a Complementary Metal-Oxide Silicon (CMOS) camera with a distal ferrule terminated with a wide-angle lens. The novelty of this system is a scintillating balloon in the front of the wide-angle lens to image light from the decay of (18)F-FDG emission signal. To identify the optimal scintillating materials with respect to resolution, we calculated the modulation transfer function of yttrium-aluminum-garnet doped with cerium, anthracene, and calcium fluoride doped with europium (CaF2:Eu) phosphors using an edge pattern and a thin-line optical phantom. The scintillating balloon was then fabricated from 10 mL of silicone RTV catalyst mixed with 1 mL of base and 50 mg of CaF2:Eu per mL. The addition of a lutetium oxyorthosilicate scintillating crystal (500 μm thick) to the balloon was also investigated. The SBRI system was tested in a murine atherosclerosis model: carotid-ligated mice (n = 5) were injected with (18)F-FDG, followed by ex vivo imaging of the macrophage-rich carotid plaques and nonligated controls. Confirmatory imaging of carotid plaques and controls was also performed by an external optical imaging system and autoradiography.Analyses of the different phosphors showed that CaF2:Eu enabled the best resolution of 1.2 μm. The SBRI system detected almost a 4-fold-higher radioluminescence signal from the ligated left carotid artery than the nonligated right carotid: 1.63 × 10(2) ± 4.01 × 10(1) vs. 4.21 × 10(1) ± 2.09 × 10(0) (photon counts), P = 0.006. We found no significant benefit to adding a scintillating crystal to the balloon: 1.65 × 10(2) ± 4.07 × 10(1) vs. 4.44 × 10(1) ± 2.17 × 10(0) (photon counts), P = 0.005. Both external optical imaging and autoradiography confirmed the high signal from the (18)F-FDG in carotid plaques versus controls.This SBRI system provides high-resolution and sensitive detection of (18)F-FDG uptake by murine atherosclerotic plaques.

    View details for DOI 10.2967/jnumed.114.153239

    View details for PubMedID 25858046

  • Optimized Detector Angular Configuration Increases the Sensitivity of X-ray Fluorescence Computed Tomography (XFCT) IEEE TRANSACTIONS ON MEDICAL IMAGING Ahmad, M., Bazalova-Carter, M., Fahrig, R., Xing, L. 2015; 34 (5): 1140-1147

    Abstract

    In this work, we demonstrated that an optimized detector angular configuration based on the anisotropic energy distribution of background scattered X-rays improves X-ray fluorescence computed tomography (XFCT) detection sensitivity. We built an XFCT imaging system composed of a bench-top fluoroscopy X-ray source, a CdTe X-ray detector, and a phantom motion stage. We imaged a 6.4-cm-diameter phantom containing different concentrations of gold solution and investigated the effect of detector angular configuration on XFCT image quality. Based on our previous theoretical study, three detector angles were considered. The X-ray fluorescence detector was first placed at 145 (°) (approximating back-scatter) to minimize scatter X-rays. XFCT image quality was compared to images acquired with the detector at 60 (°) (forward-scatter) and 90 (°) (side-scatter). The datasets for the three different detector positions were also combined to approximate an isotropically arranged detector. The sensitivity was optimized with detector in the 145 (°) back-scatter configuration counting the 78-keV gold Kβ1 X-rays. The improvement arose from the reduced energy of scattered X-ray at the 145 (°) position and the large energy separation from gold K β1 X-rays. The lowest detected concentration in this configuration was 2.5 mgAu/mL (or 0.25% Au with SNR = 4.3). This concentration could not be detected with the 60 (°) , 90 (°) , or isotropic configurations (SNRs = 1.3, 0, 2.3, respectively). XFCT imaging dose of 14 mGy was in the range of typical clinical X-ray CT imaging doses. To our knowledge, the sensitivity achieved in this experiment is the highest in any XFCT experiment using an ordinary bench-top X-ray source in a phantom larger than a mouse ( > 3 cm).

    View details for DOI 10.1109/TMI.2014.2376813

    View details for Web of Science ID 000353899600011

    View details for PubMedID 25474808

  • Beam's-eye-view dosimetrics (BEVD) guided rotational station parameter optimized radiation therapy (SPORT) planning based on reweighted total-variation minimization PHYSICS IN MEDICINE AND BIOLOGY Kim, H., Li, R., Lee, R., Xing, L. 2015; 60 (5): N71-N82

    Abstract

    Conventional VMAT optimizes aperture shapes and weights at uniformly sampled stations, which is a generalization of the concept of a control point. Recently, rotational station parameter optimized radiation therapy (SPORT) has been proposed to improve the plan quality by inserting beams to the regions that demand additional intensity modulations, thus formulating nonuniform beam sampling. This work presents a new rotational SPORT planning strategy based on reweighted total-variation (TV) minimization (min.), using beam’s-eye-view dosimetrics (BEVD) guided beam selection. The convex programming based reweighted TV min. assures the simplified fluence-map, which facilitates single-aperture selection at each station for single-arc delivery. For the rotational arc treatment planning and non-uniform beam angle setting, the mathematical model needs to be modified by additional penalty term describing the fluence-map similarity and by determination of appropriate angular weighting factors. The proposed algorithm with additional penalty term is capable of achieving more efficient and deliverable plans adaptive to the conventional VMAT and SPORT planning schemes by reducing the dose delivery time about 5 to 10 s in three clinical cases (one prostate and two head-and-neck (HN) cases with a single and multiple targets). The BEVD guided beam selection provides effective and yet easy calculating methodology to select angles for denser, non-uniform angular sampling in SPORT planning. Our BEVD guided SPORT treatment schemes improve the dose sparing to femoral heads in the prostate and brainstem, parotid glands and oral cavity in the two HN cases, where the mean dose reduction of those organs ranges from 0.5 to 2.5 Gy. Also, it increases the conformation number assessing the dose conformity to the target from 0.84, 0.75 and 0.74 to 0.86, 0.79 and 0.80 in the prostate and two HN cases, while preserving the delivery efficiency, relative to conventional single-arc VMAT plans.

    View details for DOI 10.1088/0031-9155/60/5/N71

    View details for PubMedID 25675281

  • Optimization approaches to volumetric modulated arc therapy planning MEDICAL PHYSICS Unkelbach, J., Bortfeld, T., Craft, D., Alber, M., Bangert, M., Bokrantz, R., Chen, D., Li, R., Xing, L., Men, C., Nill, S., Papp, D., Romeijn, E., Salari, E. 2015; 42 (3): 1367-1377

    Abstract

    Volumetric modulated arc therapy (VMAT) has found widespread clinical application in recent years. A large number of treatment planning studies have evaluated the potential for VMAT for different disease sites based on the currently available commercial implementations of VMAT planning. In contrast, literature on the underlying mathematical optimization methods used in treatment planning is scarce. VMAT planning represents a challenging large scale optimization problem. In contrast to fluence map optimization in intensity-modulated radiotherapy planning for static beams, VMAT planning represents a nonconvex optimization problem. In this paper, the authors review the state-of-the-art in VMAT planning from an algorithmic perspective. Different approaches to VMAT optimization, including arc sequencing methods, extensions of direct aperture optimization, and direct optimization of leaf trajectories are reviewed. Their advantages and limitations are outlined and recommendations for improvements are discussed.

    View details for DOI 10.1118/1.4908224

    View details for Web of Science ID 000350661500022

    View details for PubMedID 25735291

  • Oral administration of aflatoxin G(1) induces chronic alveolar inflammation associated with lung tumorigenesis TOXICOLOGY LETTERS Liu, C., Shen, H., Yi, L., Shao, P., Soulika, A. M., Meng, X., Xing, L., Yan, X., Zhang, X. 2015; 232 (3): 547-556

    Abstract

    Our previous studies showed oral gavage of aflatoxin G₁ (AFG₁) induced lung adenocarcinoma in NIH mice. We recently found that a single intratracheal administration of AFG₁ caused chronic inflammatory changes in rat alveolar septum. Here, we examine whether oral gavage of AFG₁ induces chronic lung inflammation and how it contributes to carcinogenesis. We evaluated chronic lung inflammatory responses in Balb/c mice after oral gavage of AFG₁ for 1, 3 and 6 months. Inflammatory responses were heightened in the lung alveolar septum, 3 and 6 months after AFG₁ treatment, evidenced by increased macrophages and lymphocytes infiltration, up-regulation of NF-κB and p-STAT3, and cytokines production. High expression levels of superoxide dismutase (SOD-2) and hemoxygenase-1 (HO-1), two established markers of oxidative stress, were detected in alveolar epithelium of AFG₁-treated mice. Promoted alveolar type II cell (AT-II) proliferation in alveolar epithelium and angiogenesis, as well as increased COX-2 expression were also observed in lung tissues of AFG₁-treated mice. Furthermore, we prolonged survival of the mice in the above model for another 6 months to examine the contribution of AFG₁-induced chronic inflammation to lung tumorigenesis. Twelve months later, we observed that AFG₁ induced alveolar epithelial hyperplasia and adenocarcinoma in Balb/c mice. Up-regulation of NF-κB, p-STAT3, and COX-2 was also induced in lung adenocarcinoma, thus establishing a link between AFG₁-induced chronic inflammation and lung tumorigenesis. This is the first study to show that oral administration of AFG₁ could induce chronic lung inflammation, which may provide a pro-tumor microenvironment to contribute to lung tumorigenesis.

    View details for DOI 10.1016/j.toxlet.2014.11.002

    View details for Web of Science ID 000348200800001

    View details for PubMedID 25445582

  • Proton-induced x-ray fluorescence CT imaging. Medical physics Bazalova-Carter, M., Ahmad, M., Matsuura, T., Takao, S., Matsuo, Y., Fahrig, R., Shirato, H., Umegaki, K., Xing, L. 2015; 42 (2): 900-?

    Abstract

    To demonstrate the feasibility of proton-induced x-ray fluorescence CT (pXFCT) imaging of gold in a small animal sized object by means of experiments and Monte Carlo (MC) simulations.First, proton-induced gold x-ray fluorescence (pXRF) was measured as a function of gold concentration. Vials of 2.2 cm in diameter filled with 0%-5% Au solutions were irradiated with a 220 MeV proton beam and x-ray fluorescence induced by the interaction of protons, and Au was detected with a 3 × 3 mm(2) CdTe detector placed at 90° with respect to the incident proton beam at a distance of 45 cm from the vials. Second, a 7-cm diameter water phantom containing three 2.2-diameter vials with 3%-5% Au solutions was imaged with a 7-mm FWHM 220 MeV proton beam in a first generation CT scanning geometry. X-rays scattered perpendicular to the incident proton beam were acquired with the CdTe detector placed at 45 cm from the phantom positioned on a translation/rotation stage. Twenty one translational steps spaced by 3 mm at each of 36 projection angles spaced by 10° were acquired, and pXFCT images of the phantom were reconstructed with filtered back projection. A simplified geometry of the experimental data acquisition setup was modeled with the MC TOPAS code, and simulation results were compared to the experimental data.A linear relationship between gold pXRF and gold concentration was observed in both experimental and MC simulation data (R(2) > 0.99). All Au vials were apparent in the experimental and simulated pXFCT images. Specifically, the 3% Au vial was detectable in the experimental [contrast-to-noise ratio (CNR) = 5.8] and simulated (CNR = 11.5) pXFCT image. Due to fluorescence x-ray attenuation in the higher concentration vials, the 4% and 5% Au contrast were underestimated by 10% and 15%, respectively, in both the experimental and simulated pXFCT images.Proton-induced x-ray fluorescence CT imaging of 3%-5% gold solutions in a small animal sized water phantom has been demonstrated for the first time by means of experiments and MC simulations.

    View details for DOI 10.1118/1.4906169

    View details for PubMedID 25652502

    View details for PubMedCentralID PMC4312343

  • Simultaneous beam sampling and aperture shape optimization for SPORT. Medical physics Zarepisheh, M., Li, R., Ye, Y., Xing, L. 2015; 42 (2): 1012-?

    Abstract

    Station parameter optimized radiation therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital linear accelerators, in which the station parameters of a delivery system, such as aperture shape and weight, couch position/angle, gantry/collimator angle, can be optimized simultaneously. SPORT promises to deliver remarkable radiation dose distributions in an efficient manner, yet there exists no optimization algorithm for its implementation. The purpose of this work is to develop an algorithm to simultaneously optimize the beam sampling and aperture shapes.The authors build a mathematical model with the fundamental station point parameters as the decision variables. To solve the resulting large-scale optimization problem, the authors devise an effective algorithm by integrating three advanced optimization techniques: column generation, subgradient method, and pattern search. Column generation adds the most beneficial stations sequentially until the plan quality improvement saturates and provides a good starting point for the subsequent optimization. It also adds the new stations during the algorithm if beneficial. For each update resulted from column generation, the subgradient method improves the selected stations locally by reshaping the apertures and updating the beam angles toward a descent subgradient direction. The algorithm continues to improve the selected stations locally and globally by a pattern search algorithm to explore the part of search space not reachable by the subgradient method. By combining these three techniques together, all plausible combinations of station parameters are searched efficiently to yield the optimal solution.A SPORT optimization framework with seamlessly integration of three complementary algorithms, column generation, subgradient method, and pattern search, was established. The proposed technique was applied to two previously treated clinical cases: a head and neck and a prostate case. It significantly improved the target conformality and at the same time critical structure sparing compared with conventional intensity modulated radiation therapy (IMRT). In the head and neck case, for example, the average PTV coverage D99% for two PTVs, cord and brainstem max doses, and right parotid gland mean dose were improved, respectively, by about 7%, 37%, 12%, and 16%.The proposed method automatically determines the number of the stations required to generate a satisfactory plan and optimizes simultaneously the involved station parameters, leading to improved quality of the resultant treatment plans as compared with the conventional IMRT plans.

    View details for DOI 10.1118/1.4906253

    View details for PubMedID 25652514

  • Independent calculation of monitor units for VMAT and SPORT. Medical physics Chen, X., Bush, K., Ding, A., Xing, L. 2015; 42 (2): 918-?

    Abstract

    Dose and monitor units (MUs) represent two important facets of a radiation therapy treatment. In current practice, verification of a treatment plan is commonly done in dose domain, in which a phantom measurement or forward dose calculation is performed to examine the dosimetric accuracy and the MU settings of a given treatment plan. While it is desirable to verify directly the MU settings, a computational framework for obtaining the MU values from a known dose distribution has yet to be developed. This work presents a strategy to calculate independently the MUs from a given dose distribution of volumetric modulated arc therapy (VMAT) and station parameter optimized radiation therapy (SPORT).The dose at a point can be expressed as a sum of contributions from all the station points (or control points). This relationship forms the basis of the proposed MU verification technique. To proceed, the authors first obtain the matrix elements which characterize the dosimetric contribution of the involved station points by computing the doses at a series of voxels, typically on the prescription surface of the VMAT/SPORT treatment plan, with unit MU setting for all the station points. An in-house Monte Carlo (MC) software is used for the dose matrix calculation. The MUs of the station points are then derived by minimizing the least-squares difference between doses computed by the treatment planning system (TPS) and that of the MC for the selected set of voxels on the prescription surface. The technique is applied to 16 clinical cases with a variety of energies, disease sites, and TPS dose calculation algorithms.For all plans except the lung cases with large tissue density inhomogeneity, the independently computed MUs agree with that of TPS to within 2.7% for all the station points. In the dose domain, no significant difference between the MC and Eclipse Anisotropic Analytical Algorithm (AAA) dose distribution is found in terms of isodose contours, dose profiles, gamma index, and dose volume histogram (DVH) for these cases. For the lung cases, the MC-calculated MUs differ significantly from that of the treatment plan computed using AAA. However, the discrepancies are reduced to within 3% when the TPS dose calculation algorithm is switched to a transport equation-based technique (Acuros™). Comparison in the dose domain between the MC and Eclipse AAA/Acuros calculation yields conclusion consistent with the MU calculation.A computational framework relating the MU and dose domains has been established. The framework does not only enable them to verify the MU values of the involved station points of a VMAT plan directly in the MU domain but also provide a much needed mechanism to adaptively modify the MU values of the station points in accordance to a specific change in the dose domain.

    View details for DOI 10.1118/1.4906185

    View details for PubMedID 25652504

    View details for PubMedCentralID PMC4312348

  • X-ray-Induced Shortwave Infrared Biomedical Imaging Using Rare-Earth Nanoprobes. Nano letters Naczynski, D. J., Sun, C., Türkcan, S., Jenkins, C., Koh, A. L., Ikeda, D., Pratx, G., Xing, L. 2015; 15 (1): 96-102

    Abstract

    Shortwave infrared (SWIR or NIR-II) light provides significant advantages for imaging biological structures due to reduced autofluorescence and photon scattering. Here, we report on the development of rare-earth nanoprobes that exhibit SWIR luminescence following X-ray irradiation. We demonstrate the ability of X-ray-induced SWIR luminescence (X-IR) to monitor biodistribution and map lymphatic drainage. Our results indicate X-IR imaging is a promising new modality for preclinical applications and has potential for dual-modality molecular disease imaging.

    View details for DOI 10.1021/nl504123r

    View details for PubMedID 25485705

    View details for PubMedCentralID PMC4296927

  • Monitoring external beam radiotherapy using real-time beam visualization. Medical physics Jenkins, C. H., Naczynski, D. J., Yu, S. S., Xing, L. 2015; 42 (1): 5-?

    Abstract

    To characterize the performance of a novel radiation therapy monitoring technique that utilizes a flexible scintillating film, common optical detectors, and image processing algorithms for real-time beam visualization (RT-BV).Scintillating films were formed by mixing Gd2O2S:Tb (GOS) with silicone and casting the mixture at room temperature. The films were placed in the path of therapeutic beams generated by medical linear accelerators (LINAC). The emitted light was subsequently captured using a CMOS digital camera. Image processing algorithms were used to extract the intensity, shape, and location of the radiation field at various beam energies, dose rates, and collimator locations. The measurement results were compared with known collimator settings to validate the performance of the imaging system.The RT-BV system achieved a sufficient contrast-to-noise ratio to enable real-time monitoring of the LINAC beam at 20 fps with normal ambient lighting in the LINAC room. The RT-BV system successfully identified collimator movements with sub-millimeter resolution.The RT-BV system is capable of localizing radiation therapy beams with sub-millimeter precision and tracking beam movement at video-rate exposure.

    View details for DOI 10.1118/1.4901255

    View details for PubMedID 25563243

    View details for PubMedCentralID PMC4265127

  • Feasibility-Seeking and Superiorization Algorithms Applied to Inverse Treatment Planning in Radiation Therapy Davidi, R., Censor, Y., Schulte, R. W., Geneser, S., Xing, L., Reich, S., Zaslavski, A. J. AMER MATHEMATICAL SOC. 2015: 83–92
  • Accuracy of surface registration compared to conventional volumetric registration in patient positioning for head-and-neck radiotherapy: A simulation study using patient data MEDICAL PHYSICS Kim, Y., Li, R., Na, Y. H., Lee, R., Xing, L. 2014; 41 (12)

    Abstract

    3D optical surface imaging has been applied to patient positioning in radiation therapy (RT). The optical patient positioning system is advantageous over conventional method using cone-beam computed tomography (CBCT) in that it is radiation free, frameless, and is capable of real-time monitoring. While the conventional radiographic method uses volumetric registration, the optical system uses surface matching for patient alignment. The relative accuracy of these two methods has not yet been sufficiently investigated. This study aims to investigate the theoretical accuracy of the surface registration based on a simulation study using patient data.This study compares the relative accuracy of surface and volumetric registration in head-and-neck RT. The authors examined 26 patient data sets, each consisting of planning CT data acquired before treatment and patient setup CBCT data acquired at the time of treatment. As input data of surface registration, patient's skin surfaces were created by contouring patient skin from planning CT and treatment CBCT. Surface registration was performed using the iterative closest points algorithm by point-plane closest, which minimizes the normal distance between source points and target surfaces. Six degrees of freedom (three translations and three rotations) were used in both surface and volumetric registrations and the results were compared. The accuracy of each method was estimated by digital phantom tests.Based on the results of 26 patients, the authors found that the average and maximum root-mean-square translation deviation between the surface and volumetric registrations were 2.7 and 5.2 mm, respectively. The residual error of the surface registration was calculated to have an average of 0.9 mm and a maximum of 1.7 mm.Surface registration may lead to results different from those of the conventional volumetric registration. Only limited accuracy can be achieved for patient positioning with an approach based solely on surface information.

    View details for DOI 10.1118/1.4898103

    View details for Web of Science ID 000346176300004

    View details for PubMedID 25471948

    View details for PubMedCentralID PMC4235652

  • A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning PHYSICS IN MEDICINE AND BIOLOGY Gudur, M. S., Hara, W., Le, Q., Wang, L., Xing, L., Li, R. 2014; 59 (21): 6595-6606

    Abstract

    MRI significantly improves the accuracy and reliability of target delineation in radiation therapy for certain tumors due to its superior soft tissue contrast compared to CT. A treatment planning process with MRI as the sole imaging modality will eliminate systematic CT/MRI co-registration errors, reduce cost and radiation exposure, and simplify clinical workflow. However, MRI lacks the key electron density information necessary for accurate dose calculation and generating reference images for patient setup. The purpose of this work is to develop a unifying method to derive electron density from standard T1-weighted MRI. We propose to combine both intensity and geometry information into a unifying probabilistic Bayesian framework for electron density mapping. For each voxel, we compute two conditional probability density functions (PDFs) of electron density given its: (1) T1-weighted MRI intensity, and (2) geometry in a reference anatomy, obtained by deformable image registration between the MRI of the atlas and test patient. The two conditional PDFs containing intensity and geometry information are combined into a unifying posterior PDF, whose mean value corresponds to the optimal electron density value under the mean-square error criterion. We evaluated the algorithm's accuracy of electron density mapping and its ability to detect bone in the head for eight patients, using an additional patient as the atlas or template. Mean absolute HU error between the estimated and true CT, as well as receiver operating characteristics for bone detection (HU > 200) were calculated. The performance was compared with a global intensity approach based on T1 and no density correction (set whole head to water). The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 126, compared with 139 for deformable registration (p = 2  ×  10(-4)), 283 for the intensity approach (p = 2  ×  10(-6)) and 282 without density correction (p = 5  ×  10(-6)). For 90% sensitivity in bone detection, the proposed method achieved a specificity of 86%, compared with 80, 11 and 10% using deformable registration, intensity and without density correction, respectively. Notably, the Bayesian approach was more robust against anatomical differences between patients, with a specificity of 62% in the worst case (patient), compared to 30% specificity in registration-based approach. In conclusion, the proposed unifying Bayesian method provides accurate electron density estimation and bone detection from MRI of the head with highly heterogeneous anatomy.

    View details for DOI 10.1088/0031-9155/59/21/6595

    View details for Web of Science ID 000343092900020

  • A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning. Physics in medicine and biology Gudur, M. S., Hara, W., Le, Q., Wang, L., Xing, L., Li, R. 2014; 59 (21): 6595-6606

    Abstract

    MRI significantly improves the accuracy and reliability of target delineation in radiation therapy for certain tumors due to its superior soft tissue contrast compared to CT. A treatment planning process with MRI as the sole imaging modality will eliminate systematic CT/MRI co-registration errors, reduce cost and radiation exposure, and simplify clinical workflow. However, MRI lacks the key electron density information necessary for accurate dose calculation and generating reference images for patient setup. The purpose of this work is to develop a unifying method to derive electron density from standard T1-weighted MRI. We propose to combine both intensity and geometry information into a unifying probabilistic Bayesian framework for electron density mapping. For each voxel, we compute two conditional probability density functions (PDFs) of electron density given its: (1) T1-weighted MRI intensity, and (2) geometry in a reference anatomy, obtained by deformable image registration between the MRI of the atlas and test patient. The two conditional PDFs containing intensity and geometry information are combined into a unifying posterior PDF, whose mean value corresponds to the optimal electron density value under the mean-square error criterion. We evaluated the algorithm's accuracy of electron density mapping and its ability to detect bone in the head for eight patients, using an additional patient as the atlas or template. Mean absolute HU error between the estimated and true CT, as well as receiver operating characteristics for bone detection (HU > 200) were calculated. The performance was compared with a global intensity approach based on T1 and no density correction (set whole head to water). The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 126, compared with 139 for deformable registration (p = 2  ×  10(-4)), 283 for the intensity approach (p = 2  ×  10(-6)) and 282 without density correction (p = 5  ×  10(-6)). For 90% sensitivity in bone detection, the proposed method achieved a specificity of 86%, compared with 80, 11 and 10% using deformable registration, intensity and without density correction, respectively. Notably, the Bayesian approach was more robust against anatomical differences between patients, with a specificity of 62% in the worst case (patient), compared to 30% specificity in registration-based approach. In conclusion, the proposed unifying Bayesian method provides accurate electron density estimation and bone detection from MRI of the head with highly heterogeneous anatomy.

    View details for DOI 10.1088/0031-9155/59/21/6595

    View details for PubMedID 25321341

  • Cerenkov Luminescence Endoscopy: Improved Molecular Sensitivity with beta(-)-Emitting Radiotracers JOURNAL OF NUCLEAR MEDICINE Carpenter, C. M., Ma, X., Liu, H., Sun, C., Pratx, G., Wang, J., Gambhir, S. S., Xing, L., Cheng, Z. 2014; 55 (11): 1905-1909

    Abstract

    Cerenkov luminescence endoscopy (CLE) is an optical technique that captures the Cerenkov photons emitted from highly energetic moving charged particles (β(+) or β(-)) and can be used to monitor the distribution of many clinically available radioactive probes. A main limitation of CLE is its limited sensitivity to small concentrations of radiotracer, especially when used with a light guide. We investigated the improvement in the sensitivity of CLE brought about by using a β(-) radiotracer that improved Cerenkov signal due to both higher β-particle energy and lower γ noise in the imaging optics because of the lack of positron annihilation.The signal-to-noise ratio (SNR) of (90)Y was compared with that of (18)F in both phantoms and small-animal tumor models. Sensitivity and noise characteristics were demonstrated using vials of activity both at the surface and beneath 1 cm of tissue. Rodent U87MG glioma xenograft models were imaged with radiotracers bound to arginine-glycine-aspartate (RGD) peptides to determine the SNR.γ noise from (18)F was demonstrated by both an observed blurring across the field of view and a more pronounced fall-off with distance. A decreased γ background and increased energy of the β particles resulted in a 207-fold improvement in the sensitivity of (90)Y compared with (18)F in phantoms. (90)Y-bound RGD peptide produced a higher tumor-to-background SNR than (18)F in a mouse model.The use of (90)Y for Cerenkov endoscopic imaging enabled superior results compared with an (18)F radiotracer.

    View details for DOI 10.2967/jnumed.114.139105

    View details for Web of Science ID 000344209200024

  • Cerenkov luminescence endoscopy: improved molecular sensitivity with ß--emitting radiotracers. Journal of nuclear medicine : official publication, Society of Nuclear Medicine Carpenter, C. M., Ma, X., Liu, H., Sun, C., Pratx, G., Wang, J., Gambhir, S. S., Xing, L., Cheng, Z. 2014; 55 (11): 1905-1909

    Abstract

    Cerenkov luminescence endoscopy (CLE) is an optical technique that captures the Cerenkov photons emitted from highly energetic moving charged particles (β(+) or β(-)) and can be used to monitor the distribution of many clinically available radioactive probes. A main limitation of CLE is its limited sensitivity to small concentrations of radiotracer, especially when used with a light guide. We investigated the improvement in the sensitivity of CLE brought about by using a β(-) radiotracer that improved Cerenkov signal due to both higher β-particle energy and lower γ noise in the imaging optics because of the lack of positron annihilation.The signal-to-noise ratio (SNR) of (90)Y was compared with that of (18)F in both phantoms and small-animal tumor models. Sensitivity and noise characteristics were demonstrated using vials of activity both at the surface and beneath 1 cm of tissue. Rodent U87MG glioma xenograft models were imaged with radiotracers bound to arginine-glycine-aspartate (RGD) peptides to determine the SNR.γ noise from (18)F was demonstrated by both an observed blurring across the field of view and a more pronounced fall-off with distance. A decreased γ background and increased energy of the β particles resulted in a 207-fold improvement in the sensitivity of (90)Y compared with (18)F in phantoms. (90)Y-bound RGD peptide produced a higher tumor-to-background SNR than (18)F in a mouse model.The use of (90)Y for Cerenkov endoscopic imaging enabled superior results compared with an (18)F radiotracer.

    View details for DOI 10.2967/jnumed.114.139105

    View details for PubMedID 25300598

  • Transferring Biomarker into Molecular Probe: Melanin Nanoparticle as a Naturally Active Platform for Multimodality Imaging JOURNAL OF THE AMERICAN CHEMICAL SOCIETY Fan, Q., Cheng, K., Hu, X., Ma, X., Zhang, R., Yang, M., Lu, X., Xing, L., Huang, W., Gambhir, S. S., Cheng, Z. 2014; 136 (43): 15185-15194

    Abstract

    Developing multifunctional and easily prepared nanoplatforms with integrated different modalities is highly challenging for molecular imaging. Here, we report the successful transferring an important molecular target, melanin, into a novel mul-timodality imaging nanoplatform. Melanin is abundantly expressed in melanotic melanomas and thus has been actively studied as a target for melanoma imaging. In our work, the multifunctional biopolymer nanoplatform based on ultrasmall (< 10 nm) water-soluble melanin nanoparticle (MNP) was developed and showed unique photoacoustic property and natural binding ability with metal ions (for example, 64Cu2+, Fe3+). Therefore MNP can serve not only as a photoacoustic contrast agent, but also as a nanoplatform for positron emission tomography (PET) and magnetic resonance imaging (MRI). Traditional passive nanoplatforms require complicated and time-consuming processes for pre-building reporting moieties or chemical modifications using active groups to integrate different contrast properties into one entity. In comparison, utilizing functional biomarker melanin can greatly simplify the building process. We further conjugated αvβ3 integrins targeting peptide, cyclic c(RGDfC) peptide, to MNPs and this allowed targeting of these nanoparticles to allow for greater U87MG tumor accumulation than that simply possible due to the enhanced permeability and retention (EPR) effect. The multimodal properties of MNPs demonstrate the high potential of endogenous materials with multifunctions as nanoplatforms for molecular theranostics and clinical translation.

    View details for DOI 10.1021/ja505412p

    View details for Web of Science ID 000344042900018

  • Transferring biomarker into molecular probe: melanin nanoparticle as a naturally active platform for multimodality imaging. Journal of the American Chemical Society Fan, Q., Cheng, K., Hu, X., Ma, X., Zhang, R., Yang, M., Lu, X., Xing, L., Huang, W., Gambhir, S. S., Cheng, Z. 2014; 136 (43): 15185-15194

    Abstract

    Developing multifunctional and easily prepared nanoplatforms with integrated different modalities is highly challenging for molecular imaging. Here, we report the successful transferring an important molecular target, melanin, into a novel mul-timodality imaging nanoplatform. Melanin is abundantly expressed in melanotic melanomas and thus has been actively studied as a target for melanoma imaging. In our work, the multifunctional biopolymer nanoplatform based on ultrasmall (< 10 nm) water-soluble melanin nanoparticle (MNP) was developed and showed unique photoacoustic property and natural binding ability with metal ions (for example, 64Cu2+, Fe3+). Therefore MNP can serve not only as a photoacoustic contrast agent, but also as a nanoplatform for positron emission tomography (PET) and magnetic resonance imaging (MRI). Traditional passive nanoplatforms require complicated and time-consuming processes for pre-building reporting moieties or chemical modifications using active groups to integrate different contrast properties into one entity. In comparison, utilizing functional biomarker melanin can greatly simplify the building process. We further conjugated αvβ3 integrins targeting peptide, cyclic c(RGDfC) peptide, to MNPs and this allowed targeting of these nanoparticles to allow for greater U87MG tumor accumulation than that simply possible due to the enhanced permeability and retention (EPR) effect. The multimodal properties of MNPs demonstrate the high potential of endogenous materials with multifunctions as nanoplatforms for molecular theranostics and clinical translation.

    View details for DOI 10.1021/ja505412p

    View details for PubMedID 25292385

  • Dual-gated volumetric modulated arc therapy RADIATION ONCOLOGY Fahimian, B., Wu, J., Wu, H., Geneser, S., Xing, L. 2014; 9

    Abstract

    Gated Volumetric Modulated Arc Therapy (VMAT) is an emerging radiation therapy modality for treatment of tumors affected by respiratory motion. However, gating significantly prolongs the treatment time, as delivery is only activated during a single respiratory phase. To enhance the efficiency of gated VMAT delivery, a novel dual-gated VMAT (DG-VMAT) technique, in which delivery is executed at both exhale and inhale phases in a given arc rotation, is developed and experimentally evaluated.Arc delivery at two phases is realized by sequentially interleaving control points consisting of MUs, MLC sequences, and angles of VMAT plans generated at the exhale and inhale phases. Dual-gated delivery is initiated when a respiration gating signal enters the exhale window; when the exhale delivery concludes, the beam turns off and the gantry rolls back to the starting position for the inhale window. The process is then repeated until both inhale and exhale arcs are fully delivered. DG-VMAT plan delivery accuracy was assessed using a pinpoint chamber and diode array phantom undergoing programmed motion.DG-VMAT delivery was experimentally implemented through custom XML scripting in Varian's TrueBeam™ STx Developer Mode. Relative to single gated delivery at exhale, the treatment time was improved by 95.5% for a sinusoidal breathing pattern. The pinpoint chamber dose measurement agreed with the calculated dose within 0.7%. For the DG-VMAT delivery, 97.5% of the diode array measurements passed the 3%/3 mm gamma criterion.The feasibility of DG-VMAT delivery scheme has been experimentally demonstrated for the first time. By leveraging the stability and natural pauses that occur at end-inspiration and end-exhalation, DG-VMAT provides a practical method for enhancing gated delivery efficiency by up to a factor of two.

    View details for DOI 10.1186/1748-717X-9-209

    View details for Web of Science ID 000344119800001

    View details for PubMedCentralID PMC4261568

  • Fiber-Optic System for Dual-Modality Imaging of Glucose Probes F-18-FDG and 6-NBDG in Atherosclerotic Plaques PLOS ONE Zaman, R. T., Kosuge, H., Pratx, G., Carpenter, C., Xing, L., McConnell, M. V. 2014; 9 (9)

    Abstract

    Atherosclerosis is a progressive inflammatory condition that underlies coronary artery disease (CAD)-the leading cause of death in the United States. Thus, the ultimate goal of this research is to advance our understanding of human CAD by improving the characterization of metabolically active vulnerable plaques within the coronary arteries using a novel catheter-based imaging system. The aims of this study include (1) developing a novel fiber-optic imaging system with a scintillator to detect both 18F and fluorescent glucose probes, and (2) validating the system on ex vivo murine plaques.A novel design implements a flexible fiber-optic catheter consisting of both a radio-luminescence and a fluorescence imaging system to detect radionuclide 18F-fluorodeoxyglucose (18F-FDG) and the fluorescent analog 6-(N-(7-Nitrobenz-2-oxa-1,3-diazol-4-yl)amino)-6-Deoxyglucose (6-NBDG), respectively. Murine macrophage-rich atherosclerotic carotid plaques were imaged ex vivo after intravenous delivery of 18F-FDG or 6-NBDG. Confirmatory optical imaging by IVIS-200 and autoradiography were also performed.Our fiber-optic imaging system successfully visualized both 18F-FDG and 6-NBDG probes in atherosclerotic plaques. For 18F-FDG, the ligated left carotid arteries (LCs) exhibited 4.9-fold higher radioluminescence signal intensity compared to the non-ligated right carotid arteries (RCs) (2.6 × 10(4) ± 1.4 × 10(3) vs. 5.4 × 10(3) ± 1.3 × 10(3) A.U., P = 0.008). Similarly, for 6-NBDG, the ligated LCs emitted 4.3-fold brighter fluorescent signals than the control RCs (1.6 × 10(2) ± 2.7 × 10(1) vs. 3.8 × 10(1) ± 5.9 A.U., P = 0.002). The higher uptake of both 18F-FDG and 6-NBDG in ligated LCs were confirmed with the IVIS-200 system. Autoradiography further verified the higher uptake of 18F-FDG by the LCs.This novel fiber-optic imaging system was sensitive to both radionuclide and fluorescent glucose probes taken up by murine atherosclerotic plaques. In addition, 6-NBDG is a promising novel fluorescent probe for detecting macrophage-rich atherosclerotic plaques.

    View details for DOI 10.1371/journal.pone.0108108

    View details for Web of Science ID 000342921200083

    View details for PubMedCentralID PMC4169475

  • Quality control procedures for dynamic treatment delivery techniques involving couch motion. Medical physics Yu, V. Y., Fahimian, B. P., Xing, L., Hristov, D. H. 2014; 41 (8): 081712-?

    Abstract

    In this study, the authors introduce and demonstrate quality control procedures for evaluating the geometric and dosimetric fidelity of dynamic treatment delivery techniques involving treatment couch motion synchronous with gantry and multileaf collimator (MLC). Tests were designed to evaluate positional accuracy, velocity constancy and accuracy for dynamic couch motion under a realistic weight load. A test evaluating the geometric accuracy of the system in delivering treatments over complex dynamic trajectories was also devised. Custom XML scripts that control the Varian TrueBeam™ STx (Serial #3) axes in Developer Mode were written to implement the delivery sequences for the tests. Delivered dose patterns were captured with radiographic film or the electronic portal imaging device. The couch translational accuracy in dynamic treatment mode was 0.01 cm. Rotational accuracy was within 0.3°, with 0.04 cm displacement of the rotational axis. Dose intensity profiles capturing the velocity constancy and accuracy for translations and rotation exhibited standard deviation and maximum deviations below 3%. For complex delivery involving MLC and couch motions, the overall translational accuracy for reproducing programmed patterns was within 0.06 cm. The authors conclude that in Developer Mode, TrueBeam™ is capable of delivering dynamic treatment delivery techniques involving couch motion with good geometric and dosimetric fidelity.

    View details for DOI 10.1118/1.4886757

    View details for PubMedID 25086522

  • Feasibility of a Table-Top Total Body Irradiation Technique using Robotic Couch Motion Chin, E., Otto, K., Hoppe, R., Hsu, A., Loo, B., Million, L., Xing, L., Fahimian, B. AMER ASSOC PHYSICISTS MEDICINE AMER INST PHYSICS. 2014: 15

    View details for DOI 10.1118/1.4894898

    View details for Web of Science ID 000341068100118

  • Quality control procedures for dynamic treatment delivery techniques involving couch motion MEDICAL PHYSICS Yu, V. Y., Fahinnian, B. R., Xing, L., Hristov, D. H. 2014; 41 (8): 164-170

    Abstract

    In this study, the authors introduce and demonstrate quality control procedures for evaluating the geometric and dosimetric fidelity of dynamic treatment delivery techniques involving treatment couch motion synchronous with gantry and multileaf collimator (MLC). Tests were designed to evaluate positional accuracy, velocity constancy and accuracy for dynamic couch motion under a realistic weight load. A test evaluating the geometric accuracy of the system in delivering treatments over complex dynamic trajectories was also devised. Custom XML scripts that control the Varian TrueBeam™ STx (Serial #3) axes in Developer Mode were written to implement the delivery sequences for the tests. Delivered dose patterns were captured with radiographic film or the electronic portal imaging device. The couch translational accuracy in dynamic treatment mode was 0.01 cm. Rotational accuracy was within 0.3°, with 0.04 cm displacement of the rotational axis. Dose intensity profiles capturing the velocity constancy and accuracy for translations and rotation exhibited standard deviation and maximum deviations below 3%. For complex delivery involving MLC and couch motions, the overall translational accuracy for reproducing programmed patterns was within 0.06 cm. The authors conclude that in Developer Mode, TrueBeam™ is capable of delivering dynamic treatment delivery techniques involving couch motion with good geometric and dosimetric fidelity.

    View details for DOI 10.1118/1.4886757

    View details for Web of Science ID 000341068100014

  • A Fourier-based compressed sensing technique for accelerated CT image reconstruction using first-order methods PHYSICS IN MEDICINE AND BIOLOGY Choi, K., Li, R., Nam, H., Xing, L. 2014; 59 (12): 3097-3119

    Abstract

    As a solution to iterative CT image reconstruction, first-order methods are prominent for the large-scale capability and the fast convergence rate [Formula: see text]. In practice, the CT system matrix with a large condition number may lead to slow convergence speed despite the theoretically promising upper bound. The aim of this study is to develop a Fourier-based scaling technique to enhance the convergence speed of first-order methods applied to CT image reconstruction. Instead of working in the projection domain, we transform the projection data and construct a data fidelity model in Fourier space. Inspired by the filtered backprojection formalism, the data are appropriately weighted in Fourier space. We formulate an optimization problem based on weighted least-squares in the Fourier space and total-variation (TV) regularization in image space for parallel-beam, fan-beam and cone-beam CT geometry. To achieve the maximum computational speed, the optimization problem is solved using a fast iterative shrinkage-thresholding algorithm with backtracking line search and GPU implementation of projection/backprojection. The performance of the proposed algorithm is demonstrated through a series of digital simulation and experimental phantom studies. The results are compared with the existing TV regularized techniques based on statistics-based weighted least-squares as well as basic algebraic reconstruction technique. The proposed Fourier-based compressed sensing (CS) method significantly improves both the image quality and the convergence rate compared to the existing CS techniques.

    View details for DOI 10.1088/0031-9155/59/12/3097

    View details for Web of Science ID 000337176600014

    View details for PubMedID 24840019

  • PHD Inhibition Mitigates and Protects Against Radiation-Induced Gastrointestinal Toxicity via HIF2. Science translational medicine Taniguchi, C. M., Miao, Y. R., Diep, A. N., Wu, C., Rankin, E. B., Atwood, T. F., Xing, L., Giaccia, A. J. 2014; 6 (236): 236ra64-?

    Abstract

    Radiation-induced gastrointestinal (GI) toxicity can be a major source of morbidity and mortality after radiation exposure. There is an unmet need for effective preventative or mitigative treatments against the potentially fatal diarrhea and water loss induced by radiation damage to the GI tract. We report that prolyl hydroxylase inhibition by genetic knockout or pharmacologic inhibition of all PHD (prolyl hydroxylase domain) isoforms by the small-molecule dimethyloxallyl glycine (DMOG) increases hypoxia-inducible factor (HIF) expression, improves epithelial integrity, reduces apoptosis, and increases intestinal angiogenesis, all of which are essential for radioprotection. HIF2, but not HIF1, is both necessary and sufficient to prevent radiation-induced GI toxicity and death. Increased vascular endothelial growth factor (VEGF) expression contributes to the protective effects of HIF2, because inhibition of VEGF function reversed the radioprotection and radiomitigation afforded by DMOG. Additionally, mortality from abdominal or total body irradiation was reduced even when DMOG was given 24 hours after exposure. Thus, prolyl hydroxylase inhibition represents a treatment strategy to protect against and mitigate GI toxicity from both therapeutic radiation and potentially lethal radiation exposures.

    View details for DOI 10.1126/scitranslmed.3008523

    View details for PubMedID 24828078

    View details for PubMedCentralID PMC4136475

  • Order of Magnitude Sensitivity Increase in X-ray Fluorescence Computed Tomography (XFCT) Imaging With an Optimized Spectro-Spatial Detector Configuration: Theory and Simulation IEEE TRANSACTIONS ON MEDICAL IMAGING Ahmad, M., Bazalova, M., Xiang, L., Xing, L. 2014; 33 (5): 1119-1128

    Abstract

    The purpose of this study was to increase the sensitivity of XFCT imaging by optimizing the data acquisition geometry for reduced scatter X-rays. The placement of detectors and detector energy window were chosen to minimize scatter X-rays. We performed both theoretical calculations and Monte Carlo simulations of this optimized detector configuration on a mouse-sized phantom containing various gold concentrations. The sensitivity limits were determined for three different X-ray spectra: a monoenergetic source, a Gaussian source, and a conventional X-ray tube source. Scatter X-rays were minimized using a backscatter detector orientation (scatter direction > 110(°) to the primary X-ray beam). The optimized configuration simultaneously reduced the number of detectors and improved the image signal-to-noise ratio. The sensitivity of the optimized configuration was 10 μg/mL (10 pM) at 2 mGy dose with the mono-energetic source, which is an order of magnitude improvement over the unoptimized configuration (102 pM without the optimization). Similar improvements were seen with the Gaussian spectrum source and conventional X-ray tube source. The optimization improvements were predicted in the theoretical model and also demonstrated in simulations. The sensitivity of XFCT imaging can be enhanced by an order of magnitude with the data acquisition optimization, greatly enhancing the potential of this modality for future use in clinical molecular imaging.

    View details for DOI 10.1109/TMI.2014.2305101

    View details for Web of Science ID 000335379500010

    View details for PubMedID 24770916

  • Feasibility evaluation of radioluminescence for oncologic imaging King, M., Carpenter, C., Sun, C., Pratx, G., Xing, L. SOC NUCLEAR MEDICINE INC. 2014
  • A novel radioluminescence microscope for imaging radiotracers at the single-cell level Pratx, G., Natarajan, A., Turkcan, S., Sasportas, L., Axente, M., Gambhir, S., Xing, L. SOC NUCLEAR MEDICINE INC. 2014
  • Synergistic Assembly of Heavy Metal Clusters and Luminescent Organic Bridging Ligands in Metal-Organic Frameworks for Highly Efficient X-ray Scintillation JOURNAL OF THE AMERICAN CHEMICAL SOCIETY Wang, C., Volotskova, O., Lu, K., Ahmad, M., Sun, C., Xing, L., Lin, W. 2014; 136 (17): 6171-6174

    Abstract

    We have designed two metal-organic frameworks (MOFs) to efficiently convert X-ray to visible-light luminescence. The MOFs are constructed from M6(μ3-O)4(μ3-OH)4(carboxylate)12 (M = Hf or Zr) secondary building units (SBUs) and anthracene-based dicarboxylate bridging ligands. The high atomic number of Zr and Hf in the SBUs serves as effective X-ray antenna by absorbing X-ray photons and converting them to fast electrons through the photoelectric effect. The generated electrons then excite multiple anthracene-based emitters in the MOF through inelastic scattering, leading to efficient generation of detectable photons in the visible spectrum. The MOF materials thus serve as efficient X-ray scintillators via synergistic X-ray absorption by the metal-cluster SBUs and optical emission by the bridging ligands.

    View details for DOI 10.1021/ja500671h

    View details for Web of Science ID 000335369200006

    View details for PubMedID 24730683

  • Assessing the dosimetric impact of real-time prostate motion during volumetric modulated arc therapy. International journal of radiation oncology, biology, physics Azcona, J. D., Xing, L., Chen, X., Bush, K., Li, R. 2014; 88 (5): 1167-1174

    Abstract

    To develop a method for dose reconstruction by incorporating the interplay effect between aperture modulation and target motion, and to assess the dosimetric impact of real-time prostate motion during volumetric modulated arc therapy (VMAT).Clinical VMAT plans were delivered with the TrueBeam linac for 8 patients with prostate cancer. The real-time target motion during dose delivery was determined based on the 2-dimensional fiducial localization using an onboard electronic portal imaging device. The target shift in each image was correlated with the control point with the same gantry angle in the VMAT plan. An in-house-developed Monte Carlo simulation tool was used to calculate the 3-dimensional dose distribution for each control point individually, taking into account the corresponding real-time target motion (assuming a nondeformable target with no rotation). The delivered target dose was then estimated by accumulating the dose from all control points in the plan. On the basis of this information, dose-volume histograms and 3-dimensional dose distributions were calculated to assess their degradation from the planned dose caused by target motion. Thirty-two prostate motion trajectories were analyzed.The minimum dose to 0.03 cm(3) of the gross tumor volume (D0.03cc) was only slightly degraded after taking motion into account, with a minimum value of 94.1% of the planned dose among all patients and fractions. However, the gross tumor volume receiving prescription dose (V100%) could be largely affected by motion, dropping below 60% in 1 trajectory. We did not observe a correlation between motion magnitude and dose degradation.Prostate motion degrades the delivered dose to the target in an unpredictable way, although its effect is reduced over multiple fractions, and for most patients the degradation is small. Patients with greater prostate motion or those treated with stereotactic body radiation therapy would benefit from real-time prostate tracking to reduce the margin.

    View details for DOI 10.1016/j.ijrobp.2013.12.015

    View details for PubMedID 24661670

  • Assessing the Dosimetric Impact of Real-Time Prostate Motion During Volumetric Modulated Arc Therapy INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS Diego Azcona, J., Xing, L., Chen, X., Bush, K., Li, R. 2014; 88 (5): 1167-1174

    Abstract

    To develop a method for dose reconstruction by incorporating the interplay effect between aperture modulation and target motion, and to assess the dosimetric impact of real-time prostate motion during volumetric modulated arc therapy (VMAT).Clinical VMAT plans were delivered with the TrueBeam linac for 8 patients with prostate cancer. The real-time target motion during dose delivery was determined based on the 2-dimensional fiducial localization using an onboard electronic portal imaging device. The target shift in each image was correlated with the control point with the same gantry angle in the VMAT plan. An in-house-developed Monte Carlo simulation tool was used to calculate the 3-dimensional dose distribution for each control point individually, taking into account the corresponding real-time target motion (assuming a nondeformable target with no rotation). The delivered target dose was then estimated by accumulating the dose from all control points in the plan. On the basis of this information, dose-volume histograms and 3-dimensional dose distributions were calculated to assess their degradation from the planned dose caused by target motion. Thirty-two prostate motion trajectories were analyzed.The minimum dose to 0.03 cm(3) of the gross tumor volume (D0.03cc) was only slightly degraded after taking motion into account, with a minimum value of 94.1% of the planned dose among all patients and fractions. However, the gross tumor volume receiving prescription dose (V100%) could be largely affected by motion, dropping below 60% in 1 trajectory. We did not observe a correlation between motion magnitude and dose degradation.Prostate motion degrades the delivered dose to the target in an unpredictable way, although its effect is reduced over multiple fractions, and for most patients the degradation is small. Patients with greater prostate motion or those treated with stereotactic body radiation therapy would benefit from real-time prostate tracking to reduce the margin.

    View details for DOI 10.1016/j.ijrobp.2013.12.015

    View details for Web of Science ID 000333460200027

  • Nonisocentric treatment strategy for breast radiation therapy: a proof of concept study. International journal of radiation oncology, biology, physics Li, R., Xing, L., Horst, K. C., Bush, K. 2014; 88 (4): 920-926

    Abstract

    To propose a nonisocentric treatment strategy as a special form of station parameter optimized radiation therapy, to improve sparing of critical structures while preserving target coverage in breast radiation therapy.To minimize the volume of exposed lung and heart in breast irradiation, we propose a novel nonisocentric treatment scheme by strategically placing nonconverging beams with multiple isocenters. As its name suggests, the central axes of these beams do not intersect at a single isocenter as in conventional breast treatment planning. Rather, the isocenter locations and beam directions are carefully selected, in that each beam is only responsible for a certain subvolume of the target, so as to minimize the volume of irradiated normal tissue. When put together, the beams will provide an adequate coverage of the target and expose only a minimal amount of normal tissue to radiation. We apply the nonisocentric planning technique to 2 previously treated clinical cases (breast and chest wall).The proposed nonisocentric technique substantially improved sparing of the ipsilateral lung. Compared with conventional isocentric plans using 2 tangential beams, the mean lung dose was reduced by 38% and 50% using the proposed technique, and the volume of the ipsilateral lung receiving ≥20 Gy was reduced by a factor of approximately 2 and 3 for the breast and chest wall cases, respectively. The improvement in lung sparing is even greater compared with volumetric modulated arc therapy.A nonisocentric implementation of station parameter optimized radiation therapy has been proposed for breast radiation therapy. The new treatment scheme overcomes the limitations of existing approaches and affords a useful tool for conformal breast radiation therapy, especially in cases with extreme chest wall curvature.

    View details for DOI 10.1016/j.ijrobp.2013.12.029

    View details for PubMedID 24606852

  • Nonisocentric treatment strategy for breast radiation therapy: a proof of concept study. International journal of radiation oncology, biology, physics Li, R., Xing, L., Horst, K. C., Bush, K. 2014; 88 (4): 920-926

    Abstract

    To propose a nonisocentric treatment strategy as a special form of station parameter optimized radiation therapy, to improve sparing of critical structures while preserving target coverage in breast radiation therapy.To minimize the volume of exposed lung and heart in breast irradiation, we propose a novel nonisocentric treatment scheme by strategically placing nonconverging beams with multiple isocenters. As its name suggests, the central axes of these beams do not intersect at a single isocenter as in conventional breast treatment planning. Rather, the isocenter locations and beam directions are carefully selected, in that each beam is only responsible for a certain subvolume of the target, so as to minimize the volume of irradiated normal tissue. When put together, the beams will provide an adequate coverage of the target and expose only a minimal amount of normal tissue to radiation. We apply the nonisocentric planning technique to 2 previously treated clinical cases (breast and chest wall).The proposed nonisocentric technique substantially improved sparing of the ipsilateral lung. Compared with conventional isocentric plans using 2 tangential beams, the mean lung dose was reduced by 38% and 50% using the proposed technique, and the volume of the ipsilateral lung receiving ≥20 Gy was reduced by a factor of approximately 2 and 3 for the breast and chest wall cases, respectively. The improvement in lung sparing is even greater compared with volumetric modulated arc therapy.A nonisocentric implementation of station parameter optimized radiation therapy has been proposed for breast radiation therapy. The new treatment scheme overcomes the limitations of existing approaches and affords a useful tool for conformal breast radiation therapy, especially in cases with extreme chest wall curvature.

    View details for DOI 10.1016/j.ijrobp.2013.12.029

    View details for PubMedID 24606852

  • L-shell x-ray fluorescence computed tomography (XFCT) imaging of Cisplatin PHYSICS IN MEDICINE AND BIOLOGY Bazalova, M., Ahmad, M., Pratx, G., Xing, L. 2014; 59 (1): 219-232

    Abstract

    X-ray fluorescence computed tomography (XFCT) imaging has been focused on the detection of K-shell x-rays. The potential utility of L-shell x-ray XFCT is, however, not well studied. Here we report the first Monte Carlo (MC) simulation of preclinical L-shell XFCT imaging of Cisplatin. We built MC models for both L- and K-shell XFCT with different excitation energies (15 and 30 keV for L-shell and 80 keV for K-shell XFCT). Two small-animal sized imaging phantoms of 2 and 4 cm diameter containing a series of objects of 0.6 to 2.7 mm in diameter at 0.7 to 16 mm depths with 10 to 250 µg mL(-1) concentrations of Pt are used in the study. Transmitted and scattered x-rays were collected with photon-integrating transmission detector and photon-counting detector arc, respectively. Collected data were rearranged into XFCT and transmission CT sinograms for image reconstruction. XFCT images were reconstructed with filtered back-projection and with iterative maximum-likelihood expectation maximization without and with attenuation correction. While K-shell XFCT was capable of providing an accurate measurement of Cisplatin concentration, its sensitivity was 4.4 and 3.0 times lower than that of L-shell XFCT with 15 keV excitation beam for the 2 cm and 4 cm diameter phantom, respectively. With the inclusion of excitation and fluorescence beam attenuation correction, we found that L-shell XFCT was capable of providing fairly accurate information of Cisplatin concentration distribution. With a dose of 29 and 58 mGy, clinically relevant Cisplatin Pt concentrations of 10 µg mg(-1) could be imaged with L-shell XFCT inside a 2 cm and 4 cm diameter object, respectively.

    View details for DOI 10.1088/0031-9155/59/1/219

    View details for Web of Science ID 000328549200011

    View details for PubMedID 24334507

    View details for PubMedCentralID PMC3928148

  • Hard X-ray-induced optical luminescence via biomolecule-directed metal clusters CHEMICAL COMMUNICATIONS Osakada, Y., Pratx, G., Sun, C., Sakamoto, M., Ahmad, M., Volotskova, O., Ong, Q., Teranishi, T., Harada, Y., Xing, L., Cui, B. 2014; 50 (27): 3549-3551

    Abstract

    Here, we demonstrate that biomolecule-directed metal clusters are applicable in the study of hard X-ray excited optical luminescence, promising a new direction in the development of novel X-ray-activated imaging probes.

    View details for DOI 10.1039/c3cc48661c

    View details for Web of Science ID 000332483200003

    View details for PubMedID 24463467

  • X-Ray Luminescence and X-Ray Fluorescence Computed Tomography: New Molecular Imaging Modalities IEEE ACCESS Ahmad, M., Pratx, G., Bazalova, M., Xing, L. 2014; 2: 1051-1061
  • Fiber-optic system for dual-modality imaging of glucose probes 18F-FDG and 6-NBDG in atherosclerotic plaques. PloS one Zaman, R. T., Kosuge, H., Pratx, G., Carpenter, C., Xing, L., McConnell, M. V. 2014; 9 (9)

    Abstract

    Atherosclerosis is a progressive inflammatory condition that underlies coronary artery disease (CAD)-the leading cause of death in the United States. Thus, the ultimate goal of this research is to advance our understanding of human CAD by improving the characterization of metabolically active vulnerable plaques within the coronary arteries using a novel catheter-based imaging system. The aims of this study include (1) developing a novel fiber-optic imaging system with a scintillator to detect both 18F and fluorescent glucose probes, and (2) validating the system on ex vivo murine plaques.A novel design implements a flexible fiber-optic catheter consisting of both a radio-luminescence and a fluorescence imaging system to detect radionuclide 18F-fluorodeoxyglucose (18F-FDG) and the fluorescent analog 6-(N-(7-Nitrobenz-2-oxa-1,3-diazol-4-yl)amino)-6-Deoxyglucose (6-NBDG), respectively. Murine macrophage-rich atherosclerotic carotid plaques were imaged ex vivo after intravenous delivery of 18F-FDG or 6-NBDG. Confirmatory optical imaging by IVIS-200 and autoradiography were also performed.Our fiber-optic imaging system successfully visualized both 18F-FDG and 6-NBDG probes in atherosclerotic plaques. For 18F-FDG, the ligated left carotid arteries (LCs) exhibited 4.9-fold higher radioluminescence signal intensity compared to the non-ligated right carotid arteries (RCs) (2.6 × 10(4) ± 1.4 × 10(3) vs. 5.4 × 10(3) ± 1.3 × 10(3) A.U., P = 0.008). Similarly, for 6-NBDG, the ligated LCs emitted 4.3-fold brighter fluorescent signals than the control RCs (1.6 × 10(2) ± 2.7 × 10(1) vs. 3.8 × 10(1) ± 5.9 A.U., P = 0.002). The higher uptake of both 18F-FDG and 6-NBDG in ligated LCs were confirmed with the IVIS-200 system. Autoradiography further verified the higher uptake of 18F-FDG by the LCs.This novel fiber-optic imaging system was sensitive to both radionuclide and fluorescent glucose probes taken up by murine atherosclerotic plaques. In addition, 6-NBDG is a promising novel fluorescent probe for detecting macrophage-rich atherosclerotic plaques.

    View details for DOI 10.1371/journal.pone.0108108

    View details for PubMedID 25233472

    View details for PubMedCentralID PMC4169475

  • Inverse planning in the age of digital LINACs: station parameter optimized radiation therapy (SPORT) 17th International Conference on the Use of Computers in Radiation Therapy (ICCR) Xing, L., Li, R. IOP PUBLISHING LTD. 2014
  • Clinical implementation of intrafraction cone beam computed tomography imaging during lung tumor stereotactic ablative radiation therapy. International journal of radiation oncology, biology, physics Li, R., Han, B., Meng, B., Maxim, P. G., Xing, L., Koong, A. C., Diehn, M., Loo, B. W. 2013; 87 (5): 917-923

    Abstract

    To develop and clinically evaluate a volumetric imaging technique for assessing intrafraction geometric and dosimetric accuracy of stereotactic ablative radiation therapy (SABR).Twenty patients received SABR for lung tumors using volumetric modulated arc therapy (VMAT). At the beginning of each fraction, pretreatment cone beam computed tomography (CBCT) was used to align the soft-tissue tumor position with that in the planning CT. Concurrent with dose delivery, we acquired fluoroscopic radiograph projections during VMAT using the Varian on-board imaging system. Those kilovolt projections acquired during millivolt beam-on were automatically extracted, and intrafraction CBCT images were reconstructed using the filtered backprojection technique. We determined the time-averaged target shift during VMAT by calculating the center of mass of the tumor target in the intrafraction CBCT relative to the planning CT. To estimate the dosimetric impact of the target shift during treatment, we recalculated the dose to the GTV after shifting the entire patient anatomy according to the time-averaged target shift determined earlier.The mean target shift from intrafraction CBCT to planning CT was 1.6, 1.0, and 1.5 mm; the 95th percentile shift was 5.2, 3.1, 3.6 mm; and the maximum shift was 5.7, 3.6, and 4.9 mm along the anterior-posterior, left-right, and superior-inferior directions. Thus, the time-averaged intrafraction gross tumor volume (GTV) position was always within the planning target volume. We observed some degree of target blurring in the intrafraction CBCT, indicating imperfect breath-hold reproducibility or residual motion of the GTV during treatment. By our estimated dose recalculation, the GTV was consistently covered by the prescription dose (PD), that is, V100% above 0.97 for all patients, and minimum dose to GTV >100% PD for 18 patients and >95% PD for all patients.Intrafraction CBCT during VMAT can provide geometric and dosimetric verification of SABR valuable for quality assurance and potentially for treatment adaptation.

    View details for DOI 10.1016/j.ijrobp.2013.08.015

    View details for PubMedID 24113060

  • Clinical implementation of intrafraction cone beam computed tomography imaging during lung tumor stereotactic ablative radiation therapy. International journal of radiation oncology, biology, physics Li, R., Han, B., Meng, B., Maxim, P. G., Xing, L., Koong, A. C., Diehn, M., Loo, B. W. 2013; 87 (5): 917-923

    Abstract

    To develop and clinically evaluate a volumetric imaging technique for assessing intrafraction geometric and dosimetric accuracy of stereotactic ablative radiation therapy (SABR).Twenty patients received SABR for lung tumors using volumetric modulated arc therapy (VMAT). At the beginning of each fraction, pretreatment cone beam computed tomography (CBCT) was used to align the soft-tissue tumor position with that in the planning CT. Concurrent with dose delivery, we acquired fluoroscopic radiograph projections during VMAT using the Varian on-board imaging system. Those kilovolt projections acquired during millivolt beam-on were automatically extracted, and intrafraction CBCT images were reconstructed using the filtered backprojection technique. We determined the time-averaged target shift during VMAT by calculating the center of mass of the tumor target in the intrafraction CBCT relative to the planning CT. To estimate the dosimetric impact of the target shift during treatment, we recalculated the dose to the GTV after shifting the entire patient anatomy according to the time-averaged target shift determined earlier.The mean target shift from intrafraction CBCT to planning CT was 1.6, 1.0, and 1.5 mm; the 95th percentile shift was 5.2, 3.1, 3.6 mm; and the maximum shift was 5.7, 3.6, and 4.9 mm along the anterior-posterior, left-right, and superior-inferior directions. Thus, the time-averaged intrafraction gross tumor volume (GTV) position was always within the planning target volume. We observed some degree of target blurring in the intrafraction CBCT, indicating imperfect breath-hold reproducibility or residual motion of the GTV during treatment. By our estimated dose recalculation, the GTV was consistently covered by the prescription dose (PD), that is, V100% above 0.97 for all patients, and minimum dose to GTV >100% PD for 18 patients and >95% PD for all patients.Intrafraction CBCT during VMAT can provide geometric and dosimetric verification of SABR valuable for quality assurance and potentially for treatment adaptation.

    View details for DOI 10.1016/j.ijrobp.2013.08.015

    View details for PubMedID 24113060

  • Trajectory modulated prone breast irradiation: A LINAC-based technique combining intensity modulated delivery and motion of the couch RADIOTHERAPY AND ONCOLOGY Fahimian, B., Yu, V., Horst, K., Xing, L., Hristov, D. 2013; 109 (3): 475-481

    Abstract

    External beam radiation therapy (EBRT) provides a non-invasive treatment alternative for accelerated partial breast irradiation (APBI), however, limitations in achievable dose conformity of current EBRT techniques have been correlated to reported toxicity. To enhance the conformity of EBRT APBI, a technique for conventional LINACs is developed, which through combined motion of the couch, intensity modulated delivery, and a prone breast setup, enables wide-angular coronal arc irradiation of the ipsilateral breast without irradiating through the thorax and contralateral breast.A couch trajectory optimization technique was developed to determine the trajectories that concurrently avoid collision with the LINAC and maintain the target within the MLC apertures. Inverse treatment planning was performed along the derived trajectory. The technique was experimentally implemented by programming the Varian TrueBeam™ STx in Developer Mode. The dosimetric accuracy of the delivery was evaluated by ion chamber and film measurements in phantom.The resulting optimized trajectory was shown to be necessarily non-isocentric, and contain both translation and rotations of the couch. Film measurements resulted in 93% of the points in the measured two-dimensional dose maps passing the 3%/3mm Gamma criterion. Preliminary treatment plan comparison to 5-field 3D-conformal, IMRT, and VMAT demonstrated enhancement in conformity, and reduction of the normal tissue V50% and V100% parameters that have been correlated with EBRT toxicity.The feasibility of wide-angular intensity modulated partial breast irradiation using motion of the couch has been demonstrated experimentally on a standard LINAC for the first time. For patients eligible for a prone setup, the technique may enable improvement of dose conformity and associated dose-volume parameters correlated with toxicity.

    View details for DOI 10.1016/j.radonc.2013.10.031

    View details for Web of Science ID 000329482000027

    View details for PubMedID 24231240

  • Atherosclerotic Plaque Detection With a Fluorescence/Radionuclide Intravascular Imaging System for 18F-FDG and 6-NBDG Zaman, R., Kosuge, H., Pratx, G., Carpenter, C., Xing, L., McConnell, M. V. LIPPINCOTT WILLIAMS & WILKINS. 2013
  • Cone beam CT imaging with limited angle of projections and prior knowledge for volumetric verification of non-coplanar beam radiation therapy: a proof of concept study. Physics in medicine and biology Meng, B., Xing, L., Han, B., Koong, A., Chang, D., Cheng, J., Li, R. 2013; 58 (21): 7777-7789

    Abstract

    Non-coplanar beams are important for treatment of both cranial and noncranial tumors. Treatment verification of such beams with couch rotation/kicks, however, is challenging, particularly for the application of cone beam CT (CBCT). In this situation, only limited and unconventional imaging angles are feasible to avoid collision between the gantry, couch, patient, and on-board imaging system. The purpose of this work is to develop a CBCT verification strategy for patients undergoing non-coplanar radiation therapy. We propose an image reconstruction scheme that integrates a prior image constrained compressed sensing (PICCS) technique with image registration. Planning CT or CBCT acquired at the neutral position is rotated and translated according to the nominal couch rotation/translation to serve as the initial prior image. Here, the nominal couch movement is chosen to have a rotational error of 5° and translational error of 8 mm from the ground truth in one or more axes or directions. The proposed reconstruction scheme alternates between two major steps. First, an image is reconstructed using the PICCS technique implemented with total-variation minimization and simultaneous algebraic reconstruction. Second, the rotational/translational setup errors are corrected and the prior image is updated by applying rigid image registration between the reconstructed image and the previous prior image. The PICCS algorithm and rigid image registration are alternated iteratively until the registration results fall below a predetermined threshold. The proposed reconstruction algorithm is evaluated with an anthropomorphic digital phantom and physical head phantom. The proposed algorithm provides useful volumetric images for patient setup using projections with an angular range as small as 60°. It reduced the translational setup errors from 8 mm to generally <1 mm and the rotational setup errors from 5° to <1°. Compared with the PICCS algorithm alone, the integration of rigid registration significantly improved the reconstructed image quality, with a reduction of mostly 2-3 folds (up to 100) in root mean square image error. The proposed algorithm provides a remedy for solving the problem of non-coplanar CBCT reconstruction from limited angle of projections by combining the PICCS technique and rigid image registration in an iterative framework. In this proof of concept study, non-coplanar beams with couch rotations of 45° can be effectively verified with the CBCT technique.

    View details for DOI 10.1088/0031-9155/58/21/7777

    View details for PubMedID 24140954

  • An assessment of PTV margin based on actual accumulated dose for prostate cancer radiotherapy PHYSICS IN MEDICINE AND BIOLOGY Wen, N., Kumarasiri, A., Nurushev, T., Burmeister, J., Xing, L., Liu, D., Glide-Hurst, C., Kim, J., Zhong, H., Movsas, B., Chetty, I. J. 2013; 58 (21): 7733–44

    Abstract

    The purpose of this work is to present the results of a margin reduction study involving dosimetric and radiobiologic assessment of cumulative dose distributions, computed using an image guided adaptive radiotherapy based framework. Eight prostate cancer patients, treated with 7-9, 6 MV, intensity modulated radiation therapy (IMRT) fields, were included in this study. The workflow consists of cone beam CT (CBCT) based localization, deformable image registration of the CBCT to simulation CT image datasets (SIM-CT), dose reconstruction and dose accumulation on the SIM-CT, and plan evaluation using radiobiological models. For each patient, three IMRT plans were generated with different margins applied to the CTV. The PTV margin for the original plan was 10 mm and 6 mm at the prostate/anterior rectal wall interface (10/6 mm) and was reduced to: (a) 5/3 mm, and (b) 3 mm uniformly. The average percent reductions in predicted tumor control probability (TCP) in the accumulated (actual) plans in comparison to the original plans over eight patients were 0.4%, 0.7% and 11.0% with 10/6 mm, 5/3 mm and 3 mm uniform margin respectively. The mean increase in predicted normal tissue complication probability (NTCP) for grades 2/3 rectal bleeding for the actual plans in comparison to the static plans with margins of 10/6, 5/3 and 3 mm uniformly was 3.5%, 2.8% and 2.4% respectively. For the actual dose distributions, predicted NTCP for late rectal bleeding was reduced by 3.6% on average when the margin was reduced from 10/6 mm to 5/3 mm, and further reduced by 1.0% on average when the margin was reduced to 3 mm. The average reduction in complication free tumor control probability (P+) in the actual plans in comparison to the original plans with margins of 10/6, 5/3 and 3 mm was 3.7%, 2.4% and 13.6% correspondingly. The significant reduction of TCP and P+ in the actual plan with 3 mm margin came from one outlier, where individualizing patient treatment plans through margin adaptation based on biological models, might yield higher quality treatments.

    View details for DOI 10.1088/0031-9155/58/21/7733

    View details for Web of Science ID 000326377100020

    View details for PubMedID 24140847

    View details for PubMedCentralID PMC4073000

  • High-Resolution Radioluminescence Microscopy of F-18-FDG Uptake by Reconstructing the beta-Ionization Track JOURNAL OF NUCLEAR MEDICINE Pratx, G., Chen, K., Sun, C., Axente, M., Sasportas, L., Carpenter, C., Xing, L. 2013; 54 (10): 1841-1846

    Abstract

    Radioluminescence microscopy is a new method for imaging radionuclide uptake by single live cells with a fluorescence microscope. Here, we report a particle-counting scheme that improves spatial resolution by overcoming the β-range limit.Short frames (10 μs-1 s) were acquired using a high-gain camera coupled to a microscope to capture individual ionization tracks. Optical reconstruction of the β-ionization track (ORBIT) was performed to localize individual β decays, which were aggregated into a composite image. The new approach was evaluated by imaging the uptake of (18)F-FDG in nonconfluent breast cancer cells.After image reconstruction, ORBIT resulted in better definition of individual cells. This effect was particularly noticeable in small clusters (2-4 cells), which occur naturally even for nonconfluent cell cultures. The annihilation and Bremsstrahlung photon background signal was markedly lower. Single-cell measurements of (18)F-FDG uptake that were computed from ORBIT images more closely matched the uptake of the fluorescent glucose analog (Pearson correlation coefficient, 0.54 vs. 0.44, respectively).ORBIT can image the uptake of a radiotracer in living cells with spatial resolution better than the β range. In principle, ORBIT may also allow for greater quantitative accuracy because the decay rate is measured more directly, with no dependency on the β-particle energy.

    View details for DOI 10.2967/jnumed.112.113365

    View details for Web of Science ID 000325341300027

    View details for PubMedID 24003077

  • An introduction to molecular imaging in radiation oncology: A report by the AAPM Working Group on Molecular Imaging in Radiation Oncology (WGMIR) MEDICAL PHYSICS Munley, M. T., Kagadis, G. C., McGee, K. P., Kirov, A. S., Jang, S., Mutic, S., Jeraj, R., Xing, L., Bourland, J. D. 2013; 40 (10)

    Abstract

    Molecular imaging is the direct or indirect noninvasive monitoring and recording of the spatial and temporal distribution of in vivo molecular, genetic, and/or cellular processes for biochemical, biological, diagnostic, or therapeutic applications. Molecular images that indicate the presence of malignancy can be acquired using optical, ultrasonic, radiologic, radionuclide, and magnetic resonance techniques. For the radiation oncology physicist in particular, these methods and their roles in molecular imaging of oncologic processes are reviewed with respect to their physical bases and imaging characteristics, including signal intensity, spatial scale, and spatial resolution. Relevant molecular terminology is defined as an educational assist. Current and future clinical applications in oncologic diagnosis and treatment are discussed. National initiatives for the development of basic science and clinical molecular imaging techniques and expertise are reviewed, illustrating research opportunities in as well as the importance of this growing field.

    View details for DOI 10.1118/1.4819818

    View details for Web of Science ID 000325394400004

    View details for PubMedID 24089890

  • Toward a web-based real-time radiation treatment planning system in a cloud computing environment PHYSICS IN MEDICINE AND BIOLOGY Na, Y. H., Suh, T., Kapp, D. S., Xing, L. 2013; 58 (18): 6525-6540

    Abstract

    To exploit the potential dosimetric advantages of intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), an in-depth approach is required to provide efficient computing methods. This needs to incorporate clinically related organ specific constraints, Monte Carlo (MC) dose calculations, and large-scale plan optimization. This paper describes our first steps toward a web-based real-time radiation treatment planning system in a cloud computing environment (CCE). The Amazon Elastic Compute Cloud (EC2) with a master node (named m2.xlarge containing 17.1 GB of memory, two virtual cores with 3.25 EC2 Compute Units each, 420 GB of instance storage, 64-bit platform) is used as the backbone of cloud computing for dose calculation and plan optimization. The master node is able to scale the workers on an 'on-demand' basis. MC dose calculation is employed to generate accurate beamlet dose kernels by parallel tasks. The intensity modulation optimization uses total-variation regularization (TVR) and generates piecewise constant fluence maps for each initial beam direction in a distributed manner over the CCE. The optimized fluence maps are segmented into deliverable apertures. The shape of each aperture is iteratively rectified to be a sequence of arcs using the manufacture's constraints. The output plan file from the EC2 is sent to the simple storage service. Three de-identified clinical cancer treatment plans have been studied for evaluating the performance of the new planning platform with 6 MV flattening filter free beams (40 × 40 cm(2)) from the Varian TrueBeam(TM) STx linear accelerator. A CCE leads to speed-ups of up to 14-fold for both dose kernel calculations and plan optimizations in the head and neck, lung, and prostate cancer cases considered in this study. The proposed system relies on a CCE that is able to provide an infrastructure for parallel and distributed computing. The resultant plans from the cloud computing are identical to PC-based IMRT and VMAT plans, confirming the reliability of the cloud computing platform. This cloud computing infrastructure has been established for a radiation treatment planning. It substantially improves the speed of inverse planning and makes future on-treatment adaptive re-planning possible.

    View details for DOI 10.1088/0031-9155/58/18/6525

    View details for Web of Science ID 000324573400019

    View details for PubMedID 24002571

  • Automatic prostate tracking and motion assessment in volumetric modulated arc therapy with an electronic portal imaging device. International journal of radiation oncology, biology, physics Azcona, J. D., Li, R., Mok, E., Hancock, S., Xing, L. 2013; 86 (4): 762-768

    Abstract

    PURPOSE: To assess the prostate intrafraction motion in volumetric modulated arc therapy treatments using cine megavoltage (MV) images acquired with an electronic portal imaging device (EPID). METHODS AND MATERIALS: Ten prostate cancer patients were treated with volumetric modulated arc therapy using a Varian TrueBeam linear accelerator equipped with an EPID for acquiring cine MV images during treatment. Cine MV images acquisition was scheduled for single or multiple treatment fractions (between 1 and 8). A novel automatic fiducial detection algorithm that can handle irregular multileaf collimator apertures, field edges, fast leaf and gantry movement, and MV image noise and artifacts in patient anatomy was used. All sets of images (approximately 25,000 images in total) were analyzed to measure the positioning accuracy of implanted fiducial markers and assess the prostate movement. RESULTS: Prostate motion can vary greatly in magnitude among different patients. Different motion patterns were identified, showing its unpredictability. The mean displacement and standard deviation of the intrafraction motion was generally less than 2.0 ± 2.0 mm in each of the spatial directions. In certain patients, however, the percentage of the treatment time in which the prostate is displaced more than 5 mm from its planned position in at least 1 spatial direction was 10% or more. The maximum prostate displacement observed was 13.3 mm. CONCLUSION: Prostate tracking and motion assessment was performed with MV imaging and an EPID. The amount of prostate motion observed suggests that patients will benefit from its real-time monitoring. Megavoltage imaging can provide the basis for real-time prostate tracking using conventional linear accelerators.

    View details for DOI 10.1016/j.ijrobp.2013.03.007

    View details for PubMedID 23608236

  • Sequentially reweighted TV minimization for CT metal artifact reduction MEDICAL PHYSICS Zhang, X., Xing, L. 2013; 40 (7)

    Abstract

    Metal artifact reduction has long been an important topic in x-ray CT image reconstruction. In this work, the authors propose an iterative method that sequentially minimizes a reweighted total variation (TV) of the image and produces substantially artifact-reduced reconstructions.A sequentially reweighted TV minimization algorithm is proposed to fully exploit the sparseness of image gradients (IG). The authors first formulate a constrained optimization model that minimizes a weighted TV of the image, subject to the constraint that the estimated projection data are within a specified tolerance of the available projection measurements, with image non-negativity enforced. The authors then solve a sequence of weighted TV minimization problems where weights used for the next iteration are computed from the current solution. Using the complete projection data, the algorithm first reconstructs an image from which a binary metal image can be extracted. Forward projection of the binary image identifies metal traces in the projection space. The metal-free background image is then reconstructed from the metal-trace-excluded projection data by employing a different set of weights. Each minimization problem is solved using a gradient method that alternates projection-onto-convex-sets and steepest descent. A series of simulation and experimental studies are performed to evaluate the proposed approach.Our study shows that the sequentially reweighted scheme, by altering a single parameter in the weighting function, flexibly controls the sparsity of the IG and reconstructs artifacts-free images in a two-stage process. It successfully produces images with significantly reduced streak artifacts, suppressed noise and well-preserved contrast and edge properties.The sequentially reweighed TV minimization provides a systematic approach for suppressing CT metal artifacts. The technique can also be generalized to other "missing data" problems in CT image reconstruction.

    View details for DOI 10.1118/1.4811129

    View details for Web of Science ID 000321272200044

    View details for PubMedID 23822444

    View details for PubMedCentralID PMC3710254

  • Improving IMRT delivery efficiency with reweighted L1-minimization for inverse planning MEDICAL PHYSICS Kim, H., Becker, S., Lee, R., Lee, S., Shin, S., Candes, E., Xing, L., Li, R. 2013; 40 (7)

    Abstract

    This study presents an improved technique to further simplify the fluence-map in intensity modulated radiation therapy (IMRT) inverse planning, thereby reducing plan complexity and improving delivery efficiency, while maintaining the plan quality.First-order total-variation (TV) minimization (min.) based on L1-norm has been proposed to reduce the complexity of fluence-map in IMRT by generating sparse fluence-map variations. However, with stronger dose sparing to the critical structures, the inevitable increase in the fluence-map complexity can lead to inefficient dose delivery. Theoretically, L0-min. is the ideal solution for the sparse signal recovery problem, yet practically intractable due to its nonconvexity of the objective function. As an alternative, the authors use the iteratively reweighted L1-min. technique to incorporate the benefits of the L0-norm into the tractability of L1-min. The weight multiplied to each element is inversely related to the magnitude of the corresponding element, which is iteratively updated by the reweighting process. The proposed penalizing process combined with TV min. further improves sparsity in the fluence-map variations, hence ultimately enhancing the delivery efficiency. To validate the proposed method, this work compares three treatment plans obtained from quadratic min. (generally used in clinic IMRT), conventional TV min., and our proposed reweighted TV min. techniques, implemented by a large-scale L1-solver (template for first-order conic solver), for five patient clinical data. Criteria such as conformation number (CN), modulation index (MI), and estimated treatment time are employed to assess the relationship between the plan quality and delivery efficiency.The proposed method yields simpler fluence-maps than the quadratic and conventional TV based techniques. To attain a given CN and dose sparing to the critical organs for 5 clinical cases, the proposed method reduces the number of segments by 10-15 and 30-35, relative to TV min. and quadratic min. based plans, while MIs decreases by about 20%-30% and 40%-60% over the plans by two existing techniques, respectively. With such conditions, the total treatment time of the plans obtained from our proposed method can be reduced by 12-30 s and 30-80 s mainly due to greatly shorter multileaf collimator (MLC) traveling time in IMRT step-and-shoot delivery.The reweighted L1-minimization technique provides a promising solution to simplify the fluence-map variations in IMRT inverse planning. It improves the delivery efficiency by reducing the entire segments and treatment time, while maintaining the plan quality in terms of target conformity and critical structure sparing.

    View details for DOI 10.1118/1.4811100

    View details for PubMedID 23822423

  • Tissue feature-based intra-fractional motion tracking for stereoscopic x-ray image guided radiotherapy. Physics in medicine and biology Xie, Y., Xing, L., Gu, J., Liu, W. 2013; 58 (11): 3615-3630

    Abstract

    Real-time knowledge of tumor position during radiation therapy is essential to overcome the adverse effect of intra-fractional organ motion. The goal of this work is to develop a tumor tracking strategy by effectively utilizing the inherent image features of stereoscopic x-ray images acquired during dose delivery. In stereoscopic x-ray image guided radiation delivery, two orthogonal x-ray images are acquired either simultaneously or sequentially. The essence of markerless tumor tracking is the reliable identification of inherent points with distinct tissue features on each projection image and their association between two images. The identification of the feature points on a planar x-ray image is realized by searching for points with high intensity gradient. The feature points are associated by using the scale invariance features transform descriptor. The performance of the proposed technique is evaluated by using images of a motion phantom and four archived clinical cases acquired using either a CyberKnife equipped with a stereoscopic x-ray imaging system, or a LINAC equipped with an onboard kV imager and an electronic portal imaging device. In the phantom study, the results obtained using the proposed method agree with the measurements to within 2 mm in all three directions. In the clinical study, the mean error is 0.48 ± 0.46 mm for four patient data with 144 sequential images. In this work, a tissue feature-based tracking method for stereoscopic x-ray image guided radiation therapy is developed. The technique avoids the invasive procedure of fiducial implantation and may greatly facilitate the clinical workflow.

    View details for DOI 10.1088/0031-9155/58/11/3615

    View details for PubMedID 23648334

  • X-ray excitable luminescent polymer dots doped with an iridium(iii) complex. Chemical communications Osakada, Y., Pratx, G., Hanson, L., Solomon, P. E., Xing, L., Cui, B. 2013; 49 (39): 4319-4321

    Abstract

    In this study, cyclometalated iridium(III) complex-doped polymer dots were synthesized and shown to emit luminescence upon X-ray irradiation, potentially serving as a new probe for molecular imaging during X-ray computed tomography.

    View details for DOI 10.1039/c2cc37169c

    View details for PubMedID 23320256

  • Image-guided resection of malignant gliomas using ?uorescent nanoparticles. Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology Su, X., Cheng, K., Wang, C., Xing, L., Wu, H., Cheng, Z. 2013; 5 (3): 219-232

    Abstract

    Intraoperative fluorescence imaging especially near-infrared fluorescence (NIRF) imaging has the potential to revolutionize neurosurgery by providing high sensitivity and real-time image guidance to surgeons for defining gliomas margins. Fluorescence probes including targeted nanoprobes are expected to improve the specificity and selectivity for intraoperative fluorescence or NIRF tumor imaging. The main focus of this article is to provide a brief overview of intraoperative fluorescence imaging systems and probes including fluorescein sodium, 5-aminolevulinic acid, dye-containing nanoparticles, and targeted NIRF nanoprobes for their applications in image-guided resection of malignant gliomas. Moreover, photoacoustic imaging is a promising molecular imaging modality, and its potential applications for brain tumor imaging are also briefly discussed.

    View details for DOI 10.1002/wnan.1212

    View details for PubMedID 23378052

  • Optimization of normalized prescription isodose selection for stereotactic body radiation therapy: Conventional vs robotic linac MEDICAL PHYSICS Ding, C., Solberg, T. D., Hrycushko, B., Xing, L., Heinzerling, J., Timmerman, R. D. 2013; 40 (5)

    Abstract

    Although modern technology has allowed for target dose escalation by minimizing normal tissue dose, the dose delivered to a tumor and surrounding tissues still depends largely on the inherent characteristics of the radiation delivery platform. This work aims to determine the optimal prescription isodose line that minimizes normal tissue irradiation for stereotactic body radiation therapy (SBRT) for a conventional linear accelerator and a robotic delivery platform.Spherical targets with diameters of 10, 20, and 30 mm were constructed in the lungs and liver of a computer based digital torso phantom which simulates respiratory and cardiac motion. Normal tissue contours included normal lung, normal liver, and a concentric 10 mm shell of normal tissue extending from the spherical target surface. For linac planning, noncoplanar, nonopposing three dimensional (3D) conformal beams were designed, and variable prescription isodose lines were achieved by varying the MLC block margin. For CyberKnife planning, variable prescription isodose lines were achieved by inverse planning. True 4D dose calculations were used for the moving target and surrounding tissue based on each of ten phases of a 4D CT dataset. Doses of 60 Gy in three fractions were prescribed to cover 95% of the target tumor. Commonly used conformality, dosimetric, and radiobiological indices for lung and liver SBRT were used to compare different plans and determine the optimally prescribed isodose line for each treatment platform.For linac plans, the average optimal prescription isodose line based on all indices evaluated occurred between 59% and 69% for lung tumors and between 67% and 77% for liver tumors depending on the tumor size. CyberKnife plans had average optimal prescription isodose lines occurring between 40% and 48% for lung tumors and between 41% and 42% depending on the tumor size. However, prescription isodose lines under 50% are not advised to prevent large heterogeneous dose distributions within the target.The choice of prescription isodose line was shown to have a significant impact on parameters commonly used as constraints for lung and liver SBRT treatment planning for both linac-based and CyberKnife delivery platforms. By methodically choosing the prescription isodose line, normal tissue toxicities from SBRT may be reduced.

    View details for DOI 10.1118/1.4798944

    View details for Web of Science ID 000318553900008

    View details for PubMedID 23635253

  • An adaptive planning strategy for station parameter optimized radiation therapy (SPORT): Segmentally boosted VMAT. Medical physics Li, R., Xing, L. 2013; 40 (5): 050701-?

    Abstract

    Conventional volumetric modulated arc therapy (VMAT) discretizes the angular space into equally spaced control points during planning and then optimizes the apertures and weights of the control points. The aperture at an angle in between two control points is obtained through interpolation. This approach tacitly ignores the differential need for intensity modulation of different angles. As such, multiple arcs are often required, which may oversample some angle(s) and undersample others. The purpose of this work is to develop a segmentally boosted VMAT scheme to eliminate the need for multiple arcs in VMAT treatment with improved dose distribution and∕or delivery efficiency.The essence of the new treatment scheme is how to identify the need of individual angles for intensity modulation and to provide the necessary beam intensity modulation for those beam angles that need it. We introduce a "demand metric" at each control point to decide which station or control points need intensity modulation. To boost the modulation at selected stations, additional segments are added in the vicinity of the selected stations. The added segments are then optimized together with the original set of station or control points as a whole. The authors apply the segmentally boosted planning technique to four previously treated clinical cases: two head and neck (HN) cases, one prostate case, and one liver case. The proposed planning technique is compared with conventional one-arc and two-arc VMAT.The proposed segmentally boosted VMAT technique achieves better critical structure sparing than one-arc VMAT with similar or better target coverage in all four clinical cases. The segmentally boosted VMAT also outperforms two-arc VMAT for the two complicated HN cases, yet with ∼30% reduction in the machine monitor units (MUs) relative to two-arc VMAT, which leads to less leakage∕scatter dose to the patient and can potentially translate into faster dose delivery. For the less challenging prostate and liver cases, similar critical structure sparing as the two-arc VMAT plans was obtained using the segmentally boosted VMAT. The benefit for the two simpler cases is the reduction of MUs and improvement of treatment delivery efficiency.Segmentally boosted VMAT achieves better dose conformality and∕or reduced MUs through effective consideration of the need of individual beam angles for intensity modulation. Elimination of the need for multiple arcs in rotational arc therapy while improving the dose distribution should lead to improved workflow and treatment efficacy, thus may have significant implication to radiation oncology practice.

    View details for DOI 10.1118/1.4802748

    View details for PubMedID 23635247

    View details for PubMedCentralID PMC3656955

  • First study of on-treatment volumetric imaging during respiratory gated VMAT. Medical physics Choi, K., Xing, L., Koong, A., Li, R. 2013; 40 (4): 040701-?

    Abstract

    To obtain on-treatment volumetric patient anatomy during respiratory gated volumetric modulated arc therapy (VMAT).On-board imaging device integrated with Linacs offers a viable tool for obtaining patient anatomy during radiation treatment delivery. In this study, the authors acquired beam-level kV images during gated VMAT treatments using a Varian TrueBeam™STx Linac. These kV projection images are triggered by a respiratory gating signal and can be acquired immediately before treatment MV beam on at every breathing cycle during delivery. Because the kV images are acquired with an on-board imaging device during a rotational arc therapy, they provide the patient anatomical information from many different angles or projection views (typically 20-40). To reconstruct the volumetric image representing patient anatomy during the VMAT treatment, the authors used a compressed sensing method with a fast first-order optimization algorithm. The conventional FDK reconstruction was also used for comparison purposes. The method was tested on a dynamic anthropomorphic physical phantom as well as a lung patient.The reconstructed volumetric images for a dynamic anthropomorphic physical phantom and a lung patient showed clearly visible soft-tissue target as well as other anatomical structures, with the proposed compressed sensing-based image reconstruction method. Compared with FDK, the compressed sensing method leads to a ≈ two and threefold increase in contrast-to-noise ratio around the target area in the phantom and patient case, respectively.The proposed technique provides on-treatment volumetric patient anatomy, with only a fraction (<10%) of the imaging dose used in conventional CBCT procedures. This anatomical information may be valuable for geometric verification and treatment guidance, and useful for verification of treatment dose delivery, accumulation, and adaptation in the future.

    View details for DOI 10.1118/1.4794925

    View details for PubMedID 23556870

    View details for PubMedCentralID PMC3612119

  • Development of a fast and feasible spectrum modeling technique for flattening filter free beams MEDICAL PHYSICS Cho, W., Bush, K., Mok, E., Xing, L., Suh, T. 2013; 40 (4)

    Abstract

    To develop a fast and robust technique for the determination of optimized photon spectra for flattening filter free (FFF) beams to be applied in convolution/superposition dose calculations.A two-step optimization method was developed to derive optimal photon spectra for FFF beams. In the first step, a simple functional form of the photon spectra proposed by Ali ["Functional forms for photon spectra of clinical linacs," Phys. Med. Biol. 57, 31-50 (2011)] is used to determine generalized shapes of the photon spectra. In this method, the photon spectra were defined for the ranges of field sizes to consider the variations of the contributions of scattered photons with field size. Percent depth doses (PDDs) for each field size were measured and calculated to define a cost function, and a collapsed cone convolution (CCC) algorithm was used to calculate the PDDs. In the second step, the generalized functional form of the photon spectra was fine-tuned in a process whereby the weights of photon fluence became the optimizing free parameters. A line search method was used for the optimization and first order derivatives with respect to the optimizing parameters were derived from the CCC algorithm to enhance the speed of the optimization. The derived photon spectra were evaluated, and the dose distributions using the optimized spectra were validated.The optimal spectra demonstrate small variations with field size for the 6 MV FFF beam and relatively large variations for the 10 MV FFF beam. The mean energies of the optimized 6 MV FFF spectra were decreased from 1.31 MeV for a 3 × 3 cm(2) field to 1.21 MeV for a 40 × 40 cm(2) field, and from 2.33 MeV at 3 × 3 cm(2) to 2.18 MeV at 40 × 40 cm(2) for the 10 MV FFF beam. The developed method could significantly improve the agreement between the calculated and measured PDDs. Root mean square differences on the optimized PDDs were observed to be 0.41% (3 × 3 cm(2)) down to 0.21% (40 × 40 cm(2)) for the 6 MV FFF beam, and 0.35% (3 × 3 cm(2)) down to 0.29% (40 × 40 cm(2)) for the 10 MV FFF beam. The first order derivatives from the functional form were found to improve the speed of computational time up to 20 times compared to the other techniques.The derived photon spectra resulted in good agreements with measured PDDs over the range of field sizes investigated. The suggested method is easily applicable to commercial radiation treatment planning systems since it only requires measured PDDs as input.

    View details for DOI 10.1118/1.4797469

    View details for Web of Science ID 000317945900027

    View details for PubMedID 23556891

  • Dosimetric analysis of organs at risk during expiratory gating in stereotactic body radiation therapy for pancreatic cancer. International journal of radiation oncology, biology, physics Taniguchi, C. M., Murphy, J. D., Eclov, N., Atwood, T. F., Kielar, K. N., Christman-Skieller, C., Mok, E., Xing, L., Koong, A. C., Chang, D. T. 2013; 85 (4): 1090-1095

    Abstract

    To determine how the respiratory phase impacts dose to normal organs during stereotactic body radiation therapy (SBRT) for pancreatic cancer.Eighteen consecutive patients with locally advanced, unresectable pancreatic adenocarcinoma treated with SBRT were included in this study. On the treatment planning 4-dimensional computed tomography (CT) scan, the planning target volume (PTV), defined as the gross tumor volume plus 3-mm margin, the duodenum, and the stomach were contoured on the end-expiration (CTexp) and end-inspiration (CTinsp) phases for each patient. A separate treatment plan was constructed for both phases with the dose prescription of 33 Gy in 5 fractions with 95% coverage of the PTV by the 100% isodose line. The dose-volume histogram (DVH) endpoints, volume of duodenum that received 20 Gy (V20), V25, and V30 and maximum dose to 5 cc of contoured organ (D5cc), D1cc, and D0.1cc, were evaluated.Dosimetric parameters for the duodenum, including V25, V30, D1cc, and D0.1cc improved by planning on the CTexp compared to those on the CTinsp. There was a statistically significant overlap of the PTV with the duodenum but not the stomach during the CTinsp compared to the CTexp (0.38 ± 0.17 cc vs 0.01 ± 0.01 cc, P=.048). A larger expansion of the PTV, in accordance with a Danish phase 2 trial, showed even more overlapping volume of duodenum on the CTinsp compared to that on the CTexp (5.5 ± 0.9 cc vs 3.0 ± 0.8 cc, P=.0003) but no statistical difference for any stomach dosimetric DVH parameter.Dose to the duodenum was higher when treating on the inspiratory than on the expiratory phase. These data suggest that expiratory gating may be preferable to inspiratory breath-hold and free breathing strategies for minimizing risk of toxicity.

    View details for DOI 10.1016/j.ijrobp.2012.07.2366

    View details for PubMedID 23273994

  • Dosimetric Analysis of Organs at Risk During Expiratory Gating in Stereotactic Body Radiation Therapy for Pancreatic Cancer INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS Taniguchi, C. M., Murphy, J. D., Eclov, N., Atwood, T. F., Phd, K. N., Christman-Skieller, C., Mok, E., Xing, L., Koong, A. C., Chang, D. T. 2013; 85 (4): 1090-1095

    Abstract

    To determine how the respiratory phase impacts dose to normal organs during stereotactic body radiation therapy (SBRT) for pancreatic cancer.Eighteen consecutive patients with locally advanced, unresectable pancreatic adenocarcinoma treated with SBRT were included in this study. On the treatment planning 4-dimensional computed tomography (CT) scan, the planning target volume (PTV), defined as the gross tumor volume plus 3-mm margin, the duodenum, and the stomach were contoured on the end-expiration (CTexp) and end-inspiration (CTinsp) phases for each patient. A separate treatment plan was constructed for both phases with the dose prescription of 33 Gy in 5 fractions with 95% coverage of the PTV by the 100% isodose line. The dose-volume histogram (DVH) endpoints, volume of duodenum that received 20 Gy (V20), V25, and V30 and maximum dose to 5 cc of contoured organ (D5cc), D1cc, and D0.1cc, were evaluated.Dosimetric parameters for the duodenum, including V25, V30, D1cc, and D0.1cc improved by planning on the CTexp compared to those on the CTinsp. There was a statistically significant overlap of the PTV with the duodenum but not the stomach during the CTinsp compared to the CTexp (0.38 ± 0.17 cc vs 0.01 ± 0.01 cc, P=.048). A larger expansion of the PTV, in accordance with a Danish phase 2 trial, showed even more overlapping volume of duodenum on the CTinsp compared to that on the CTexp (5.5 ± 0.9 cc vs 3.0 ± 0.8 cc, P=.0003) but no statistical difference for any stomach dosimetric DVH parameter.Dose to the duodenum was higher when treating on the inspiratory than on the expiratory phase. These data suggest that expiratory gating may be preferable to inspiratory breath-hold and free breathing strategies for minimizing risk of toxicity.

    View details for DOI 10.1016/j.ijrobp.2012.07.2366

    View details for Web of Science ID 000315809300047

  • Development of XFCT imaging strategy for monitoring the spatial distribution of platinum-based chemodrugs: Instrumentation and phantom validation MEDICAL PHYSICS Kuang, Y., Pratx, G., Bazalova, M., Qian, J., Meng, B., Xing, L. 2013; 40 (3)

    Abstract

    Developing an imaging method to directly monitor the spatial distribution of platinum-based (Pt) drugs at the tumor region is of critical importance for early assessment of treatment efficacy and personalized treatment. In this study, the authors investigated the feasibility of imaging platinum (Pt)-based drug distribution using x-ray fluorescence (XRF, a.k.a. characteristic x ray) CT (XFCT).A 5-mm-diameter pencil beam produced by a polychromatic x-ray source equipped with a tungsten anode was used to stimulate emission of XRF photons from Pt drug embedded within a water phantom. The phantom was translated and rotated relative to the stationary pencil beam in a first-generation CT geometry. The x-ray energy spectrum was collected for 18 s at each position using a cadmium telluride detector. The spectra were then used for the K-shell XRF peak isolation and sinogram generation for Pt. The distribution and concentration of Pt were reconstructed with an iterative maximum likelihood expectation maximization algorithm. The capability of XFCT to multiplexed imaging of Pt, gadolinium (Gd), and iodine (I) within a water phantom was also investigated.Measured XRF spectrum showed a sharp peak characteristic of Pt with a narrow full-width at half-maximum (FWHM) (FWHMKα1 = 1.138 keV, FWHMKα2 = 1.052 keV). The distribution of Pt drug in the water phantom was clearly identifiable on the reconstructed XRF images. Our results showed a linear relationship between the XRF intensity of Pt and its concentrations (R(2) = 0.995), suggesting that XFCT is capable of quantitative imaging. A transmission CT image was also obtained to show the potential of the approach for providing attenuation correction and morphological information. Finally, the distribution of Pt, Gd, and I in the water phantom was clearly identifiable in the reconstructed images from XFCT multiplexed imaging.XFCT is a promising modality for monitoring the spatial distribution of Pt drugs. The technique may be useful in tailoring tumor treatment regimen in the future.

    View details for DOI 10.1118/1.4789917

    View details for Web of Science ID 000316369400003

    View details for PubMedID 23464279

    View details for PubMedCentralID PMC3585826

  • Development and clinical evaluation of automatic fiducial detection for tumor tracking in cine megavoltage images during volumetric modulated arc therapy MEDICAL PHYSICS Azcona, J. D., Li, R., Mok, E., Hancock, S., Xing, L. 2013; 40 (3)

    Abstract

    Real-time tracking of implanted fiducials in cine megavoltage (MV) imaging during volumetric modulated arc therapy (VMAT) delivery is complicated due to the inherent low contrast of MV images and potential blockage of dynamic leaves configurations. The purpose of this work is to develop a clinically practical autodetection algorithm for motion management during VMAT.The expected field-specific segments and the planned fiducial position from the Eclipse (Varian Medical Systems, Palo Alto, CA) treatment planning system were projected onto the MV images. The fiducials were enhanced by applying a Laplacian of Gaussian filter in the spatial domain for each image, with a blob-shaped object as the impulse response. The search of implanted fiducials was then performed on a region of interest centered on the projection of the fiducial when it was within an open field including the case when it was close to the field edge or partially occluded by the leaves. A universal template formula was proposed for template matching and normalized cross correlation was employed for its simplicity and computational efficiency. The search region for every image was adaptively updated through a prediction model that employed the 3D position of the fiducial estimated from the localized positions in previous images. This prediction model allowed the actual fiducial position to be tracked dynamically and was used to initialize the search region. The artifacts caused by electronic interference during the acquisition were effectively removed. A score map was computed by combining both morphological information and image intensity. The pixel location with the highest score was selected as the detected fiducial position. The sets of cine MV images taken during treatment were analyzed with in-house developed software written in MATLAB (The Mathworks, Inc., Natick, MA). Five prostate patients were analyzed to assess the algorithm performance by measuring their positioning accuracy during treatment.The algorithm was able to accurately localize the fiducial position on MV images with success rates of more than 90% per case. The percentage of images in which each fiducial was localized in the studied cases varied between 23% and 65%, with at least one fiducial having been localized between 40% and 95% of the images. This depended mainly on the modulation of the plan and fiducial blockage. The prostate movement in the presented cases varied between 0.8 and 3.5 mm (mean values). The maximum displacement detected among all patients was of 5.7 mm.An algorithm for automatic detection of fiducial markers in cine MV images has been developed and tested with five clinical cases. Despite the challenges posed by complex beam aperture shapes, fiducial localization close to the field edge, partial occlusion of fiducials, fast leaf and gantry movement, and inherently low MV image quality, good localization results were achieved in patient images. This work provides a technique for enabling real-time accurate fiducial detection and tumor tracking during VMAT treatments without the use of extra imaging dose.

    View details for DOI 10.1118/1.4791646

    View details for Web of Science ID 000316369400011

    View details for PubMedID 23464303

    View details for PubMedCentralID PMC3592890

  • Point/counterpoint. DASSIM-RT is likely to become the method of choice over conventional IMRT and VMAT for delivery of highly conformal radiotherapy. Medical physics Xing, L., Phillips, M. H., Orton, C. G. 2013; 40 (2): 020601-?

    View details for DOI 10.1118/1.4773025

    View details for PubMedID 23387721

  • First Demonstration of Multiplexed X-Ray Fluorescence Computed Tomography (XFCT) Imaging IEEE TRANSACTIONS ON MEDICAL IMAGING Kuang, Y., Pratx, G., Bazalova, M., Meng, B., Qian, J., Xing, L. 2013; 32 (2): 262-267

    Abstract

    Simultaneous imaging of multiple probes or biomarkers represents a critical step toward high specificity molecular imaging. In this work, we propose to utilize the element-specific nature of the X-ray fluorescence (XRF) signal for imaging multiple elements simultaneously (multiplexing) using XRF computed tomography (XFCT). A 5-mm-diameter pencil beam produced by a polychromatic X-ray source (150 kV, 20 mA) was used to stimulate emission of XRF photons from 2% (weight/volume) gold (Au), gadolinium (Gd), and barium (Ba) embedded within a water phantom. The phantom was translated and rotated relative to the stationary pencil beam in a first-generation CT geometry. The X-ray energy spectrum was collected for 18 s at each position using a cadmium telluride detector. The spectra were then used to isolate the K shell XRF peak and to generate sinograms for the three elements of interest. The distribution and concentration of the three elements were reconstructed with the iterative maximum likelihood expectation maximization algorithm. The linearity between the XFCT intensity and the concentrations of elements of interest was investigated. We found that measured XRF spectra showed sharp peaks characteristic of Au, Gd, and Ba. The narrow full-width at half-maximum (FWHM) of the peaks strongly supports the potential of XFCT for multiplexed imaging of Au, Gd, and Ba ( FWHM(Au,Kα1) = 0.619 keV, FWHM(Au,Kα2)=1.371 keV , FWHM(Gd,Kα)=1.297 keV, FWHM(Gd,Kβ)=0.974 keV , FWHM(Ba,Kα)=0.852 keV, and FWHM(Ba,Kβ)=0.594 keV ). The distribution of Au, Gd, and Ba in the water phantom was clearly identifiable in the reconstructed XRF images. Our results showed linear relationships between the XRF intensity of each tested element and their concentrations ( R(2)(Au)=0.944 , R(Gd)(2)=0.986, and R(Ba)(2)=0.999), suggesting that XFCT is capable of quantitative imaging. Finally, a transmission CT image was obtained to show the potential of the approach for providing attenuation correction and morphological information. In conclusion, XFCT is a promising modality for multiplexed imaging of high atomic number probes.

    View details for DOI 10.1109/TMI.2012.2223709

    View details for Web of Science ID 000314367100011

    View details for PubMedID 23076031

  • Single-scan patient-specific scatter correction in computed tomography using peripheral detection of scatter and compressed sensing scatter retrieval MEDICAL PHYSICS Meng, B., Lee, H., Xing, L., Fahimian, B. P. 2013; 40 (1)

    Abstract

    X-ray scatter results in a significant degradation of image quality in computed tomography (CT), representing a major limitation in cone-beam CT (CBCT) and large field-of-view diagnostic scanners. In this work, a novel scatter estimation and correction technique is proposed that utilizes peripheral detection of scatter during the patient scan to simultaneously acquire image and patient-specific scatter information in a single scan, and in conjunction with a proposed compressed sensing scatter recovery technique to reconstruct and correct for the patient-specific scatter in the projection space.The method consists of the detection of patient scatter at the edges of the field of view (FOV) followed by measurement based compressed sensing recovery of the scatter through-out the projection space. In the prototype implementation, the kV x-ray source of the Varian TrueBeam OBI system was blocked at the edges of the projection FOV, and the image detector in the corresponding blocked region was used for scatter detection. The design enables image data acquisition of the projection data on the unblocked central region of and scatter data at the blocked boundary regions. For the initial scatter estimation on the central FOV, a prior consisting of a hybrid scatter model that combines the scatter interpolation method and scatter convolution model is estimated using the acquired scatter distribution on boundary region. With the hybrid scatter estimation model, compressed sensing optimization is performed to generate the scatter map by penalizing the L1 norm of the discrete cosine transform of scatter signal. The estimated scatter is subtracted from the projection data by soft-tuning, and the scatter-corrected CBCT volume is obtained by the conventional Feldkamp-Davis-Kress algorithm. Experimental studies using image quality and anthropomorphic phantoms on a Varian TrueBeam system were carried out to evaluate the performance of the proposed scheme.The scatter shading artifacts were markedly suppressed in the reconstructed images using the proposed method. On the Catphan©504 phantom, the proposed method reduced the error of CT number to 13 Hounsfield units, 10% of that without scatter correction, and increased the image contrast by a factor of 2 in high-contrast regions. On the anthropomorphic phantom, the spatial nonuniformity decreased from 10.8% to 6.8% after correction.A novel scatter correction method, enabling unobstructed acquisition of the high frequency image data and concurrent detection of the patient-specific low frequency scatter data at the edges of the FOV, is proposed and validated in this work. Relative to blocker based techniques, rather than obstructing the central portion of the FOV which degrades and limits the image reconstruction, compressed sensing is used to solve for the scatter from detection of scatter at the periphery of the FOV, enabling for the highest quality reconstruction in the central region and robust patient-specific scatter correction.

    View details for DOI 10.1118/1.4769421

    View details for Web of Science ID 000313033200032

    View details for PubMedID 23298098

    View details for PubMedCentralID PMC3543379

  • Enhancement of four-dimensional cone-beam computed tomography by compressed sensing with Bregman iteration JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY Choi, K., Fahimian, B. P., Li, T., Suh, T., Lei, X. 2013; 21 (2): 177-192

    Abstract

    In four-dimensional (4D) cone-beam computed tomography (CBCT), there is a spatio-temporal tradeoff that currently limits the accuracy. The aim of this study is to develop a Bregman iteration based formalism for high quality 4D CBCT image reconstruction from a limited number of low-dose projections. The 4D CBCT problem is first divided into multiple 3D CBCT subproblems by grouping the projection images corresponding to the phases. To maximally utilize the information from the under-sampled projection data, a compressed sensing (CS) method with Bregman iterations is employed for solving each subproblem. We formulate an unconstrained optimization problem based on least-square criterion regularized by total-variation. The least-square criterion reflects the inconsistency between the measured and the estimated line integrals. Furthermore, the unconstrained problem is updated and solved repeatedly by Bregman iterations. The performance of the proposed algorithm is demonstrated through a series of simulation studies and phantom experiments, and the results are compared to those of previously implemented compressed sensing technique using other gradient-based methods as well as conventional filtered back-projection (FBP) results. The simulation and experimental studies have shown that artifact suppressed images can be obtained with as small as 41 projections per phase, which is adequate for clinical 4D CBCT reconstruction. With such small number of projections, the conventional FDK failed to yield meaningful 4D CBCT images, and CS technique using conjugate gradient was not able to recover sharp edges. The proposed method significantly reduces the radiation dose and scanning time to achieve the high quality images compared to the 4D CBCT imaging based on the conventional FDK technique and the existing CS techniques.

    View details for DOI 10.3233/XST-130371

    View details for Web of Science ID 000319344700003

    View details for PubMedID 23694910

  • X-ray induced photoacoustic tomography Conference on Photons Plus Ultrasound - Imaging and Sensing Xiang, L., Han, B., Carpenter, C., Pratx, G., Kuang, Y., Xing, L. SPIE-INT SOC OPTICAL ENGINEERING. 2013

    View details for DOI 10.1117/12.2005765

    View details for Web of Science ID 000322832800032

  • X-ray acoustic computed tomography with pulsed x-ray beam from a medical linear accelerator MEDICAL PHYSICS Xiang, L., Han, B., Carpenter, C., Pratx, G., Kuang, Y., Xing, L. 2013; 40 (1)

    Abstract

    The feasibility of medical imaging using a medical linear accelerator to generate acoustic waves is investigated. This modality, x-ray acoustic computed tomography (XACT), has the potential to enable deeper tissue penetration in tissue than photoacoustic tomography via laser excitation.Short pulsed (μs-range) 10 MV x-ray beams with dose-rate of approximately 30 Gy∕min were generated from a medical linear accelerator. The acoustic signals were collected with an ultrasound transducer (500 KHz central frequency) positioned around an object. The transducer, driven by a computer-controlled step motor to scan around the object, detected the resulting acoustic signals in the imaging plane at each scanning position. A pulse preamplifier, with a bandwidth of 20 KHz-2 MHz at -3 dB, and switchable gains of 40 and 60 dB, received the signals from the transducer and delivered the amplified signals to a secondary amplifier. The secondary amplifier had bandwidth of 20 KHz-30 MHz at -3 dB, and a gain range of 10-60 dB. Signals were recorded and averaged 128 times by an oscilloscope. A sampling rate of 100 MHz was used to record 2500 data points at each view angle. One set of data incorporated 200 positions as the receiver moved 360°. The x-ray generated acoustic image was then reconstructed with the filtered back projection algorithm.The x-ray generated acoustic signals were detected from a lead rod embedded in a chicken breast tissue. The authors found that the acoustic signal was proportional to the x-ray dose deposition, with a correlation of 0.998. The two-dimensional XACT images of the lead rod embedded in chicken breast tissue were found to be in good agreement with the shape of the object.The first x-ray acoustic computed tomography image is presented. The new modality may be useful for a number of applications, such as providing the location of a fiducial, or monitoring x-ray dose distribution during radiation therapy. Although much work is needed to improve the image quality of XACT and to explore its performance in other irradiation energies, the benefits of this modality, as highlighted in this work, encourage further study.

    View details for DOI 10.1118/1.4771935

    View details for Web of Science ID 000313033200003

    View details for PubMedID 23298069

    View details for PubMedCentralID PMC3537718

  • Practical Implementation of a Collapsed Cone Convolution Algorithm for a Radiation Treatment Planning System JOURNAL OF THE KOREAN PHYSICAL SOCIETY Cho, W., Suh, T., Park, J., Xing, L., Lee, J. 2012; 61 (12): 2073-2083
  • Single Scan Scatter Correction in Cone Beam CT Using a Stationary Boundary Blocker and Compressed Sensing-based Scatter Estimation 54th Annual Meeting of the American-Society-for-Radiation-Oncology (ASTRO) Meng, B., LEE, H., Xing, L., Fahimian, B. P. ELSEVIER SCIENCE INC. 2012: S82–S83
  • Prone Partial Breast Coronal Arc Irradiation: Combining Intensity Modulated Delivery With Dynamic Motion of the Couch 54th Annual Meeting of the American-Society-for-Radiation-Oncology (ASTRO) Fahimian, B. P., Yu, V., Xing, L., Horst, K., Hristov, D. ELSEVIER SCIENCE INC. 2012: S214–S214
  • Development of XFCT Imaging Strategy for Monitoring the Spatial Distribution of Platinum Drugs: Instrumentation and Phantom Validation 54th Annual Meeting of the American-Society-for-Radiation-Oncology (ASTRO) Kuang, Y., Pratx, G., Qian, J., Meng, B., Bazalova, M., Xing, L. ELSEVIER SCIENCE INC. 2012: S132–S133
  • Prospectively Gated CBCT for Volumetric Image Guidance in SBRT 54th Annual Meeting of the American-Society-for-Radiation-Oncology (ASTRO) Fahimian, B. P., Xing, L. ELSEVIER SCIENCE INC. 2012: S199–S200
  • 4D cone beam CT via spatiotemporal tensor framelet MEDICAL PHYSICS Gao, H., Li, R., Lin, Y., Xing, L. 2012; 39 (11): 6943-6946

    Abstract

    On-board 4D cone beam CT (4DCBCT) offers respiratory phase-resolved volumetric imaging, and improves the accuracy of target localization in image guided radiation therapy. However, the clinical utility of this technique has been greatly impeded by its degraded image quality, prolonged imaging time, and increased imaging dose. The purpose of this letter is to develop a novel iterative 4DCBCT reconstruction method for improved image quality, increased imaging speed, and reduced imaging dose.The essence of this work is to introduce the spatiotemporal tensor framelet (STF), a high-dimensional tensor generalization of the 1D framelet for 4DCBCT, to effectively take into account of highly correlated and redundant features of the patient anatomy during respiration, in a multilevel fashion with multibasis sparsifying transform. The STF-based algorithm is implemented on a GPU platform for improved computational efficiency. To evaluate the method, 4DCBCT full-fan scans were acquired within 30 s, with a gantry rotation of 200°; STF is also compared with a state-of-art reconstruction method via spatiotemporal total variation regularization.Both the simulation and experimental results demonstrate that STF-based reconstruction achieved superior image quality. The reconstruction of 20 respiratory phases took less than 10 min on an NVIDIA Tesla C2070 GPU card. The STF codes are available at https://sites.google.com/site/spatiotemporaltensorframelet.By effectively utilizing the spatiotemporal coherence of the patient anatomy among different respiratory phases in a multilevel fashion with multibasis sparsifying transform, the proposed STF method potentially enables fast and low-dose 4DCBCT with improved image quality.

    View details for DOI 10.1118/1.4762288

    View details for Web of Science ID 000310726300042

    View details for PubMedID 23127087

    View details for PubMedCentralID PMC3494730

  • Binary Moving Blocker-based Scatter Correction for Single Scan Cone Beam CT System With Off-Centered Detector 54th Annual Meeting of the American-Society-for-Radiation-Oncology (ASTRO) LEE, H., Fahimian, B. P., Xing, L. ELSEVIER SCIENCE INC. 2012: S797–S797
  • Effect of Li-deficiency impurities on the electron-overdoped LiFeAs superconductor PHYSICAL REVIEW B Wang, M., Wang, M., Miao, H., CARR, S. V., Abernathy, D. L., Stone, M. B., Wang, X. C., Xing, L., Jin, C. Q., Zhang, X., Hu, J., Xiang, T., Ding, H., Dai, P. 2012; 86 (14)
  • Radioluminescence Microscopy: Measuring the Heterogeneous Uptake of Radiotracers in Single Living Cells PLOS ONE Pratx, G., Chen, K., Sun, C., Martin, L., Carpenter, C. M., Olcott, P. D., Xing, L. 2012; 7 (10)

    Abstract

    Radiotracers play an important role in interrogating molecular processes both in vitro and in vivo. However, current methods are limited to measuring average radiotracer uptake in large cell populations and, as a result, lack the ability to quantify cell-to-cell variations. Here we apply a new technique, termed radioluminescence microscopy, to visualize radiotracer uptake in single living cells, in a standard fluorescence microscopy environment. In this technique, live cells are cultured sparsely on a thin scintillator plate and incubated with a radiotracer. Light produced following beta decay is measured using a highly sensitive microscope. Radioluminescence microscopy revealed strong heterogeneity in the uptake of [(18)F]fluoro-deoxyglucose (FDG) in single cells, which was found consistent with fluorescence imaging of a glucose analog. We also verified that dynamic uptake of FDG in single cells followed the standard two-tissue compartmental model. Last, we transfected cells with a fusion PET/fluorescence reporter gene and found that uptake of FHBG (a PET radiotracer for transgene expression) coincided with expression of the fluorescent protein. Together, these results indicate that radioluminescence microscopy can visualize radiotracer uptake with single-cell resolution, which may find a use in the precise characterization of radiotracers.

    View details for DOI 10.1371/journal.pone.0046285

    View details for Web of Science ID 000309454000029

    View details for PubMedID 23056276

    View details for PubMedCentralID PMC3463617

  • Intraoperative Imaging of Tumors Using Cerenkov Luminescence Endoscopy: A Feasibility Experimental Study JOURNAL OF NUCLEAR MEDICINE Liu, H., Carpenter, C. M., Jiang, H., Pratx, G., Sun, C., Buchin, M. P., Gambhir, S. S., Xing, L., Cheng, Z. 2012; 53 (10): 1579-1584

    Abstract

    Cerenkov luminescence imaging (CLI) is an emerging new molecular imaging modality that is relatively inexpensive, easy to use, and has high throughput. CLI can image clinically available PET and SPECT probes using optical instrumentation. Cerenkov luminescence endoscopy (CLE) is one of the most intriguing applications that promise potential clinical translation. We developed a prototype customized fiberscopic Cerenkov imaging system to investigate the potential in guiding minimally invasive surgical resection.All experiments were performed in a dark chamber. Cerenkov luminescence from (18)F-FDG samples containing decaying radioactivity was transmitted through an optical fiber bundle and imaged by an intensified charge-coupled device camera. Phantoms filled with (18)F-FDG were used to assess the imaging spatial resolution. Finally, mice bearing subcutaneous C6 glioma cells were injected intravenously with (18)F-FDG to determine the feasibility of in vivo imaging. The tumor tissues were exposed, and CLI was performed on the mouse before and after surgical removal of the tumor using the fiber-based imaging system and compared with a commercial optical imaging system.The sensitivity of this particular setup was approximately 45 kBq (1.21 μCi)/300 μL. The 3 smallest sets of cylindric holes in a commercial SPECT phantom were identifiable via this system, demonstrating that the system has a resolution better than 1.2 mm. Finally, the in vivo tumor imaging study demonstrated the feasibility of using CLI to guide the resection of tumor tissues.This proof-of-concept study explored the feasibility of using fiber-based CLE for the detection of tumor tissue in vivo for guided surgery. With further improvements of the imaging sensitivity and spatial resolution of the current system, CLE may have a significant application in the clinical setting in the near future.

    View details for DOI 10.2967/jnumed.111.098541

    View details for Web of Science ID 000309432400017

    View details for PubMedID 22904353

  • Significance of intraplaque neovascularisation for vulnerability: optical coherence tomography study HEART Tian, J., Hou, J., Xing, L., Kim, S., Yonetsu, T., Kato, K., Lee, H., Zhang, S., Yu, B., Jang, I. 2012; 98 (20): 1504-1509

    Abstract

    This study aimed to investigate the role of intraplaque neovascularisation (NV) in culprit lesions and non-culprit lesions of unstable angina pectoris (UAP) and in lesions of stable angina pectoris (SAP) using optical coherence tomography (OCT).This study was a retrospective study.The significance of NV for culprit and non-culprit plaques remains unclear.A total of 356 plaques from 92 UAP patients and 25 SAP patients who underwent OCT imaging were divided into three groups: culprit lesions in UAP (92), non-culprit lesions in UAP (203) and lesions of SAP (61).NV and plaque characteristics were examined by OCT and plaques with and without NV were compared.Among UAP culprit lesions, plaques with NV had significantly higher incidence of thin cap fibroatheroma (81% vs 47%, p=0.002) compared with those without NV. In addition, the fibrous cap was thinner (56±20 μm vs 75±30 μm, p<0.001), lipid arc was greater (254±66° vs 222±65°, p=0.024) and lipid core length was longer (13±5 mm vs 10±6 mm, p=0.007). No significant difference was observed between non-culprit lesions of UAP with and without NV, and between lesions of SAP with and without NV.In patients with UAP, the culprit plaques with NV had vulnerable features such as thinner fibrous cap, greater lipid arc, longer lipid core length and more frequent thin cap fibroatheroma compared with those without NV. In both non-culprit lesions of UAP patients and in lesions of SAP patients NV was not associated with vulnerable plaque characteristics.

    View details for DOI 10.1136/heartjnl-2012-302445

    View details for Web of Science ID 000309116600008

    View details for PubMedID 22869676

  • Evaluation of the deformation and corresponding dosimetric implications in prostate cancer treatment PHYSICS IN MEDICINE AND BIOLOGY Wen, N., Glide-Hurst, C., Nurushev, T., Xing, L., Kim, J., Zhong, H., Liu, D., Liu, M., Burmeister, J., Movsas, B., Chetty, I. J. 2012; 57 (17): 5361-5379

    Abstract

    The cone-beam computed tomography (CBCT) imaging modality is an integral component of image-guided adaptive radiation therapy (IGART), which uses patient-specific dynamic/temporal information for potential treatment plan modification. In this study, an offline process for the integral component IGART framework has been implemented that consists of deformable image registration (DIR) and its validation, dose reconstruction, dose accumulation and dose verification. This study compares the differences between planned and estimated delivered doses under an IGART framework of five patients undergoing prostate cancer radiation therapy. The dose calculation accuracy on CBCT was verified by measurements made in a Rando pelvic phantom. The accuracy of DIR on patient image sets was evaluated in three ways: landmark matching with fiducial markers, visual image evaluation and unbalanced energy (UE); UE has been previously demonstrated to be a feasible method for the validation of DIR accuracy at a voxel level. The dose calculated on each CBCT image set was reconstructed and accumulated over all fractions to reflect the 'actual dose' delivered to the patient. The deformably accumulated (delivered) plans were then compared to the original (static) plans to evaluate tumor and normal tissue dose discrepancies. The results support the utility of adaptive planning, which can be used to fully elucidate the dosimetric impact based on the simulated delivered dose to achieve the desired tumor control and normal tissue sparing, which may be of particular importance in the context of hypofractionated radiotherapy regimens.

    View details for DOI 10.1088/0031-9155/57/17/5361

    View details for Web of Science ID 000307876600002

    View details for PubMedID 22863976

    View details for PubMedCentralID PMC3652266

  • Predictors for Neoatherosclerosis A Retrospective Observational Study From the Optical Coherence Tomography Registry CIRCULATION-CARDIOVASCULAR IMAGING Yonetsu, T., Kato, K., Kim, S., Xing, L., Jia, H., McNulty, I., Lee, H., Zhang, S., Uemura, S., Jang, Y., Kang, S., Park, S., Lee, S., Yu, B., kakuta, T., Jang, I. 2012; 5 (5): 660-666

    Abstract

    Recent studies have reported development of neoatherosclerosis (NA) inside the stents several years after stent implantation. The aim of this study was to determine the predictors for NA using optical coherence tomography.From a total of 1080 patients who underwent optical coherence tomography, we identified 179 stents in 151 patients in which the mean neointimal thickness was >100 µm. The presence of lipid-laden neointima or calcification inside the stents was defined as NA in the present study. Patient characteristics, stent type, and time since stent implantation (stent age) were compared between stents with or without NA. Univariable and multivariable logistic regression analyses were used to assess the independent predictors. In univariate analysis, stent age ≥48 months (Odds ratio [OR], 4.48; [95% CI 2.68-9.65]; P<0.001), drug-eluting stents (OR, 2.66; [95% CI, 1.38-5.16]; P=0.004), age ≥65 years (OR, 1.91; [95% CI, 1.05-3.44]; P=0.032), current smoking (OR, 2.30; [95% CI, 1.10-4.82]; P=0.024), chronic kidney disease (OR, 4.17; [95% CI, 1.42-12.23]; P=0.009), and angiotensin-converting enzyme inhibitors or angiotensin II receptor blockade use (OR, 0.42; [95% CI, 0.22-0.80]; P=0.008) were significant predictors. In multivariate analysis, stent age ≥48 months, all subtypes of drug-eluting stent, current smoking, chronic kidney disease, and angiotensin-converting enzyme inhibitors/angiotensin II receptor blockade use remained independent predictors for NA.In addition to the stent type and the stent age, patient characteristics, including current smoking, chronic kidney disease, and angiotensin-converting enzyme inhibitors/angiotensin II receptor blockade, were associated with the presence of NA. This result may support the importance of secondary prevention after stent implantation.

    View details for DOI 10.1161/CIRCIMAGING.112.976167

    View details for Web of Science ID 000313574200018

    View details for PubMedID 22798521

  • Volumetric modulated arc therapy planning method for supine craniospinal irradiation JOURNAL OF RADIATION ONCOLOGY Chen, J., Chen, C., Atwood, T. F., Gibbs, I. C., Soltys, S. G., Fasola, C., Xing, L. 2012; 1 (3): 291–97
  • Tracking the motion trajectories of junction structures in 4D CT images of the lung PHYSICS IN MEDICINE AND BIOLOGY Xiong, G., Chen, C., Chen, J., Xie, Y., Xing, L. 2012; 57 (15): 4905-4930

    Abstract

    Respiratory motion poses a major challenge in lung radiotherapy. Based on 4D CT images, a variety of intensity-based deformable registration techniques have been proposed to study the pulmonary motion. However, the accuracy achievable with these approaches can be sub-optimal because the deformation is defined globally in space. Therefore, the accuracy of the alignment of local structures may be compromised. In this work, we propose a novel method to detect a large collection of natural junction structures in the lung and use them as the reliable markers to track the lung motion. Specifically, detection of the junction centers and sizes is achieved by analysis of local shape profiles on one segmented image. To track the temporal trajectory of a junction, the image intensities within a small region of interest surrounding the center are selected as its signature. Under the assumption of the cyclic motion, we describe the trajectory by a closed B-spline curve and search for the control points by maximizing a metric of combined correlation coefficients. Local extrema are suppressed by improving the initial conditions using random walks from pair-wise optimizations. Several descriptors are introduced to analyze the motion trajectories. Our method was applied to 13 real 4D CT images. More than 700 junctions in each case are detected with an average positive predictive value of greater than 90%. The average tracking error between automated and manual tracking is sub-voxel and smaller than the published results using the same set of data.

    View details for DOI 10.1088/0031-9155/57/15/4905

    View details for Web of Science ID 000306521900015

    View details for PubMedID 22796656

  • Real-time tumor motion estimation using respiratory surrogate via memory-based learning PHYSICS IN MEDICINE AND BIOLOGY Li, R., Lewis, J. H., Berbeco, R. I., Xing, L. 2012; 57 (15): 4771-4786

    Abstract

    Respiratory tumor motion is a major challenge in radiation therapy for thoracic and abdominal cancers. Effective motion management requires an accurate knowledge of the real-time tumor motion. External respiration monitoring devices (optical, etc) provide a noninvasive, non-ionizing, low-cost and practical approach to obtain the respiratory signal. Due to the highly complex and nonlinear relations between tumor and surrogate motion, its ultimate success hinges on the ability to accurately infer the tumor motion from respiratory surrogates. Given their widespread use in the clinic, such a method is critically needed. We propose to use a powerful memory-based learning method to find the complex relations between tumor motion and respiratory surrogates. The method first stores the training data in memory and then finds relevant data to answer a particular query. Nearby data points are assigned high relevance (or weights) and conversely distant data are assigned low relevance. By fitting relatively simple models to local patches instead of fitting one single global model, it is able to capture highly nonlinear and complex relations between the internal tumor motion and external surrogates accurately. Due to the local nature of weighting functions, the method is inherently robust to outliers in the training data. Moreover, both training and adapting to new data are performed almost instantaneously with memory-based learning, making it suitable for dynamically following variable internal/external relations. We evaluated the method using respiratory motion data from 11 patients. The data set consists of simultaneous measurement of 3D tumor motion and 1D abdominal surface (used as the surrogate signal in this study). There are a total of 171 respiratory traces, with an average peak-to-peak amplitude of ∼15 mm and average duration of ∼115 s per trace. Given only 5 s (roughly one breath) pretreatment training data, the method achieved an average 3D error of 1.5 mm and 95th percentile error of 3.4 mm on unseen test data. The average 3D error was further reduced to 1.4 mm when the model was tuned to its optimal setting for each respiratory trace. In one trace where a few outliers are present in the training data, the proposed method achieved an error reduction of as much as ∼50% compared with the best linear model (1.0 mm versus 2.1 mm). The memory-based learning technique is able to accurately capture the highly complex and nonlinear relations between tumor and surrogate motion in an efficient manner (a few milliseconds per estimate). Furthermore, the algorithm is particularly suitable to handle situations where the training data are contaminated by large errors or outliers. These desirable properties make it an ideal candidate for accurate and robust tumor gating/tracking using respiratory surrogates.

    View details for DOI 10.1088/0031-9155/57/15/4771

    View details for Web of Science ID 000306521900007

    View details for PubMedID 22772042

    View details for PubMedCentralID PMC3658941

  • Intrafraction Verification of Gated RapidArc by Using Beam-Level Kilovoltage X-Ray Images INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS Li, R., Mok, E., Chang, D. T., Daly, M., Loo, B. W., Diehn, M., Quynh-Thu Le, Q. T., Koong, A., Xing, L. 2012; 83 (5): E709-E715

    Abstract

    To verify the geometric accuracy of gated RapidArc treatment using kV images acquired during dose delivery.Twenty patients were treated using the gated RapidArc technique with a Varian TrueBeam STx linear accelerator. One to 7 metallic fiducial markers were implanted inside or near the tumor target before treatment simulation. For patient setup and treatment verification purposes, the internal target volume (ITV) was created, corresponding to each implanted marker. The gating signal was generated from the Real-time Position Management (RPM) system. At the beginning of each fraction, individualized respiratory gating amplitude thresholds were set based on fluoroscopic image guidance. During the treatment, we acquired kV images immediately before MV beam-on at every breathing cycle, using the on-board imaging system. After the treatment, all implanted markers were detected, and their 3-dimensional (3D) positions in the patient were estimated using software developed in-house. The distance from the marker to the corresponding ITV was calculated for each patient by averaging over all markers and all fractions.The average 3D distance between the markers and their ITVs was 0.8 ± 0.5 mm (range, 0-1.7 mm) and was 2.1 ± 1.2 mm at the 95th percentile (range, 0-3.8 mm). On average, a left-right margin of 0.6 mm, an anterior-posterior margin of 0.8 mm, and a superior-inferior margin of 1.5 mm is required to account for 95% of the intrafraction uncertainty in RPM-based RapidArc gating.To our knowledge, this is the first clinical report of intrafraction verification of respiration-gated RapidArc treatment in stereotactic ablative radiation therapy. For some patients, the markers deviated significantly from the ITV by more than 2 mm at the beginning of the MV beam-on. This emphasizes the need for gating techniques with beam-on/-off controlled directly by the actual position of the tumor target instead of external surrogates such as RPM.

    View details for DOI 10.1016/j.ijrobp.2012.03.006

    View details for PubMedID 22554582

  • Investigation of X-ray Fluorescence Computed Tomography (XFCT) and K-Edge Imaging IEEE TRANSACTIONS ON MEDICAL IMAGING Bazalova, M., Kuang, Y., Pratx, G., Xing, L. 2012; 31 (8): 1620-1627

    Abstract

    This work provides a comprehensive Monte Carlo study of X-ray fluorescence computed tomography (XFCT) and K-edge imaging system, including the system design, the influence of various imaging components, the sensitivity and resolution under various conditions. We modified the widely used EGSnrc/DOSXYZnrc code to simulate XFCT images of two acrylic phantoms loaded with various concentrations of gold nanoparticles and Cisplatin for a number of XFCT geometries. In particular, reconstructed signal as a function of the width of the detector ring, its angular coverage and energy resolution were studied. We found that XFCT imaging sensitivity of the modeled systems consisting of a conventional X-ray tube and a full 2-cm-wide energy-resolving detector ring was 0.061% and 0.042% for gold nanoparticles and Cisplatin, respectively, for a dose of ∼ 10 cGy. Contrast-to-noise ratio (CNR) of XFCT images of the simulated acrylic phantoms was higher than that of transmission K-edge images for contrast concentrations below 0.4%.

    View details for DOI 10.1109/TMI.2012.2201165

    View details for Web of Science ID 000307120600010

    View details for PubMedID 22692896

  • Efficient IMRT inverse planning with a new L1-solver: template for first-order conic solver PHYSICS IN MEDICINE AND BIOLOGY Kim, H., Suh, T., Lee, R., Xing, L., Li, R. 2012; 57 (13): 4139-4153

    Abstract

    Intensity modulated radiation therapy (IMRT) inverse planning using total-variation (TV) regularization has been proposed to reduce the complexity of fluence maps and facilitate dose delivery. Conventionally, the optimization problem with L-1 norm is solved with quadratic programming (QP), which is time consuming and memory expensive due to the second-order Newton update. This study proposes to use a new algorithm, template for first-order conic solver (TFOCS), for fast and memory-efficient optimization in IMRT inverse planning. The TFOCS utilizes dual-variable updates and first-order approaches for TV minimization without the need to compute and store the enlarged Hessian matrix required for Newton update in the QP technique. To evaluate the effectiveness and efficiency of the proposed method, two clinical cases were used for IMRT inverse planning: a head and neck case and a prostate case. For comparison, the conventional QP-based method for the TV form was adopted to solve the fluence map optimization problem in the above two cases. The convergence criteria and algorithm parameters were selected to achieve similar dose conformity for a fair comparison between the two methods. Compared with conventional QP-based approach, the proposed TFOCS-based method shows a remarkable improvement in computational efficiency for fluence map optimization, while maintaining the conformal dose distribution. Compared with QP-based algorithms, the computational speed using TFOCS for fluence optimization is increased by a factor of 4 to 6, and at the same time the memory requirement is reduced by a factor of 3 to 4. Therefore, TFOCS provides an effective, fast and memory-efficient method for IMRT inverse planning. The unique features of the approach should be particularly important in inverse planning involving a large number of beams, such as in VMAT and dense angularly sampled and sparse intensity modulated radiation therapy (DASSIM-RT).

    View details for DOI 10.1088/0031-9155/57/13/4139

    View details for Web of Science ID 000305803600006

    View details for PubMedID 22683930

  • Dose optimization with first-order total-variation minimization for dense angularly sampled and sparse intensity modulated radiation therapy (DASSIM-RT) MEDICAL PHYSICS Kim, H., Li, R., Lee, R., Goldstein, T., Boyd, S., Candes, E., Xing, L. 2012; 39 (7): 4316-4327

    Abstract

    A new treatment scheme coined as dense angularly sampled and sparse intensity modulated radiation therapy (DASSIM-RT) has recently been proposed to bridge the gap between IMRT and VMAT. By increasing the angular sampling of radiation beams while eliminating dispensable segments of the incident fields, DASSIM-RT is capable of providing improved conformity in dose distributions while maintaining high delivery efficiency. The fact that DASSIM-RT utilizes a large number of incident beams represents a major computational challenge for the clinical applications of this powerful treatment scheme. The purpose of this work is to provide a practical solution to the DASSIM-RT inverse planning problem.The inverse planning problem is formulated as a fluence-map optimization problem with total-variation (TV) minimization. A newly released L1-solver, template for first-order conic solver (TFOCS), was adopted in this work. TFOCS achieves faster convergence with less memory usage as compared with conventional quadratic programming (QP) for the TV form through the effective use of conic forms, dual-variable updates, and optimal first-order approaches. As such, it is tailored to specifically address the computational challenges of large-scale optimization in DASSIM-RT inverse planning. Two clinical cases (a prostate and a head and neck case) are used to evaluate the effectiveness and efficiency of the proposed planning technique. DASSIM-RT plans with 15 and 30 beams are compared with conventional IMRT plans with 7 beams in terms of plan quality and delivery efficiency, which are quantified by conformation number (CN), the total number of segments and modulation index, respectively. For optimization efficiency, the QP-based approach was compared with the proposed algorithm for the DASSIM-RT plans with 15 beams for both cases.Plan quality improves with an increasing number of incident beams, while the total number of segments is maintained to be about the same in both cases. For the prostate patient, the conformation number to the target was 0.7509, 0.7565, and 0.7611 with 80 segments for IMRT with 7 beams, and DASSIM-RT with 15 and 30 beams, respectively. For the head and neck (HN) patient with a complicated target shape, conformation numbers of the three treatment plans were 0.7554, 0.7758, and 0.7819 with 75 segments for all beam configurations. With respect to the dose sparing to the critical structures, the organs such as the femoral heads in the prostate case and the brainstem and spinal cord in the HN case were better protected with DASSIM-RT. For both cases, the delivery efficiency has been greatly improved as the beam angular sampling increases with the similar or better conformal dose distribution. Compared with conventional quadratic programming approaches, first-order TFOCS-based optimization achieves far faster convergence and smaller memory requirements in DASSIM-RT.The new optimization algorithm TFOCS provides a practical and timely solution to the DASSIM-RT or other inverse planning problem requiring large memory space. The new treatment scheme is shown to outperform conventional IMRT in terms of dose conformity to both the targetand the critical structures, while maintaining high delivery efficiency.

    View details for DOI 10.1118/1.4729717

    View details for Web of Science ID 000306893000029

    View details for PubMedID 22830765

  • Nonculprit Plaques in Patients With Acute Coronary Syndromes Have More Vulnerable Features Compared With Those With Non-Acute Coronary Syndromes A 3-Vessel Optical Coherence Tomography Study CIRCULATION-CARDIOVASCULAR IMAGING Kato, K., Yonetsu, T., Kim, S., Xing, L., Lee, H., McNulty, I., Yeh, R. W., Sakhuja, R., Zhang, S., Uemura, S., Yu, B., Mizuno, K., Jang, I. 2012; 5 (4): 433-440

    Abstract

    Patients with acute coronary syndrome (ACS) have a higher incidence of recurrent ischemic events. The aim of this study was to compare the plaque characteristics of nonculprit lesions between ACS and non-ACS patients using optical coherence tomography (OCT) imaging.Patients who had 3-vessel OCT imaging were selected from the Massachusetts General Hospital (MGH) OCT Registry. MGH registry is a multicenter registry of patients undergoing OCT. The prevalence and characteristics of nonculprit plaques were compared between ACS and non-ACS patients. A total of 248 nonculprit plaques were found in 104 patients: 45 plaques in 17 ACS patients and 203 plaques in 87 non-ACS patients. Compared with plaques of non-ACS patients, plaques of ACS patients had a wider lipid arc (147.3 ± 29.5° versus 116.2 ± 33.7°, P<0.001), a longer lipid length (10.7 ± 5.9 mm versus 7.0 ± 3.7 mm, P=0.002), a larger lipid volume index [averaged lipid arc×lipid length] (1605.5 ± 1013.1 versus 853.4 ± 570.8, P<0.001), and a thinner fibrous cap (70.2 ± 20.2 µm versus 103.3 ± 46.8 µm, P<0.001). Moreover, thin-cap fibroatheroma (64.7% versus 14.9%, P<0.001), macrophage (82.4% versus 37.9%, P=0.001), and thrombus (29.4% versus 1.1%, P<0.001) were more frequent in ACS patients. Although the prevalence of microchannel did not differ between the groups, the closest distance from the lumen to microchannel was shorter in ACS subjects than in non-ACS (104.6 ± 67.0 µm versus 198.3 ± 133.0 µm, P=0.027).Nonculprit lesions in patients with ACS have more vulnerable plaque characteristics compared with those with non-ACS. Neovascularization was more frequently located close to the lumen in patients with ACS.

    View details for DOI 10.1161/CIRCIMAGING.112.973701

    View details for Web of Science ID 000313573500008

    View details for PubMedID 22679059

  • A Binary Moving Blocker-Based Scatter Correction Technique for Cone-Beam CT with Width-Truncated Projections 54th Annual Meeting and Exhibition of the American-Association-of-Physicists-in-Medicine (AAPM) LEE, H., Fahimian, B., Xing, L. AMER ASSOC PHYSICISTS MEDICINE AMER INST PHYSICS. 2012: 3892–92
  • WE-C-217BCD-07: Best in Physics (Joint Eyiaging-Therapy) - Direct Imaging of the Uptake of Platinum Anticancer Agents Using X-Ray Stimulated Fluorescence: A Proof-Of-Concept Study. Medical physics Kuang, Y., Pratx, G., Qian, J., Meng, B., Bazalova, M., Xing, L. 2012; 39 (6): 3950-3951

    Abstract

    Platinum-based (Pt) chemotherapy has greatly improved the initial response rate of different cancers. However, relapse of a drug-resistant tumor occurs with a high frequency, resulting in poor long term survival. The most common phenotype of Pt chemoresistance is decreased Pt drug accumulation in the tumor region due to either decreased influx or increased efflux, which is invisible for current imaging modalities. The inability of imaging methods to directly image the Pt drug uptake has resulted in a very unfavorable scenario for early assessment of chemotherapeutic efficacy as well as for personalized treatment planning. In this study, we investigated the feasibility of imaging the uptake and retention of Pt drugs using x-ray stimulated fluorescence CT (XSF-CT).Pt is a high atomic number element, and it emits XSF photons when excited by ionizing photons. Therefore, the alteration of spatial distribution and concentration of Pt drugs in the cancer region could be monitored with XSF-CT. In this study, a polychromatic X-ray source was used to stimulate emission of XSF photons from the Pt drugs. XSF-CT used a first-generation CT geometry. The data were collected using a cadmium telluride detector to sort out a set of spectra. The spectra were then used to generate sinogram. The bio distribution and concentration of each element were reconstructed with the ML-EM algorithm.The reconstructed images clearly identified the distributions of cisplatin. A good linearity between XSF intensities and the concentrations of cisplatin was also observed, suggesting that XSF-CT is capable of quantitative imaging. The X-ray dose for stimulating the XSF photon was 0.25 cGy per projection.XSF-CT has the potential to be a promising modality for monitoring the intervention processes within X-ray scanners. It would afford a powerful way to reliably modify an ineffective treatment regimen in nearly real time. This work was supported by NIH/NCI grants (CA133474, CA153587), an NSF grant (0854492) and a grant from the Friends for an Earlier Breast Cancer Test Foundation.

    View details for DOI 10.1118/1.4736123

    View details for PubMedID 28520005

  • Automated Data Mining of Lung SBRT Cases for Predicting Dosimetric Indices in Prospective Plans 54th Annual Meeting and Exhibition of the American-Association-of-Physicists-in-Medicine (AAPM) Atwood, T., Xing, L., Hristov, D. AMER ASSOC PHYSICISTS MEDICINE AMER INST PHYSICS. 2012: 3753–53
  • Single-Scan Scatter Correction in Cone Beam CT Using Stationary Boundary Blockers and Compressed Sensing 54th Annual Meeting and Exhibition of the American-Association-of-Physicists-in-Medicine (AAPM) Meng, B., Xing, L., Fahimian, B. AMER ASSOC PHYSICISTS MEDICINE AMER INST PHYSICS. 2012: 3891–91
  • BEST IN PHYSICS (JOINT IMAGING-THERAPY) - Direct Imaging of the Uptake of Platinum Anticancer Agents Using X-Ray Stimulated Fluorescence: A Proof-Of-Concept Study 54th Annual Meeting and Exhibition of the American-Association-of-Physicists-in-Medicine (AAPM) Kuang, Y., Pratx, G., Qian, J., Meng, B., Bazalova, M., Xing, L. AMER ASSOC PHYSICISTS MEDICINE AMER INST PHYSICS. 2012: 3950–51

    Abstract

    Platinum-based (Pt) chemotherapy has greatly improved the initial response rate of different cancers. However, relapse of a drug-resistant tumor occurs with a high frequency, resulting in poor long term survival. The most common phenotype of Pt chemoresistance is decreased Pt drug accumulation in the tumor region due to either decreased influx or increased efflux, which is invisible for current imaging modalities. The inability of imaging methods to directly image the Pt drug uptake has resulted in a very unfavorable scenario for early assessment of chemotherapeutic efficacy as well as for personalized treatment planning. In this study, we investigated the feasibility of imaging the uptake and retention of Pt drugs using x-ray stimulated fluorescence CT (XSF-CT).Pt is a high atomic number element, and it emits XSF photons when excited by ionizing photons. Therefore, the alteration of spatial distribution and concentration of Pt drugs in the cancer region could be monitored with XSF-CT. In this study, a polychromatic X-ray source was used to stimulate emission of XSF photons from the Pt drugs. XSF-CT used a first-generation CT geometry. The data were collected using a cadmium telluride detector to sort out a set of spectra. The spectra were then used to generate sinogram. The bio distribution and concentration of each element were reconstructed with the ML-EM algorithm.The reconstructed images clearly identified the distributions of cisplatin. A good linearity between XSF intensities and the concentrations of cisplatin was also observed, suggesting that XSF-CT is capable of quantitative imaging. The X-ray dose for stimulating the XSF photon was 0.25 cGy per projection.XSF-CT has the potential to be a promising modality for monitoring the intervention processes within X-ray scanners. It would afford a powerful way to reliably modify an ineffective treatment regimen in nearly real time. This work was supported by NIH/NCI grants (CA133474, CA153587), an NSF grant (0854492) and a grant from the Friends for an Earlier Breast Cancer Test Foundation.

    View details for Web of Science ID 000308905805582

  • Improving Respiration-Gated IMRT Delivery Efficiency by Dual-Gating at Inhale and Exhale: Evaluation of Planning On Eclipse and the Need for Accurate Image Registration 54th Annual Meeting and Exhibition of the American-Association-of-Physicists-in-Medicine (AAPM) Geneser, S., Fahimian, B., Xing, L. AMER ASSOC PHYSICISTS MEDICINE AMER INST PHYSICS. 2012: 3671–71
  • Improving Respiration-Gated IMRT Delivery Efficiency by Dual-Gating at Inhale and Exhale: Treatment Planning Formalism 54th Annual Meeting and Exhibition of the American-Association-of-Physicists-in-Medicine (AAPM) Geneser, S., Fahimian, B., Xing, L. AMER ASSOC PHYSICISTS MEDICINE AMER INST PHYSICS. 2012: 3902–
  • Compton Scatter in X-Ray Fluorescence CT Imaging 54th Annual Meeting and Exhibition of the American-Association-of-Physicists-in-Medicine (AAPM) Bazalova, M., Kuang, Y., Pratx, G., Xing, L. AMER ASSOC PHYSICISTS MEDICINE AMER INST PHYSICS. 2012: 3986–86
  • Radioluminescent nanophosphors enable multiplexed small-animal imaging OPTICS EXPRESS Carpenter, C. M., Sun, C., Pratx, G., Liu, H., Cheng, Z., Xing, L. 2012; 20 (11): 11598-11604

    Abstract

    We demonstrate the ability to image multiple nanoparticle-based contrast agents simultaneously using a nanophosphor platform excited by either radiopharmaceutical or X-ray irradiation. These radioluminescent nanoparticles emit optical light at unique wavelengths depending on their lanthanide dopant, enabling multiplexed imaging. This study demonstrates the separation of two distinct nanophosphor contrast agents in gelatin phantoms with a recovered phosphor separation correlation of -0.98. The ability to distinguish the two nanophosphors and a Cerenkov component is then demonstrated in a small animal phantom. Combined with the high-resolution potential of low-scattering X-ray excitation, this imaging technique may be a promising method to probe molecular processes in living organisms.

    View details for Web of Science ID 000304403100002

    View details for PubMedID 22714145

  • Evaluation of the geometric accuracy of surrogate-based gated VMAT using intrafraction kilovoltage x-ray images MEDICAL PHYSICS Li, R., Mok, E., Han, B., Koong, A., Xing, L. 2012; 39 (5): 2686-2693

    Abstract

    To evaluate the geometric accuracy of beam targeting in external surrogate-based gated volumetric modulated arc therapy (VMAT) using kilovoltage (kV) x-ray images acquired during dose delivery.Gated VMAT treatments were delivered using a Varian TrueBeam STx Linac for both physical phantoms and patients. Multiple gold fiducial markers were implanted near the target. The reference position was created for each implanted marker, representing its correct position at the gating threshold. The gating signal was generated from the RPM system. During the treatment, kV images were acquired immediately before MV beam-on at every breathing cycle, using the on-board imaging system. All implanted markers were detected and their 3D positions were estimated using in-house developed software. The positioning error of a marker is defined as the distance of the marker from its reference position for each frame of the images. The overall error of the system is defined as the average over all markers. For the phantom study, both sinusoidal motion (1D and 3D) and real human respiratory motion was simulated for the target and surrogate. In the baseline case, the two motions were synchronized for the first treatment fraction. To assess the effects of surrogate-target correlation on the geometric accuracy, a phase shift of 5% and 10% between the two motions was introduced. For the patient study, intrafraction kV images of five stereotactic body radiotherapy (SBRT) patients were acquired for one or two fractions.For the phantom study, a high geometric accuracy was achieved in the baseline case (average error: 0.8 mm in the superior-inferior or SI direction). However, the treatment delivery is prone to geometric errors if changes in the target-surrogate relation occur during the treatment: the average error was increased to 2.3 and 4.7 mm for the phase shift of 5% and 10%, respectively. Results obtained with real human respiratory curves show a similar trend. For a target with 3D motion, the technique is able to detect geometric errors in the left-right (LR) and anterior-posterior (AP) directions. For the patient study, the average intrafraction positioning errors are 0.8, 0.9, and 1.4 mm and 95th percentile errors are 1.7, 2.1, and 2.7 mm in the LR, AP, and SI directions, respectively.The correlation between external surrogate and internal target motion is crucial to ensure the geometric accuracy of surrogate-based gating. Real-time guidance based on kV x-ray images overcomes the potential issues in surrogate-based gating and can achieve accurate beam targeting in gated VMAT.

    View details for DOI 10.1118/1.4704729

    View details for Web of Science ID 000303604300039

    View details for PubMedID 22559639

    View details for PubMedCentralID PMC3344884

  • FDG-Cerenkov image guided surgical margin detection Carpenter, C., Liu, H., Sun, C., Pratx, G., Chengand, Z., Xing, L. SOC NUCLEAR MEDICINE INC. 2012
  • Development and quantification of a novel intravascular catheter-based radionuclide imaging system Zaman, R., Carpenter, C., Pratx, G., Sun, C., Xing, L., McConnell, M. SOC NUCLEAR MEDICINE INC. 2012
  • Scatter correction in cone-beam CT via a half beam blocker technique allowing simultaneous acquisition of scatter and image information MEDICAL PHYSICS Lee, H., Xing, L., Lee, R., Fahimian, B. P. 2012; 39 (5): 2386-2395

    Abstract

    X-ray scatter incurred to detectors degrades the quality of cone-beam computed tomography (CBCT) and represents a problem in volumetric image guided and adaptive radiation therapy. Several methods using a beam blocker for the estimation and subtraction of scatter have been proposed. However, due to missing information resulting from the obstruction of the blocker, such methods require dual scanning or dynamically moving blocker to obtain a complete volumetric image. Here, we propose a half beam blocker-based approach, in conjunction with a total variation (TV) regularized Feldkamp-Davis-Kress (FDK) algorithm, to correct scatter-induced artifacts by simultaneously acquiring image and scatter information from a single-rotation CBCT scan.A half beam blocker, comprising lead strips, is used to simultaneously acquire image data on one side of the projection data and scatter data on the other half side. One-dimensional cubic B-Spline interpolation/extrapolation is applied to derive patient specific scatter information by using the scatter distributions on strips. The estimated scatter is subtracted from the projection image acquired at the opposite view. With scatter-corrected projections where this subtraction is completed, the FDK algorithm based on a cosine weighting function is performed to reconstruct CBCT volume. To suppress the noise in the reconstructed CBCT images produced by geometric errors between two opposed projections and interpolated scatter information, total variation regularization is applied by a minimization using a steepest gradient descent optimization method. The experimental studies using Catphan504 and anthropomorphic phantoms were carried out to evaluate the performance of the proposed scheme.The scatter-induced shading artifacts were markedly suppressed in CBCT using the proposed scheme. Compared with CBCT without a blocker, the nonuniformity value was reduced from 39.3% to 3.1%. The root mean square error relative to values inside the regions of interest selected from a benchmark scatter free image was reduced from 50 to 11.3. The TV regularization also led to a better contrast-to-noise ratio.An asymmetric half beam blocker-based FDK acquisition and reconstruction technique has been established. The proposed scheme enables simultaneous detection of patient specific scatter and complete volumetric CBCT reconstruction without additional requirements such as prior images, dual scans, or moving strips.

    View details for DOI 10.1118/1.3691901

    View details for Web of Science ID 000303604300008

    View details for PubMedID 22559608

  • Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints PHYSICS IN MEDICINE AND BIOLOGY Lee, H., Xing, L., Davidi, R., Li, R., Qian, J., Lee, R. 2012; 57 (8): 2287-2307

    Abstract

    Volumetric cone-beam CT (CBCT) images are acquired repeatedly during a course of radiation therapy and a natural question to ask is whether CBCT images obtained earlier in the process can be utilized as prior knowledge to reduce patient imaging dose in subsequent scans. The purpose of this work is to develop an adaptive prior image constrained compressed sensing (APICCS) method to solve this problem. Reconstructed images using full projections are taken on the first day of radiation therapy treatment and are used as prior images. The subsequent scans are acquired using a protocol of sparse projections. In the proposed APICCS algorithm, the prior images are utilized as an initial guess and are incorporated into the objective function in the compressed sensing (CS)-based iterative reconstruction process. Furthermore, the prior information is employed to detect any possible mismatched regions between the prior and current images for improved reconstruction. For this purpose, the prior images and the reconstructed images are classified into three anatomical regions: air, soft tissue and bone. Mismatched regions are identified by local differences of the corresponding groups in the two classified sets of images. A distance transformation is then introduced to convert the information into an adaptive voxel-dependent relaxation map. In constructing the relaxation map, the matched regions (unchanged anatomy) between the prior and current images are assigned with smaller weight values, which are translated into less influence on the CS iterative reconstruction process. On the other hand, the mismatched regions (changed anatomy) are associated with larger values and the regions are updated more by the new projection data, thus avoiding any possible adverse effects of prior images. The APICCS approach was systematically assessed by using patient data acquired under standard and low-dose protocols for qualitative and quantitative comparisons. The APICCS method provides an effective way for us to enhance the image quality at the matched regions between the prior and current images compared to the existing PICCS algorithm. Compared to the current CBCT imaging protocols, the APICCS algorithm allows an imaging dose reduction of 10-40 times due to the greatly reduced number of projections and lower x-ray tube current level coming from the low-dose protocol.

    View details for DOI 10.1088/0031-9155/57/8/2287

    View details for Web of Science ID 000302567100013

    View details for PubMedID 22460008

  • Total-Variation Regularization Based Inverse Planning for Intensity Modulated Arc Therapy TECHNOLOGY IN CANCER RESEARCH & TREATMENT Zhu, L., Niu, T., Choi, K., Xing, L. 2012; 11 (2): 149-162

    Abstract

    Intensity modulated arc therapy (IMAT) delivers conformal dose distributions through continuous gantry rotation with constant or variable speed while modulating the field aperture shape and weight. The enlarged angular space and machine delivery constraints make inverse planning of IMAT more intractable as compared to its counterpart of fixed gantry IMRT. Currently, IMAT inverse planning is being done using two extreme methods: the first one computes in beamlet domain with a subsequent arc leaf sequencing, and the second proceeds in machine parameter domain with entire emphasis placed on a pre-determined delivery method without exploring potentially better alternative delivery schemes. Towards truly optimizing the IMAT treatment on a patient specific basis, in this work we propose a total-variation based inverse planning framework for IMAT, which takes advantage of the useful features of the above two existing approaches while avoiding their shortcomings. A quadratic optimization algorithm has been implemented to demonstrate the performance and advantage of the proposed approach. Applications of the technique to a prostate case and a head and neck case indicate that the algorithm is capable of generating IMAT plans with patient specific numbers of arcs efficiently. Superior dose distributions and delivery time are achieved with a maximum number of apertures of three for each field. As compared to conventional beamlet-based algorithms, our method regularizes the field modulation complexity during optimization, and permits us to obtain the best possible plan with a pre-set modulation complexity of fluences. As illustrated in both prostate and head-and-neck case studies, the proposed method produces more favorable dose distributions than the segment-based algorithms, by optimally accommodating the clinical need of intensity modulation levels for each individual field. On a more fundamental level, our formulation preserves the convexity of optimization and makes the search of the global optimal solution possible with a deterministic method.

    View details for DOI 10.7785/tcrt.2012.500244

    View details for Web of Science ID 000300869500006

    View details for PubMedID 22335409

  • Fiber-based system for imaging tumor margins with Cerenkov Luminescence Liu, H., Carpenter, C. M., Jiang, H., Pratx, G., Sun, C., Buchin, M. P., Gambhir, S. S., Xing, L., Cheng, Z. AMER CHEMICAL SOC. 2012
  • Response to "Comment on 'Bridging the gap between IMRT and VMAT: Dense angularly sampled and sparse intensity modulated radiation therapy'" [Med. Phys. 38, 4912-4919 (2011)] MEDICAL PHYSICS Li, R., Xing, L. 2012; 39 (3): 1676-1676

    View details for DOI 10.1118/1.3687906

    View details for Web of Science ID 000301503400053

    View details for PubMedID 22380399

  • An end-to-end examination of geometric accuracy of IGRT using a new digital accelerator equipped with onboard imaging system PHYSICS IN MEDICINE AND BIOLOGY Wang, L., Kielar, K. N., Mok, E., Hsu, A., Dieterich, S., Xing, L. 2012; 57 (3): 757-769

    Abstract

    The Varian's new digital linear accelerator (LINAC), TrueBeam STx, is equipped with a high dose rate flattening filter free (FFF) mode (6 MV and 10 MV), a high definition multileaf collimator (2.5 mm leaf width), as well as onboard imaging capabilities. A series of end-to-end phantom tests were performed, TrueBeam-based image guided radiation therapy (IGRT), to determine the geometric accuracy of the image-guided setup and dose delivery process for all beam modalities delivered using intensity modulated radiation therapy (IMRT) and RapidArc. In these tests, an anthropomorphic phantom with a Ball Cube II insert and the analysis software (FilmQA (3cognition)) were used to evaluate the accuracy of TrueBeam image-guided setup and dose delivery. Laser cut EBT2 films with 0.15 mm accuracy were embedded into the phantom. The phantom with the film inserted was first scanned with a GE Discovery-ST CT scanner, and the images were then imported to the planning system. Plans with steep dose fall off surrounding hypothetical targets of different sizes were created using RapidArc and IMRT with FFF and WFF (with flattening filter) beams. Four RapidArc plans (6 MV and 10 MV FFF) and five IMRT plans (6 MV and 10 MV FFF; 6 MV, 10 MV and 15 MV WFF) were studied. The RapidArc plans with 6 MV FFF were planned with target diameters of 1 cm (0.52 cc), 2 cm (4.2 cc) and 3 cm (14.1 cc), and all other plans with a target diameter of 3 cm. Both onboard planar and volumetric imaging procedures were used for phantom setup and target localization. The IMRT and RapidArc plans were then delivered, and the film measurements were compared with the original treatment plans using a gamma criteria of 3%/1 mm and 3%/2 mm. The shifts required in order to align the film measured dose with the calculated dose distributions was attributed to be the targeting error. Targeting accuracy of image-guided treatment using TrueBeam was found to be within 1 mm. For irradiation of the 3 cm target, the gammas (3%, 1 mm) were found to be above 90% in all plan deliveries. For irradiations of smaller targets (2 cm and 1 cm), similar accuracy was achieved for 6 MV and 10 MV beams. Slightly degraded accuracy was observed for irradiations with higher energy beam (15 MV). In general, gammas (3%, 2 mm) were found to be above 97% for all the plans. Our end-to-end tests showed an excellent relative dosimetric agreement and sub-millimeter targeting accuracy for 6 MV and 10 MV beams, using both FFF and WFF delivery methods. However, increased deviations in spatial and dosimetric accuracy were found when treating lesions smaller than 2 cm or with 15 MV beam.

    View details for DOI 10.1088/0031-9155/57/3/757

    View details for Web of Science ID 000299542000013

    View details for PubMedID 22252134

    View details for PubMedCentralID PMC5233463

  • Investigations on the electrochemical properties of new conjugated polymers containing benzo[c]cinnoline and oxadiazole moieties POLYMER Chen, J., Wu, H., Chiang, C., Peng, L., Chen, T., Xing, L., Liu, S. 2011; 52 (26): 6011-6019
  • Ultrafast and scalable cone-beam CT reconstruction using MapReduce in a cloud computing environment MEDICAL PHYSICS Meng, B., Pratx, G., Xing, L. 2011; 38 (12): 6603-6609

    Abstract

    Four-dimensional CT (4DCT) and cone beam CT (CBCT) are widely used in radiation therapy for accurate tumor target definition and localization. However, high-resolution and dynamic image reconstruction is computationally demanding because of the large amount of data processed. Efficient use of these imaging techniques in the clinic requires high-performance computing. The purpose of this work is to develop a novel ultrafast, scalable and reliable image reconstruction technique for 4D CBCT∕CT using a parallel computing framework called MapReduce. We show the utility of MapReduce for solving large-scale medical physics problems in a cloud computing environment.In this work, we accelerated the Feldcamp-Davis-Kress (FDK) algorithm by porting it to Hadoop, an open-source MapReduce implementation. Gated phases from a 4DCT scans were reconstructed independently. Following the MapReduce formalism, Map functions were used to filter and backproject subsets of projections, and Reduce function to aggregate those partial backprojection into the whole volume. MapReduce automatically parallelized the reconstruction process on a large cluster of computer nodes. As a validation, reconstruction of a digital phantom and an acquired CatPhan 600 phantom was performed on a commercial cloud computing environment using the proposed 4D CBCT∕CT reconstruction algorithm.Speedup of reconstruction time is found to be roughly linear with the number of nodes employed. For instance, greater than 10 times speedup was achieved using 200 nodes for all cases, compared to the same code executed on a single machine. Without modifying the code, faster reconstruction is readily achievable by allocating more nodes in the cloud computing environment. Root mean square error between the images obtained using MapReduce and a single-threaded reference implementation was on the order of 10(-7). Our study also proved that cloud computing with MapReduce is fault tolerant: the reconstruction completed successfully with identical results even when half of the nodes were manually terminated in the middle of the process.An ultrafast, reliable and scalable 4D CBCT∕CT reconstruction method was developed using the MapReduce framework. Unlike other parallel computing approaches, the parallelization and speedup required little modification of the original reconstruction code. MapReduce provides an efficient and fault tolerant means of solving large-scale computing problems in a cloud computing environment.

    View details for DOI 10.1118/1.3660200

    View details for Web of Science ID 000298250100028

    View details for PubMedID 22149842

    View details for PubMedCentralID PMC3247927

  • Application reliability for communication networks and its analysis method JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS Huang, N., Chen, Y., Hou, D., Xing, L., Kang, R. 2011; 22 (6): 1030-1036
  • Monte Carlo simulation of photon migration in a cloud computing environment with MapReduce JOURNAL OF BIOMEDICAL OPTICS Pratx, G., Xing, L. 2011; 16 (12)

    Abstract

    Monte Carlo simulation is considered the most reliable method for modeling photon migration in heterogeneous media. However, its widespread use is hindered by the high computational cost. The purpose of this work is to report on our implementation of a simple MapReduce method for performing fault-tolerant Monte Carlo computations in a massively-parallel cloud computing environment. We ported the MC321 Monte Carlo package to Hadoop, an open-source MapReduce framework. In this implementation, Map tasks compute photon histories in parallel while a Reduce task scores photon absorption. The distributed implementation was evaluated on a commercial compute cloud. The simulation time was found to be linearly dependent on the number of photons and inversely proportional to the number of nodes. For a cluster size of 240 nodes, the simulation of 100 billion photon histories took 22 min, a 1258 × speed-up compared to the single-threaded Monte Carlo program. The overall computational throughput was 85,178 photon histories per node per second, with a latency of 100 s. The distributed simulation produced the same output as the original implementation and was resilient to hardware failure: the correctness of the simulation was unaffected by the shutdown of 50% of the nodes.

    View details for DOI 10.1117/1.3656964

    View details for Web of Science ID 000299490300011

    View details for PubMedID 22191916

    View details for PubMedCentralID PMC3273307

  • Continued exploration of biphenylsulfonamide scaffold as a platform for aggrecanase-1 inhibition BIOORGANIC & MEDICINAL CHEMISTRY LETTERS Hu, Y., Xing, L., Thomason, J. R., Xiang, J., Ipek, M., Guler, S., Li, H., Sabatini, J., Chockalingam, P., Reifenberg, E., Sheldon, R., Morris, E. A., Georgiadis, K. E., Tam, S. 2011; 21 (22): 6800-6803

    Abstract

    Design, synthesis and structure-activity relationship of a series of biphenylsulfonamido-3-methylbutanoic acid based aggrecanase-1 inhibitors are described. In addition to robust aggrecanase-1 inhibition, these compounds also exhibit potent MMP-13 activity. In cell-based cartilage explants assay compound 48 produced 87% inhibition of proteoglycan degradation at 10 μg/mL. Good pharmacokinetic properties were demonstrated by 46 with a half-life of 6h and bioavailability of 23%.

    View details for DOI 10.1016/j.bmcl.2011.09.036

    View details for Web of Science ID 000296423700032

    View details for PubMedID 21982494

  • Toward real-time Monte Carlo simulation using a commercial cloud computing infrastructure PHYSICS IN MEDICINE AND BIOLOGY Wang, H., Ma, Y., Pratx, G., Xing, L. 2011; 56 (17): N175-N181

    Abstract

    Monte Carlo (MC) methods are the gold standard for modeling photon and electron transport in a heterogeneous medium; however, their computational cost prohibits their routine use in the clinic. Cloud computing, wherein computing resources are allocated on-demand from a third party, is a new approach for high performance computing and is implemented to perform ultra-fast MC calculation in radiation therapy. We deployed the EGS5 MC package in a commercial cloud environment. Launched from a single local computer with Internet access, a Python script allocates a remote virtual cluster. A handshaking protocol designates master and worker nodes. The EGS5 binaries and the simulation data are initially loaded onto the master node. The simulation is then distributed among independent worker nodes via the message passing interface, and the results aggregated on the local computer for display and data analysis. The described approach is evaluated for pencil beams and broad beams of high-energy electrons and photons. The output of cloud-based MC simulation is identical to that produced by single-threaded implementation. For 1 million electrons, a simulation that takes 2.58 h on a local computer can be executed in 3.3 min on the cloud with 100 nodes, a 47× speed-up. Simulation time scales inversely with the number of parallel nodes. The parallelization overhead is also negligible for large simulations. Cloud computing represents one of the most important recent advances in supercomputing technology and provides a promising platform for substantially improved MC simulation. In addition to the significant speed up, cloud computing builds a layer of abstraction for high performance parallel computing, which may change the way dose calculations are performed and radiation treatment plans are completed.

    View details for DOI 10.1088/0031-9155/56/17/N02

    View details for PubMedID 21841211

  • Bridging the gap between IMRT and VMAT: Dense angularly sampled and sparse intensity modulated radiation therapy MEDICAL PHYSICS Li, R., Xing, L. 2011; 38 (9): 4912-4919

    Abstract

    To propose an alternative radiation therapy (RT) planning and delivery scheme with optimal angular beam sampling and intrabeam modulation for improved dose distribution while maintaining high delivery efficiency.In the proposed approach, coined as dense angularly sampled and sparse intensity modulated RT (DASSIM-RT), a large number of beam angles are used to increase the angular sampling, leading to potentially more conformal dose distributions as compared to conventional IMRT. At the same time, intensity modulation of the incident beams is simplified to eliminate the dispensable segments, compensating the increase in delivery time caused by the increased number of beams and facilitating the plan delivery. In a sense, the proposed approach shifts and transforms, in an optimal fashion, some of the beam segments in conventional IMRT to the added beams. For newly available digital accelerators, the DASSIM-RT delivery can be made very efficient by concatenating the beams so that they can be delivered sequentially without operator's intervention. Different from VMAT, the level of intensity modulation in DASSIS-RT is field specific and optimized to meet the need of each beam direction. Three clinical cases (a head and neck (HN) case, a pancreas case, and a lung case) are used to evaluate the proposed RT scheme. DASSIM-RT, VMAT, and conventional IMRT plans are compared quantitatively in terms of the conformality index (CI) and delivery efficiency.Plan quality improves generally with the number and intensity modulation of the incident beams. For a fixed number of beams or fixed level of intensity modulation, the improvement saturates after the intensity modulation or number of beams reaches to a certain level. An interplay between the two variables is observed and the saturation point depends on the values of both variables. For all the cases studied here, the CI of DASSIM-RT with 15 beams and 5 intensity levels (0.90, 0.79, and 0.84 for the HN, pancreas, and lung cases, respectively) is similar with that of conventional IMRT with seven beams and ten intensity levels (0.88, 0.79, and 0.83) and is higher than that of single-arc VMAT (0.75, 0.75, and 0.82). It is also found that the DASSIM-RT plans generally have better sparing of organs-at-risk than IMRT plans. It is estimated that the dose delivery time of DASSIM-RT with 15 beams and 5 intensity levels is about 4.5, 4.4, and 4.2 min for the HN, pancreas, and lung case, respectively, similar to that of IMRT plans with 7 beams and 10 intensity levels.DASSIS-RT bridges the gap between IMRT and VMAT and allows optimal sampling of angular space and intrabeam modulation, thus it provides improved conformity in dose distributions while maintaining high delivery efficiency.

    View details for DOI 10.1118/1.3618736

    View details for Web of Science ID 000294482900002

    View details for PubMedID 21978036

    View details for PubMedCentralID PMC3166337

  • Dose verification for respiratory-gated volumetric modulated arc therapy PHYSICS IN MEDICINE AND BIOLOGY Qian, J., Xing, L., Liu, W., Luxton, G. 2011; 56 (15): 4827-4838

    Abstract

    A novel commercial medical linac system (TrueBeam™, Varian Medical Systems, Palo Alto, CA) allows respiratory-gated volumetric modulated arc therapy (VMAT), a new modality for treating moving tumors with high precision and improved accuracy by allowing for regular motion associated with a patient's breathing during VMAT delivery. The purpose of this work is to adapt a previously-developed dose reconstruction technique to evaluate the fidelity of VMAT treatment during gated delivery under clinic-relevant periodic motion related to patient breathing. A Varian TrueBeam system was used in this study. VMAT plans were created for three patients with lung or pancreas tumors. Conventional 6 and 15 MV beams with flattening filter and high-dose-rate 10 MV beams with no flattening filter were used in these plans. Each patient plan was delivered to a phantom first without gating and then with gating for three simulated respiratory periods (3, 4.5 and 6 s). Using the adapted log-file-based dose reconstruction procedure supplemented with ion chamber array (Seven29™, PTW, Freiburg, Germany) measurements, the delivered dose was used to evaluate the fidelity of gated VMAT delivery. Comparison of Seven29 measurements with and without gating showed good agreement with gamma-index passing rates above 99% for 1%/1 mm dose accuracy/distance-to-agreement criteria. With original plans as reference, gamma-index passing rates were 100% for the reconstituted plans (1%/1 mm criteria) and 93.5-100% for gated Seven29 measurements (3%/3 mm criteria). In the presence of leaf error deliberately introduced into the gated delivery of a pancreas patient plan, both dose reconstruction and Seven29 measurement consistently indicated substantial dosimetric differences from the original plan. In summary, a dose reconstruction procedure was demonstrated for evaluating the accuracy of respiratory-gated VMAT delivery. This technique showed that under clinical operation, the TrueBeam system faithfully realized treatment plans with gated delivery. This methodology affords a useful tool for machine- and patient-specific quality assurance of the newly available respiratory-gated VMAT.

    View details for DOI 10.1088/0031-9155/56/15/013

    View details for Web of Science ID 000292885000014

    View details for PubMedID 21753232

    View details for PubMedCentralID PMC3360016

  • A Bayesian approach to real-time 3D tumor localization via monoscopic x-ray imaging during treatment delivery MEDICAL PHYSICS Li, R., Fahimian, B. P., Xing, L. 2011; 38 (7): 4205-4214

    Abstract

    Monoscopic x-ray imaging with on-board kV devices is an attractive approach for real-time image guidance in modern radiation therapy such as VMAT or IMRT, but it falls short in providing reliable information along the direction of imaging x-ray. By effectively taking consideration of projection data at prior times and/or angles through a Bayesian formalism, the authors develop an algorithm for real-time and full 3D tumor localization with a single x-ray imager during treatment delivery.First, a prior probability density function is constructed using the 2D tumor locations on the projection images acquired during patient setup. Whenever an x-ray image is acquired during the treatment delivery, the corresponding 2D tumor location on the imager is used to update the likelihood function. The unresolved third dimension is obtained by maximizing the posterior probability distribution. The algorithm can also be used in a retrospective fashion when all the projection images during the treatment delivery are used for 3D localization purposes. The algorithm does not involve complex optimization of any model parameter and therefore can be used in a "plug-and-play" fashion. The authors validated the algorithm using (1) simulated 3D linear and elliptic motion and (2) 3D tumor motion trajectories of a lung and a pancreas patient reproduced by a physical phantom. Continuous kV images were acquired over a full gantry rotation with the Varian TrueBeam on-board imaging system. Three scenarios were considered: fluoroscopic setup, cone beam CT setup, and retrospective analysis.For the simulation study, the RMS 3D localization error is 1.2 and 2.4 mm for the linear and elliptic motions, respectively. For the phantom experiments, the 3D localization error is < 1 mm on average and < 1.5 mm at 95th percentile in the lung and pancreas cases for all three scenarios. The difference in 3D localization error for different scenarios is small and is not statistically significant.The proposed algorithm eliminates the need for any population based model parameters in monoscopic image guided radiotherapy and allows accurate and real-time 3D tumor localization on current standard LINACs with a single x-ray imager.

    View details for DOI 10.1118/1.3598435

    View details for Web of Science ID 000292521100037

    View details for PubMedID 21859022

    View details for PubMedCentralID PMC3145219

  • Synthesis and Radioluminescence of PEGylated Eu3+-doped Nanophosphors as Bioimaging Probes ADVANCED MATERIALS Sun, C., Pratx, G., Carpenter, C. M., Liu, H., Cheng, Z., Gambhir, S. S., Xing, L. 2011; 23 (24): H195-H199

    View details for DOI 10.1002/adma.201100919

    View details for Web of Science ID 000293046600018

    View details for PubMedID 21557339

    View details for PubMedCentralID PMC4145869

  • Limited-angle x-ray luminescence tomography: methodology and feasibility study PHYSICS IN MEDICINE AND BIOLOGY Carpenter, C. M., Pratx, G., Sun, C., Xing, L. 2011; 56 (12): 3487-3502

    Abstract

    X-ray luminescence tomography (XLT) has recently been proposed as a new imaging modality for biological imaging applications. This modality utilizes phosphor nanoparticles which luminesce near-infrared light when excited by x-ray photons. The advantages of this modality are that it uniquely combines the high sensitivity of radioluminescent nanoparticles and the high spatial localization of collimated x-ray beams. Currently, XLT has been demonstrated using x-ray spatial encoding to resolve the imaging volume. However, there are applications where the x-ray excitation may be limited by geometry, where increased temporal resolution is desired, or where a lower dose is mandatory. This paper extends the utility of XLT to meet these requirements by incorporating a photon propagation model into the reconstruction algorithm in an x-ray limited-angle (LA) geometry. This enables such applications as image-guided surgery, where the ability to resolve lesions at depths of several centimeters can be the key to successful resection. The hybrid x-ray/diffuse optical model is first formulated and then demonstrated in a breast-sized phantom, simulating a breast lumpectomy geometry. Both numerical and experimental phantoms are tested, with lesion-simulating objects of various sizes and depths. Results show localization accuracy with median error of 2.2 mm, or 4% of object depth, for small 2-14 mm diameter lesions positioned from 1 to 4.5 cm in depth. This compares favorably with fluorescence optical imaging, which is not able to resolve such small objects at this depth. The recovered lesion size has lower size bias in the x-ray excitation direction than the optical direction, which is expected due to the increased optical scatter. However, the technique is shown to be quite invariant in recovered size with respect to depth, as the standard deviation is less than 2.5 mm. Sensitivity is a function of dose; radiological doses are found to provide sufficient recovery for µg ml(-1) concentrations, while therapy dosages provide recovery for ng ml(-1) concentrations. Experimental phantom results agree closely with the numerical results, with positional errors recovered within 8.6% of the effective depth for a 5 mm object, and within 5.2% of the depth for a 10 mm object. Object-size median error is within 2.3% and 2% for the 5 and 10 mm objects, respectively. For shallow-to-medium depth applications where optical and radio-emission imaging modalities are not ideal, such as in intra-operative procedures, LAXLT may be a useful tool to detect molecular signatures of disease.

    View details for DOI 10.1088/0031-9155/56/12/003

    View details for Web of Science ID 000291095700004

    View details for PubMedID 21606553

    View details for PubMedCentralID PMC4132056

  • In vivo MRSI of hyperpolarized [1-C-13]pyruvate metabolism in rat hepatocellular carcinoma NMR IN BIOMEDICINE Darpolor, M. M., Yen, Y., Chua, M., Xing, L., Clarke-Katzenberg, R. H., Shi, W., Mayer, D., Josan, S., Hurd, R. E., Pfefferbaum, A., Senadheera, L., So, S., Hofmann, L. V., Glazer, G. M., Spielman, D. M. 2011; 24 (5): 506-513

    Abstract

    Hepatocellular carcinoma (HCC), the primary form of human adult liver malignancy, is a highly aggressive tumor with average survival rates that are currently less than 1 year following diagnosis. Most patients with HCC are diagnosed at an advanced stage, and no efficient marker exists for the prediction of prognosis and/or response(s) to therapy. We have reported previously a high level of [1-(13)C]alanine in an orthotopic HCC using single-voxel hyperpolarized [1-(13)C]pyruvate MRS. In the present study, we implemented a three-dimensional MRSI sequence to investigate this potential hallmark of cellular metabolism in rat livers bearing HCC (n = 7 buffalo rats). In addition, quantitative real-time polymerase chain reaction was used to determine the mRNA levels of lactate dehydrogenase A, nicotinamide adenine (phosphate) dinucleotide dehydrogenase quinone 1 and alanine transaminase. The enzyme levels were significantly higher in tumor than in normal liver tissues within each rat, and were associated with the in vivo MRSI signal of [1-(13)C]alanine and [1-(13)C]lactate after a bolus intravenous injection of [1-(13)C]pyruvate. Histopathological analysis of these tumors confirmed the successful growth of HCC as a nodule in buffalo rat livers, revealing malignancy and hypervascular architecture. More importantly, the results demonstrated that the metabolic fate of [1-(13)C]pyruvate conversion to [1-(13)C]alanine significantly superseded that of [1-(13)C]pyruvate conversion to [1-(13)C]lactate, potentially serving as a marker of HCC tumors.

    View details for DOI 10.1002/nbm.1616

    View details for Web of Science ID 000291597200009

    View details for PubMedID 21674652

    View details for PubMedCentralID PMC3073155

  • GPU computing in medical physics: A review MEDICAL PHYSICS Pratx, G., Xing, L. 2011; 38 (5): 2685-2697

    Abstract

    The graphics processing unit (GPU) has emerged as a competitive platform for computing massively parallel problems. Many computing applications in medical physics can be formulated as data-parallel tasks that exploit the capabilities of the GPU for reducing processing times. The authors review the basic principles of GPU computing as well as the main performance optimization techniques, and survey existing applications in three areas of medical physics, namely image reconstruction, dose calculation and treatment plan optimization, and image processing.

    View details for DOI 10.1118/1.3578605

    View details for PubMedID 21776805

  • Initial application of a geometric QA tool for integrated MV and kV imaging systems on three image guided radiotherapy systems MEDICAL PHYSICS Mao, W., Speiser, M., Medin, P., Papiez, L., Solberg, T., Xing, L. 2011; 38 (5): 2335-2341

    Abstract

    Several linacs with integrated kilovoltage (kV) imaging have been developed for delivery of image guided radiation therapy (IGRT). High geometric accuracy and coincidence of kV imaging systems and megavoltage (MV) beam delivery are essential for successful image guidance. A geometric QA tool has been adapted for routine QA for evaluating and characterizing the geometric accuracy of kV and MV cone-beam imaging systems. The purpose of this work is to demonstrate the application of methodology to routine QA across three IGRT-dedicated linac platforms.It has been applied to a Varian Trilogy (Varian Medical Systems, Palo Alto, CA), an Elekta SynergyS (Elekta, Stockholm, Sweden), and a Brainlab Vero (Brainlab AG, Feldkirchen, Germany). Both the Trilogy and SynergyS linacs are equipped with a retractable kV x-ray tube and a flat panel detector. The Vero utilizes a rotating, rigid ring structure integrating a MV x-ray head mounted on orthogonal gimbals, an electronic portal imaging device (EPID), two kV x-ray tubes, and two fixed flat panel detectors. This dual kV imaging system provides orthogonal radiographs, CBCT images, and real-time fluoroscopic monitoring. Two QA phantoms were built to suit different field sizes. Projection images of a QA phantom were acquired using MV and kV imaging systems at a series of gantry angles. Software developed for this study was used to analyze the projection images and calculate nine geometric parameters for each projection. The Trilogy was characterized five times over one year, while the SynergyS was characterized four times and the Vero once. Over 6500 individual projections were acquired and analyzed. Quantitative geometric parameters of both MV and kV imaging systems, as well as the isocenter consistency of the imaging systems, were successfully evaluated.A geometric tool has been successfully implemented for calibration and QA of integrated kV and MV across a variety of radiotherapy platforms. X-ray source angle deviations up to 0.8 degrees, and detector center offsets up to 3 mm, were observed for three linacs, with the exception of the Vero, for which a significant center offset of one kV detector (prior to machine commissioning) was observed. In contrast, the gimbal-based MV source positioning of the Vero demonstrated differences between observed and expected source positions of less than 0.2 mm, both with and without gimbal rotation.This initial application of this geometric QA tool shows promise as a universal, independent tool for quantitative evaluation of geometric accuracies of both MV and integrated kV imaging systems across a range of platforms. It provides nine geometric parameters of any imaging system at every gantry angle as well as the isocenter coincidence of the MV and kV image systems.

    View details for DOI 10.1118/1.3570768

    View details for Web of Science ID 000290625700006

    View details for PubMedID 21776767

  • Multisource modeling of flattening filter free (FFF) beam and the optimization of model parameters MEDICAL PHYSICS Cho, W., Kielar, K. N., Mok, E., Xing, L., Park, J., Jung, W., Suh, T. 2011; 38 (4): 1931-1942

    Abstract

    With the introduction of flattening filter free (FFF) linear accelerators to radiation oncology, new analytical source models for a FFF beam applicable to current treatment planning systems is needed. In this work, a multisource model for the FFF beam and the optimization of involved model parameters were designed.The model is based on a previous three source model proposed by Yang et al. ["A three-source model for the calculation of head scatter factors," Med. Phys. 29, 2024-2033 (2002)]. An off axis ratio (OAR) of photon fluence was introduced to the primary source term to generate cone shaped profiles. The parameters of the source model were determined from measured head scatter factors using a line search optimization technique. The OAR of the photon fluence was determined from a measured dose profile of a 40 x 40 cm2 field size with the same optimization technique, but a new method to acquire gradient terms for OARs was developed to enhance the speed of the optimization process. The improved model was validated with measured dose profiles from 3 x 3 to 40 x 40 cm2 field sizes at 6 and 10 MV from a TrueBeam STx linear accelerator. Furthermore, planar dose distributions for clinically used radiation fields were also calculated and compared to measurements using a 2D array detector using the gamma index method.All dose values for the calculated profiles agreed with the measured dose profiles within 0.5% at 6 and 10 MV beams, except for some low dose regions for larger field sizes. A slight overestimation was seen in the lower penumbra region near the field edge for the large field sizes by 1%-4%. The planar dose calculations showed comparable passing rates (> 98%) when the criterion of the gamma index method was selected to be 3%/3 mm.The developed source model showed good agreements between measured and calculated dose distributions. The model is easily applicable to any other linear accelerator using FFF beams as the required data include only the measured PDD, dose profiles, and output factors for various field sizes, which are easily acquired during conventional beam commissioning process.

    View details for DOI 10.1118/1.3560426

    View details for Web of Science ID 000289153500020

    View details for PubMedID 21626926

    View details for PubMedCentralID PMC3188653

  • Metal artifact reduction in x-ray computed tomography (CT) by constrained optimization MEDICAL PHYSICS Zhang, X., Wang, J., Xing, L. 2011; 38 (2): 701-711

    Abstract

    The streak artifacts caused by metal implants have long been recognized as a problem that limits various applications of CT imaging. In this work, the authors propose an iterative metal artifact reduction algorithm based on constrained optimization.After the shape and location of metal objects in the image domain is determined automatically by the binary metal identification algorithm and the segmentation of "metal shadows" in projection domain is done, constrained optimization is used for image reconstruction. It minimizes a predefined function that reflects a priori knowledge of the image, subject to the constraint that the estimated projection data are within a specified tolerance of the available metal-shadow-excluded projection data, with image non-negativity enforced. The minimization problem is solved through the alternation of projection-onto-convex-sets and the steepest gradient descent of the objective function. The constrained optimization algorithm is evaluated with a penalized smoothness objective.The study shows that the proposed method is capable of significantly reducing metal artifacts, suppressing noise, and improving soft-tissue visibility. It outperforms the FBP-type methods and ART and EM methods and yields artifacts-free images.Constrained optimization is an effective way to deal with CT reconstruction with embedded metal objects. Although the method is presented in the context of metal artifacts, it is applicable to general "missing data" image reconstruction problems.

    View details for DOI 10.1118/1.3533711

    View details for Web of Science ID 000286945000017

    View details for PubMedID 21452707

    View details for PubMedCentralID PMC3033877

  • Automated detection of junctions structures and tracking of their trajectories in 4D images. Information processing in medical imaging : proceedings of the ... conference Xiong, G., Xing, L. 2011; 22: 486-497

    Abstract

    Junction structures, as the natural anatomical markers, are useful to study the organ or tumor motion. However, detection and tracking of the junctions in four-dimensional (4D) images are challenging. The paper presents a novel framework to automate this task. Detection of their centers and sizes is first achieved by an analysis of local shape profiles on one segmented reference image. Junctions are then separately tracked by simultaneously using neighboring intensity features from all images. Defined by a closed B-spline space curve, the individual trajectory is assumed to be cyclic and obtained by maximizing the metric of combined correlation coefficients. Local extrema are suppressed by improving the initial conditions using random walks from pair-wise optimizations. Our approach has been applied to analyze the vessel junctions in five real 4D respiration-gated computed tomography (CT) image datasets with promising results. More than 500 junctions in the lung are detected with an average accuracy of greater than 85% and the mean error between the automated and the manual tracking is sub-voxel.

    View details for PubMedID 21761680

  • Computed Tomography as a Tool for Anatomical and Molecular Imaging NANOPLATFORM-BASED MOLECULAR IMAGING Liu, P., Zhou, H., Xing, L., Chen 2011: 107–37
  • Three- and Four-Dimensional Morphological Imaging for Adaptive Radiation Therapy Planning ADAPTIVE RADIATION THERAPY Xing, L., Qian, J., Choi, K., Suh, T., Li, X. A. 2011: 19–34
  • Facile Synthesis of Amine-Functionalized Eu3+-Doped La(OH)(3) Nanophosphors for Bioimaging NANOSCALE RESEARCH LETTERS Sun, C., Carpenter, C., Pratx, G., Xing, L. 2011; 6

    Abstract

    Here, we report a straightforward synthesis process to produce colloidal Eu(3+)-activated nanophosphors (NPs) for use as bioimaging probes. In this procedure, poly(ethylene glycol) serves as a high-boiling point solvent allowing for nanoscale particle formation as well as a convenient medium for solvent exchange and subsequent surface modification. The La(OH)3:Eu(3+) NPs produced by this process were ~3.5 nm in diameter as determined by transmission electron microscopy. The NP surface was coated with aminopropyltriethoxysilane to provide chemical functionality for attachment of biological ligands, improve chemical stability and prevent surface quenching of luminescent centers. Photoluminescence spectroscopy of the NPs displayed emission peaks at 597 and 615 nm (λex = 280 nm). The red emission, due to (5)D0 → (7)F1 and (5)D0 → (7)F2 transitions, was linear with concentration as observed by imaging with a conventional bioimaging system. To demonstrate the feasibility of these NPs to serve as optical probes in biological applications, an in vitro experiment was performed with HeLa cells. NP emission was observed in the cells by fluorescence microscopy. In addition, the NPs displayed no cytotoxicity over the course of a 48-h MTT cell viability assay. These results suggest that La(OH)3:Eu(3+) NPs possess the potential to serve as a luminescent bioimaging probe.

    View details for DOI 10.1007/s11671-010-9768-x

    View details for Web of Science ID 000289104200024

    View details for PubMedCentralID PMC3211300

  • Iterative prescription refinement in fully discretized inverse problems of radiation therapy planning INVERSE PROBLEMS IN SCIENCE AND ENGINEERING Censor, Y., Xing, L. 2011; 19 (8): 1125-1137
  • Reducing Gated IMRT Delivery Times: Dual-gated Delivery Optimization and Implementation Geneser, S., Fahimian, B., Kielar, K., Xing, L. ELSEVIER SCIENCE INC. 2011: S202–S202
  • Inverse planning for IMRT with nonuniform beam profiles using total-variation regularization (TVR) MEDICAL PHYSICS Kim, T., Zhu, L., Suh, T., Geneser, S., Meng, B., Xing, L. 2011; 38 (1): 57-66

    Abstract

    Radiation therapy with high dose rate and flattening filter-free (FFF) beams has the potential advantage of greatly reduced treatment time and out-of-field dose. Current inverse planning algorithms are, however, not customized for beams with nonuniform incident profiles and the resultant IMRT plans are often inefficient in delivery. The authors propose a total-variation regularization (TVR)-based formalism by taking the inherent shapes of incident beam profiles into account.A novel TVR-based inverse planning formalism is established for IMRT with nonuniform beam profiles. The authors introduce a TVR term into the objective function, which encourages piecewise constant fluence in the nonuniform FFF fluence domain. The proposed algorithm is applied to lung and prostate and head and neck cases and its performance is evaluated by comparing the resulting plans to those obtained using a conventional beamlet-based optimization (BBO).For the prostate case, the authors' algorithm produces acceptable dose distributions with only 21 segments, while the conventional BBO requires 114 segments. For the lung case and the head and neck case, the proposed method generates similar coverage of target volume and sparing of the organs-at-risk as compared to BBO, but with a markedly reduced segment number.TVR-based optimization in nonflat beam domain provides an effective way to maximally leverage the technical capacity of radiation therapy with FFF fields. The technique can generate effective IMRT plans with improved dose delivery efficiency without significant deterioration of the dose distribution.

    View details for DOI 10.1118/1.3521465

    View details for Web of Science ID 000285769800008

    View details for PubMedID 21361175

    View details for PubMedCentralID PMC3017580

  • Toward Truly Optimal IMRT Dose Distribution: Inverse Planning with Voxel-specific Penalty TECHNOLOGY IN CANCER RESEARCH & TREATMENT Lougovski, P., LeNoach, J., Zhu, L., Ma, Y., Censor, Y., Xing, L. 2010; 9 (6): 629-636

    Abstract

    To establish an inverse planning framework with adjustable voxel penalty for more conformal IMRT dose distribution as well as improved interactive controllability over the regional dose distribution of the resultant plan.In the proposed coarse-to-fine planning scheme, a conventional inverse planning with organ specific parameters is first performed. The voxel penalty scheme is then "switched on" by allowing the prescription dose to change on an individual voxel scale according to the deviation of the actual voxel dose from the ideally desired dose. The rationale here is intuitive: when the dose at a voxel does not meet its ideal dose, it simply implies that this voxel is not competitive enough when compared with the ones that have met their planning goal. In this case, increasing the penalty of the voxel by varying the prescription can boost its competitiveness and thus improve its dose. After the prescription adjustment, the plan is re-optimized. The dose adjustment/re-optimization procedure is repeated until the resultant dose distribution cannot be improved anymore. The prescription adjustment on a finer scale can be accomplished either automatically or manually. In the latter case, the regions/voxels where a dose improvement is needed are selected visually, unlike in the automatic case where the selection is done purely based on the difference of the actual dose at a given voxel and its ideal prescription. The performance of the proposed method is evaluated using a head and neck and a prostate case.An inverse planning framework with the voxel-specific penalty is established. By adjusting voxel prescriptions iteratively to boost the region where large mismatch between the actual calculated and desired doses occurs, substantial improvements can be achieved in the final dose distribution. The proposed method is applied to a head and neck case and a prostate case. For the former case, a significant reduction in the maximum dose to the brainstem is achieved while the PTV dose coverage is greatly improved. The doses to other organs at risk are also reduced, ranging from 10% to 30%. For the prostate case, the use of the voxel penalty scheme also results in vast improvements to the final dose distribution. The PTV experiences improved dose uniformity and the mean dose to the rectum and bladder is reduced by as much as 15%.Introduction of the spatially non-uniform and adjustable prescription provides room for further improvements of currently achievable dose distributions and equips the planner with an effective tool to modify IMRT dose distributions interactively. The technique is easily implementable in any existing inverse planning platform, which should facilitate clinical IMRT planning process and, in future, off-line/on-line adaptive IMRT.

    View details for Web of Science ID 000284971100011

    View details for PubMedID 21070085

    View details for PubMedCentralID PMC3057528

  • X-Ray Luminescence Computed Tomography via Selective Excitation: A Feasibility Study IEEE TRANSACTIONS ON MEDICAL IMAGING Pratx, G., Carpenter, C. M., Sun, C., Xing, L. 2010; 29 (12): 1992-1999

    Abstract

    X-ray luminescence computed tomography (XLCT) is proposed as a new molecular imaging modality based on the selective excitation and optical detection of X-ray-excitable phosphor nanoparticles. These nano-sized particles can be fabricated to emit near-infrared (NIR) light when excited with X-rays, and, because because both X-rays and NIR photons propagate long distances in tissue, they are particularly well suited for in vivo biomedical imaging. In XLCT, tomographic images are generated by irradiating the subject using a sequence of programmed X-ray beams, while sensitive photo-detectors measure the light diffusing out of the subject. By restricting the X-ray excitation to a single, narrow beam of radiation, the origin of the optical photons can be inferred regardless of where these photons were detected, and how many times they scattered in tissue. This study presents computer simulations exploring the feasibility of imaging small objects with XLCT, such as research animals. The accumulation of 50 nm phosphor nanoparticles in a 2-mm-diameter target can be detected and quantified with subpicomolar sensitivity using less than 1 cGy of radiation dose. Provided sufficient signal-to-noise ratio, the spatial resolution of the system can be made as high as needed by narrowing the beam aperture. In particular, 1 mm spatial resolution was achieved for a 1-mm-wide X-ray beam. By including an X-ray detector in the system, anatomical imaging is performed simultaneously with molecular imaging via standard X-ray computed tomography (CT). The molecular and anatomical images are spatially and temporally co-registered, and, if a single-pixel X-ray detector is used, they have matching spatial resolution.

    View details for DOI 10.1109/TMI.2010.2055883

    View details for Web of Science ID 000284848700004

    View details for PubMedID 20615807

  • A unified framework for 3D radiation therapy and IMRT planning: plan optimization in the beamlet domain by constraining or regularizing the fluence map variations PHYSICS IN MEDICINE AND BIOLOGY Meng, B., Zhu, L., Widrow, B., Boyd, S., Xing, L. 2010; 55 (22): N521-N531

    Abstract

    The purpose of this work is to demonstrate that physical constraints on fluence gradients in 3D radiation therapy (RT) planning can be incorporated into beamlet optimization explicitly by direct constraint on the spatial variation of the fluence maps or implicitly by using total-variation regularization (TVR). The former method forces the fluence to vary in accordance with the known form of a wedged field and latter encourages the fluence to take the known form of the wedged field by requiring the derivatives of the fluence maps to be piece-wise constant. The performances of the proposed methods are evaluated by using a brain cancer case and a head and neck case. It is found that both approaches are capable of providing clinically sensible 3D RT solutions with monotonically varying fluence maps. For currently available 3D RT delivery schemes based on the use of customized physical or dynamic wedges, constrained optimization seems to be more useful because the optimized fields are directly deliverable. Working in the beamlet domain provides a natural way to model the spatial variation of the beam fluence. The proposed methods take advantage of the fact that 3D RT is a special form of intensity-modulated radiation therapy (IMRT) and finds the optimal plan by searching for fields with a certain type of spatial variation. The approach provides a unified framework for 3D CRT and IMRT plan optimization.

    View details for DOI 10.1088/0031-9155/55/22/N01

    View details for Web of Science ID 000283789700001

    View details for PubMedID 21030744

  • Inverse planning for four-dimensional (4D) volumetric modulated arc therapy MEDICAL PHYSICS Ma, Y., Chang, D., Keall, P., Xie, Y., Park, J., Suh, T., Xing, L. 2010; 37 (11): 5627-5633

    Abstract

    To develop a 4D volumetric modulated arc therapy (VMAT) inverse planning framework.4D VMAT inverse planning aims to derive an aperture and weight modulated arc therapy treatment plan that optimizes the accumulated dose distribution from all gantry angles and breathing phases. Under an assumption that the gantry rotation and patient breathing are synchronized (i.e., there is a functional relationship between the phase of the patient breathing cycle and the beam angle), the authors compute the contribution from different respiration phases through the registration of the phased CT images. The accumulative dose distribution is optimized by iteratively adjusting the aperture shape and weight of each beam through the minimization of the planning objective function. For comparison, traditional 3D VMAT plans are also performed for the two cases and the performance of the proposed technique is demonstrated.A framework for 4D VMAT inverse planning has been proposed. With the consideration of the extra dimension of time in VMAT, a tighter target margin can be achieved with a full duty cycle, which is otherwise not achievable simultaneously by either 3D VMAT optimization or gated VMAT.The 4D VMAT planning formulism proposed here provides useful insight on how the "time" dimension can be exploited in rotational arc therapy to maximally compensate for the intrafraction organ motion.

    View details for DOI 10.1118/1.3497271

    View details for PubMedID 21158274

  • Sinogram preprocessing and binary reconstruction for determination of the shape and location of metal objects in computed tomography (CT) MEDICAL PHYSICS Meng, B., Wang, J., Xing, L. 2010; 37 (11): 5867-5875

    Abstract

    To develop a binary image reconstruction method for the autolocalization of metallic object(s) in CT with sparse projections.The authors divide the system into two types of contents: Metal(s) and nonmetal(s). The boundaries of metallic objects are obtained by using a penalized weighted least-squares algorithm with the adequate intensity gradient-controlled. A novel mechanism of "amplifying" the difference between metal(s) and nonmetallic substances is introduced by preprocessing the sinogram data, which is shown to be necessary in dealing with a case with sparse projection data. A series of experimental studies are performed to evaluate the proposed approach.A novel binary CT image reconstruction formalism is established for the autodetermination of the shape and location of metallic objects in the presence of limited number of projections. Experimental studies reveal that the presented algorithm works well even when the embedded metal object(s) has different shape(s). It is also shown that when the projection data are sparse, a differential manipulation of projection data can greatly facilitate the binary reconstruction process and allow the authors to obtain accurate binary CT images that would otherwise be unattainable.Binary CT reconstruction provides a viable method for determining the geometric distribution information of the implanted metal objects in CT imaging.

    View details for DOI 10.1118/1.3505294

    View details for Web of Science ID 000283747600033

    View details for PubMedID 21158299

    View details for PubMedCentralID PMC2980545

  • A FAILURE DETECTION STRATEGY FOR INTRAFRACTION PROSTATE MOTION MONITORING WITH ON-BOARD IMAGERS FOR FIXED-GANTRY IMRT 51st Annual Meeting of the American-Association-of-Physicists-in-Medicine Liu, W., Luxton, G., Xing, L. ELSEVIER SCIENCE INC. 2010: 904–11

    Abstract

    To develop methods to monitor prostate intrafraction motion during fixed-gantry intensity-modulated radiotherapy using MV treatment beam imaging together with minimal kV imaging for a failure detection strategy that ensures prompt detection when target displacement exceeds a preset threshold.Real-time two-dimensional (2D) marker position in the MV image plane was obtained by analyzing cine-MV images. The marker's in-line movement, and thus its time-varying three-dimensional (3D) position, was estimated by combining the 2D projection data with a previously established correlative relationship between the directional components of prostate motion. A confirmation request for more accurate localization using MV-kV triangulation was triggered when the estimated prostate displacement based on the cine-MV data was greater than 3 mm. An interventional action alert followed on positive MV-kV confirmation. To demonstrate the feasibility and accuracy of the proposed method, simulation studies of conventional-fraction intensity-modulated radiotherapy sessions were done using 536 Calypso-measured prostate trajectories from 17 radiotherapy patients.A technique for intrafraction prostate motion management has been developed. The technique, using "freely available" cine-MV images and minimum on-board kV imaging (on average 2.5 images/fraction), successfully limited 3D prostate movement to within a range of 3 mm relative to the MV beam for 99.4% of the total treatment time. On average, only approximately one intervention/fraction was needed to achieve this level of accuracy.Instead of seeking to accurately and continuously localize the prostate target as existing motion tracking systems do, the present technique effectively uses cine-MV data to provide a clinically valuable way to minimize kV usage, while maintaining high targeting accuracy.

    View details for DOI 10.1016/j.ijrobp.2009.12.068

    View details for Web of Science ID 000282731300037

    View details for PubMedID 20579818

  • Tomographic molecular imaging of x-ray-excitable nanoparticles OPTICS LETTERS Pratx, G., Carpenter, C. M., Sun, C., Rao, R. P., Xing, L. 2010; 35 (20): 3345-3347

    Abstract

    X-ray luminescence computed tomography (XLCT) is proposed as a new dual molecular/anatomical imaging modality. XLCT is based on the selective excitation and optical detection of x-ray-excitable nanoparticles. As a proof of concept, we built a prototype XLCT system and imaged near-IR-emitting Gd(2)O(2)S:Eu phosphors in various phantoms. Imaging in an optically diffusive medium shows that imaging performance is not affected by optical scatter; furthermore, the linear response of the reconstructed images suggests that XLCT is capable of quantitative imaging.

    View details for Web of Science ID 000283048100013

    View details for PubMedID 20967061

  • OPTIMIZED HYBRID MEGAVOLTAGE-KILOVOLTAGE IMAGING PROTOCOL FOR VOLUMETRIC PROSTATE ARC THERAPY 51st Annual Meeting of the American-Association-of-Physicists-in-Medicine Liu, W., Wiersma, R. D., Xing, L. ELSEVIER SCIENCE INC. 2010: 595–604

    Abstract

    To develop a real-time prostate position monitoring technique for modern arc radiotherapy through novel use of cine-megavoltage (MV) imaging, together with as-needed kilovoltage (kV) imaging.We divided the task of monitoring the intrafraction prostate motion into two steps for rotational deliveries: to detect potential target motion beyond a predefined threshold using MV images from different viewing angles by taking advantage of gantry rotation during arc therapy and to verify the displacement and determine whether intervention is needed using fiducial/tumor position information acquired from combined MV-kV imaging (by turning on the kV imager). A Varian Trilogy linear accelerator with an onboard kV imager was used to examine selected typical trajectories using a four-dimensional motion phantom. The performance of the algorithm was evaluated using phantom measurements and computer simulation for 536 Calypso-measured tracks from 17 patients.Fiducial displacement relative to the MV beam was limited to within a range of 3 mm 99.9% of the time with <1 mm accuracy. On average, only ∼0.5 intervention per arc delivery was needed to achieve this level of accuracy. Compared with other fluoroscopy-based tracking techniques, kV use was significantly reduced to an average of <15 times per arc delivery.By focusing the attention on detecting predefined abnormal motion (i.e., "failure" detection) and using the inherent mechanism of gantry rotation during arc radiotherapy, the current approach provides high confidence regarding the prostate position in real time without the unwanted overhead of continuous or periodic kV imaging.

    View details for DOI 10.1016/j.ijrobp.2009.11.056

    View details for Web of Science ID 000282147000041

    View details for PubMedID 20472354

    View details for PubMedCentralID PMC4131869

  • Clinical development of a failure detection-based online repositioning strategy for prostate IMRT-Experiments, simulation, and dosimetry study MEDICAL PHYSICS Liu, W., Qian, J., Hancock, S. L., Xing, L., Luxton, G. 2010; 37 (10): 5287-5297

    Abstract

    To implement and evaluate clinic-ready adaptive imaging protocols for online patient repositioning (motion tracking) during prostate IMRT using treatment beam imaging supplemented by minimal, as-needed use of on-board kV.The authors examine the two-step decision-making strategy: (1) Use cine-MV imaging and online-updated characterization of prostate motion to detect target motion that is potentially beyond a predefined threshold and (2) use paired MV-kV 3D localization to determine overthreshold displacement and, if needed, reposition the patient. Two levels of clinical implementation were evaluated: (1) Field-by-field based motion correction for present-day linacs and (2) instantaneous repositioning for new-generation linacs with capabilities of simultaneous MV-kV imaging and remote automatic couch control during treatment delivery. Experiments were performed on a Varian Trilogy linac in clinical mode using a 4D motion phantom programed with prostate motion trajectories taken from patient data. Dosimetric impact was examined using a 2D ion chamber array. Simulations were done for 536 trajectories from 17 patients.Despite the loss of marker detection efficiency caused by the MLC leaves sometimes obscuring the field at the marker's projected position on the MV imager, the field-by-field correction halved (from 23% to 10%) the mean percentage of time that target displacement exceeded a 3 mm threshold, as compared to no intervention. This was achieved at minimal cost in additional imaging (average of one MV-kV pair per two to three treatment fractions) and with a very small number of repositionings (once every four to five fractions). Also with low kV usage (approximation 2/fraction), the instantaneous repositioning approach reduced overthreshold time by more than 75% (23% to 5%) even with severe MLC blockage as often encountered in current IMRT and could reduce the overthreshold time tenfold (to < 2%) if the MLC blockage problem were relieved. The information acquired for repositioning using combined MV-kV images was found to have submillimeter accuracy.This work demonstrated with a current clinical setup that substantial reduction of adverse targeting effects of intrafraction prostate motion can be realized. The proposed adaptive imaging strategy incurs minimal imaging dose to the patient as compared to other stereoscopic imaging techniques.

    View details for DOI 10.1118/1.3488887

    View details for Web of Science ID 000283483700016

    View details for PubMedID 21089763

    View details for PubMedCentralID PMC2951998

  • Compressed sensing based cone-beam computed tomography reconstruction with a first-order method MEDICAL PHYSICS Choi, K., Wang, J., Zhu, L., Suh, T., Boyd, S., Xing, L. 2010; 37 (9): 5113-5125

    Abstract

    This article considers the problem of reconstructing cone-beam computed tomography (CBCT) images from a set of undersampled and potentially noisy projection measurements.The authors cast the reconstruction as a compressed sensing problem based on l1 norm minimization constrained by statistically weighted least-squares of CBCT projection data. For accurate modeling, the noise characteristics of the CBCT projection data are used to determine the relative importance of each projection measurement. To solve the compressed sensing problem, the authors employ a method minimizing total-variation norm, satisfying a prespecified level of measurement consistency using a first-order method developed by Nesterov.The method converges fast to the optimal solution without excessive memory requirement, thanks to the method of iterative forward and back-projections. The performance of the proposed algorithm is demonstrated through a series of digital and experimental phantom studies. It is found a that high quality CBCT image can be reconstructed from undersampled and potentially noisy projection data by using the proposed method. Both sparse sampling and decreasing x-ray tube current (i.e., noisy projection data) lead to the reduction of radiation dose in CBCT imaging.It is demonstrated that compressed sensing outperforms the traditional algorithm when dealing with sparse, and potentially noisy, CBCT projection views.

    View details for DOI 10.1118/1.3481510

    View details for Web of Science ID 000281906000063

    View details for PubMedID 20964231

    View details for PubMedCentralID PMC2945747

  • Hybrid x-ray/optical luminescence imaging: Characterization of experimental conditions MEDICAL PHYSICS Carpenter, C. M., Sun, C., Pratx, G., Rao, R., Xing, L. 2010; 37 (8): 4011-4018

    Abstract

    The feasibility of x-ray luminescence imaging is investigated using a dual-modality imaging system that merges x-ray and optical imaging. This modality utilizes x-ray activated nanophosphors that luminesce when excited by ionizing photons. By doping phosphors with lanthanides, which emit light in the visible and near infrared range, the luminescence is suitable for biological applications. This study examines practical aspects of this new modality including phosphor concentration, light emission linearity, detector damage, and spectral emission characteristics. Finally, the contrast produced by these phosphors is compared to that of x-ray fluoroscopy.Gadolinium and lanthanum oxysulfide phosphors doped with terbium (green emission) or europium (red emission) were studied. The light emission was imaged in a clinical x-ray scanner with a cooled CCD camera and a spectrophotometer; dose measurements were determined with a calibrated dosimeter. Using these properties, in addition to luminescence efficiency values found in the literature for a similar phosphor, minimum concentration calculations are performed. Finally, a 2.5 cm agar phantom with a 1 cm diameter cylindrical phosphor-filled inclusion (diluted at 10 mg/ml) is imaged to compare x-ray luminescence contrast with x-ray fluoroscopic contrast at a superficial location.Dose to the CCD camera in the chosen imaging geometry was measured at less than 0.02 cGy/s. Emitted light was found to be linear with dose (R(2)= 1) and concentration (R(2)= 1). Emission peaks for clinical x-ray energies are less than 3 nm full width at half maximum, as expected from lanthanide dopants. The minimum practical concentration necessary to detect luminescent phosphors is dependent on dose; it is estimated that subpicomolar concentrations are detectable at the surface of the tissue with typical mammographic doses, with the minimum detectable concentration increasing with depth and decreasing with dose. In a reflection geometry, x-ray luminescence had nearly a 430-fold greater contrast to background than x-ray fluoroscopy.X-ray luminescence has the potential to be a promising new modality for enabling molecular imaging within x-ray scanners. Although much work needs to be done to ensure biocompatibility of x-ray exciting phosphors, the benefits of this modality, highlighted in this work, encourage further study.

    View details for DOI 10.1118/1.3457332

    View details for Web of Science ID 000281112900011

    View details for PubMedID 20879562

    View details for PubMedCentralID PMC2917453

  • Dose reconstruction for volumetric modulated arc therapy (VMAT) using cone-beam CT and dynamic log files PHYSICS IN MEDICINE AND BIOLOGY Qian, J., Lee, L., Liu, W., Chu, K., Mok, E., Luxton, G., Le, Q., Xing, L. 2010; 55 (13): 3597-3610

    Abstract

    Volumetric modulated arc therapy (VMAT) has recently emerged as a new clinical modality for conformal radiation therapy. The aim of this work is to establish a methodology and procedure for retrospectively reconstructing the actual dose delivered in VMAT based on the pre-treatment cone-beam computed tomography (CBCT) and dynamic log files. CBCT was performed before the dose delivery and the system's log files were retrieved after the delivery. Actual delivery at a control point including MLC leaf positions, gantry angles and cumulative monitor units (MUs) was recorded in the log files and the information was extracted using in-house developed software. The extracted information was then embedded into the original treatment DICOM-radiation therapy (RT) file to replace the original control point parameters. This reconstituted DICOM-RT file was imported into the Eclipse treatment planning system (TPS) and dose was computed on the corresponding CBCT. A series of phantom experiments was performed to show the feasibility of dose reconstruction, validate the procedure and demonstrate the efficacy of this methodology. The resultant dose distributions and dose-volume histograms (DVHs) were compared with those of the original treatment plan. The studies indicated that CBCT-based VMAT dose reconstruction is readily achievable and provides a valuable tool for monitoring the dose actually delivered to the tumor target as well as the sensitive structures. In the absence of setup errors, the reconstructed dose shows no significant difference from the original pCT-based plan. It is also elucidated that the proposed method is capable of revealing the dosimetric changes in the presence of setup errors. The method reported here affords an objective means for dosimetric evaluation of VMAT delivery and is useful for adaptive VMAT in future.

    View details for DOI 10.1088/0031-9155/55/13/002

    View details for Web of Science ID 000279004300002

    View details for PubMedID 20526034

  • Fast and accurate marker-based projective registration method for uncalibrated transmission electron microscope tilt series PHYSICS IN MEDICINE AND BIOLOGY Lee, H., Lee, J., Shin, Y. G., Lee, R., Xing, L. 2010; 55 (12): 3417-3440

    Abstract

    This paper presents a fast and accurate marker-based automatic registration technique for aligning uncalibrated projections taken from a transmission electron microscope (TEM) with different tilt angles and orientations. Most of the existing TEM image alignment methods estimate the similarity between images using the projection model with least-squares metric and guess alignment parameters by computationally expensive nonlinear optimization schemes. Approaches based on the least-squares metric which is sensitive to outliers may cause misalignment since automatic tracking methods, though reliable, can produce a few incorrect trajectories due to a large number of marker points. To decrease the influence of outliers, we propose a robust similarity measure using the projection model with a Gaussian weighting function. This function is very effective in suppressing outliers that are far from correct trajectories and thus provides a more robust metric. In addition, we suggest a fast search strategy based on the non-gradient Powell's multidimensional optimization scheme to speed up optimization as only meaningful parameters are considered during iterative projection model estimation. Experimental results show that our method brings more accurate alignment with less computational cost compared to conventional automatic alignment methods.

    View details for DOI 10.1088/0031-9155/55/12/010

    View details for Web of Science ID 000278147800010

    View details for PubMedID 20508322

  • Image-based modeling of tumor shrinkage in head and neck radiation therapy 50th Annual Meeting of the American-Society-for-Therapeutic-Radiology-and-Oncology (ASTRO) Chao, M., Xie, Y., Moros, E. G., Le, Q., Xing, L. AMER ASSOC PHYSICISTS MEDICINE AMER INST PHYSICS. 2010: 2351–58

    Abstract

    Understanding the kinetics of tumor growth/shrinkage represents a critical step in quantitative assessment of therapeutics and realization of adaptive radiation therapy. This article presents a novel framework for image-based modeling of tumor change and demonstrates its performance with synthetic images and clinical cases.Due to significant tumor tissue content changes, similarity-based models are not suitable for describing the process of tumor volume changes. Under the hypothesis that tissue features in a tumor volume or at the boundary region are partially preserved, the kinetic change was modeled in two steps: (1) Autodetection of homologous tissue features shared by two input images using the scale invariance feature transformation (SIFT) method; and (2) establishment of a voxel-to-voxel correspondence between the images for the remaining spatial points by interpolation. The correctness of the tissue feature correspondence was assured by a bidirectional association procedure, where SIFT features were mapped from template to target images and reversely. A series of digital phantom experiments and five head and neck clinical cases were used to assess the performance of the proposed technique.The proposed technique can faithfully identify the known changes introduced when constructing the digital phantoms. The subsequent feature-guided thin plate spline calculation reproduced the "ground truth" with accuracy better than 1.5 mm. For the clinical cases, the new algorithm worked reliably for a volume change as large as 30%.An image-based tumor kinetic algorithm was developed to model the tumor response to radiation therapy. The technique provides a practical framework for future application in adaptive radiation therapy.

    View details for DOI 10.1118/1.3399872

    View details for Web of Science ID 000277242800043

    View details for PubMedID 20527569

    View details for PubMedCentralID PMC2874043

  • AMERICAN SOCIETY FOR THERAPEUTIC RADIOLOGY AND ONCOLOGY (ASTRO) AND AMERICAN COLLEGE OF RADIOLOGY (ACR) PRACTICE GUIDELINES FOR IMAGE-GUIDED RADIATION THERAPY (IGRT) INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS Potters, L., Gaspar, L. E., Kavanagh, B., Galvin, J. M., Hartford, A. C., Hevezi, J. M., Kupelian, P. A., Mohiden, N., Samuels, M. A., Timmerman, R., Tripuraneni, P., Vlachaki, M. T., Xing, L., Rosenthal, S. A. 2010; 76 (2): 319-325

    View details for DOI 10.1016/j.ijrobp.2009.09.041

    View details for Web of Science ID 000274121500001

    View details for PubMedID 20117284

  • A binary image reconstruction technique for accurate determination of the shape and location of metal objects in x-ray computed tomography JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY Wang, J., Xing, L. 2010; 18 (4): 403-414

    Abstract

    The presence of metals in patients causes streaking artifacts in X-ray CT and has been recognized as a problem that limits various applications of CT imaging. Accurate localization of metals in CT images is a critical step for metal artifacts reduction in CT imaging and many practical applications of CT images. The purpose of this work is to develop a method of auto-determination of the shape and location of metallic object(s) in the image space. The proposed method is based on the fact that when a metal object is present in a patient, a CT image can be divided into two prominent components: high density metal and low density normal tissues. This prior knowledge is incorporated into an objective function as the regularization term whose role is to encourage the solution to take a form of two intensity levels. A computer simulation study and four experimental studies are performed to evaluate the proposed approach. Both simulation and experimental studies show that the presented algorithm works well even in the presence of complicated shaped metal objects. For a hexagonally shaped metal embedded in a water phantom, for example, it is found that the accuracy of metal reconstruction is within sub-millimeter.

    View details for DOI 10.3233/XST-2010-0271

    View details for Web of Science ID 000283777900006

    View details for PubMedID 21045277

  • Metal Artifact Reduction in Computed Tomography by Constrained Optimization Conference on Medical Imaging - Physics of Medical Imaging Zhang, X., Wang, J., Xing, L. SPIE-INT SOC OPTICAL ENGINEERING. 2010

    View details for DOI 10.1117/12.844646

    View details for Web of Science ID 000285047200063

  • Accurate determination of the shape and location of metal objects in x-ray computed tomography Conference on Medical Imaging - Physics of Medical Imaging Wang, J., Xing, L. SPIE-INT SOC OPTICAL ENGINEERING. 2010

    View details for DOI 10.1117/12.844294

    View details for Web of Science ID 000285047200179

  • X-ray Activated Optical Contrast Agents for Simultaneous Anatomical/Functional CT Imaging 52nd Annual Meeting of the American-Society-for-Therapeutic-Radiation-Oncology (ASTRO) Pratx, G., Carpenter, C. M., Sun, C., Rao, R., Xing, L. ELSEVIER SCIENCE INC. 2010: S641–S642
  • BEAM'S-EYE-VIEW DOSIMETRICS-GUIDED INVERSE PLANNING FOR APERTURE-MODULATED ARC THERAPY INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS Ma, Y., Popple, R., Suh, T., Xing, L. 2009; 75 (5): 1587-1595

    Abstract

    To use angular beam's-eye-view dosimetrics (BEVD) information to improve the computational efficiency and plan quality of inverse planning of aperture-modulated arc therapy (AMAT).In BEVD-guided inverse planning, the angular space spanned by a rotational arc is represented by a large number of fixed-gantry beams with angular spacing of approximately 2.5 degrees. Each beam is assigned with an initial aperture shape determined by the beam's-eye-view (BEV) projection of the planning target volume (PTV) and an initial weight. Instead of setting the beam weights arbitrarily, which slows down the subsequent optimization process and may result in a suboptimal solution, a priori knowledge about the quality of the beam directions derived from a BEVD is adopted to initialize the weights. In the BEVD calculation, a higher score is assigned to directions that allow more dose to be delivered to the PTV without exceeding the dose tolerances of the organs at risk (OARs) and vice versa. Simulated annealing is then used to optimize the segment shapes and weights. The BEVD-guided inverse planning is demonstrated by using two clinical cases, and the results are compared with those of a conventional approach without BEVD guidance.An a priori knowledge-guided inverse planning scheme for AMAT is established. The inclusion of BEVD guidance significantly improves the convergence behavior of AMAT inverse planning and results in much better OAR sparing as compared with the conventional approach.BEVD-guidance facilitates AMAT treatment planning and provides a comprehensive tool to maximally use the technical capacity of the new arc therapeutic modality.

    View details for DOI 10.1016/j.ijrobp.2009.05.003

    View details for Web of Science ID 000272341800043

    View details for PubMedID 19733446

  • Predicting respiratory tumor motion with multi-dimensional adaptive filters and support vector regression PHYSICS IN MEDICINE AND BIOLOGY Riaz, N., Shanker, P., Wiersma, R., Gudmundsson, O., Mao, W., Widrow, B., Xing, L. 2009; 54 (19): 5735-5748

    Abstract

    Intra-fraction tumor tracking methods can improve radiation delivery during radiotherapy sessions. Image acquisition for tumor tracking and subsequent adjustment of the treatment beam with gating or beam tracking introduces time latency and necessitates predicting the future position of the tumor. This study evaluates the use of multi-dimensional linear adaptive filters and support vector regression to predict the motion of lung tumors tracked at 30 Hz. We expand on the prior work of other groups who have looked at adaptive filters by using a general framework of a multiple-input single-output (MISO) adaptive system that uses multiple correlated signals to predict the motion of a tumor. We compare the performance of these two novel methods to conventional methods like linear regression and single-input, single-output adaptive filters. At 400 ms latency the average root-mean-square-errors (RMSEs) for the 14 treatment sessions studied using no prediction, linear regression, single-output adaptive filter, MISO and support vector regression are 2.58, 1.60, 1.58, 1.71 and 1.26 mm, respectively. At 1 s, the RMSEs are 4.40, 2.61, 3.34, 2.66 and 1.93 mm, respectively. We find that support vector regression most accurately predicts the future tumor position of the methods studied and can provide a RMSE of less than 2 mm at 1 s latency. Also, a multi-dimensional adaptive filter framework provides improved performance over single-dimension adaptive filters. Work is underway to combine these two frameworks to improve performance.

    View details for DOI 10.1088/0031-9155/54/19/005

    View details for Web of Science ID 000270051600006

    View details for PubMedID 19729711

  • IMAGE-GUIDED RADIOTHERAPY IN NEAR REAL TIME WITH INTENSITY-MODULATED RADIOTHERAPY MEGAVOLTAGE TREATMENT BEAM IMAGING INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS Mao, W., Hsu, A., Riaz, N., Lee, L., Wiersma, R., Luxton, G., King, C., Xing, L., Solberg, T. 2009; 75 (2): 603-610

    Abstract

    To utilize image-guided radiotherapy (IGRT) in near real time by obtaining and evaluating the online positions of implanted fiducials from continuous electronic portal imaging device (EPID) imaging of prostate intensity-modulated radiotherapy (IMRT) delivery.Upon initial setup using two orthogonal images, the three-dimensional (3D) positions of all implanted fiducial markers are obtained, and their expected two-dimensional (2D) locations in the beam's-eye-view (BEV) projection are calculated for each treatment field. During IMRT beam delivery, EPID images of the megavoltage treatment beam are acquired in cine mode and subsequently analyzed to locate 2D locations of fiducials in the BEV. Simultaneously, 3D positions are estimated according to the current EPID image, information from the setup portal images, and images acquired at other gantry angles (the completed treatment fields). The measured 2D and 3D positions of each fiducial are compared with their expected 2D and 3D setup positions, respectively. Any displacements larger than a predefined tolerance may cause the treatment system to suspend the beam delivery and direct the therapists to reposition the patient.Phantom studies indicate that the accuracy of 2D BEV and 3D tracking are better than 1 mm and 1.4 mm, respectively. A total of 7330 images from prostate treatments were acquired and analyzed, showing a maximum 2D displacement of 6.7 mm and a maximum 3D displacement of 6.9 mm over 34 fractions.This EPID-based, real-time IGRT method can be implemented on any external beam machine with portal imaging capabilities without purchasing any additional equipment, and there is no extra dose delivered to the patient.

    View details for DOI 10.1016/j.ijrobp.2009.04.068

    View details for Web of Science ID 000269941600040

    View details for PubMedID 19735886

  • Pancreatic Tumor Motion on a Single Planning 4D-CT Does Not Correlate With Intrafraction Tumor Motion During Treatment AMERICAN JOURNAL OF CLINICAL ONCOLOGY-CANCER CLINICAL TRIALS Minn, A. Y., Schellenberg, D., Maxim, P., Suh, Y., McKenna, S., Cox, B., Dieterich, S., Xing, L., Graves, E., Goodman, K. A., Chang, D., Koong, A. C. 2009; 32 (4): 364-368

    Abstract

    To quantify pancreas tumor motion on both a planning 4D-CT and during a single fraction treatment using the CyberKnife linear accelerator and Synchrony respiratory tracking software, and to investigate whether a single 4D-CT study is reliable for determining radiation treatment margins for patients with locally advanced pancreas cancer.Twenty patients underwent fiducial placement, biphasic pancreatic protocol CT scan and 4D-CT scan in the treatment position while free-breathing. Patients were then treated with a single 25 Gy fraction of stereotactic body radiotherapy. Predicted pancreas motion in the superior-inferior (SI), left-right (LR), and anterior-posterior (AP) directions was calculated from the maximum inspiration and maximum expiration 4D-CT scan. For CyberKnife treatments, mean respiratory cycle motion and maximum respiratory cycle motion was determined in the SI, LR, and AP directions.The range of centroid movement based on 4D-CT in the SI, LR, and AP directions were 0.9 to 28.8 mm, 0.1 to 13.7 mm, and 0.2 to 7.6 mm, respectively. During CyberKnife treatment, in the SI direction, the mean motion of the centroid ranged from 0.5 to 12.7 mm. In the LR direction, the mean motion range was 0.4 to 9.4 mm. In the AP direction, the mean motion range was 0.6 to 5.5 mm. The maximum range of movement (mean) during CyberKnife treatment in the SI, LR, and AP directions were 4.5 to 48.8 mm (mean 20.8 mm), 1.5 to 41.3 mm (mean 11.3 mm), and 1.6 to 68.1 mm (mean 13.4 mm), respectively. Neither the maximum or mean motion correlated with the 4D-CT movement.There is substantial respiratory associated motion of pancreatic tumors. The 4D-CT planning scans cannot accurately predict the movement of pancreatic tumors during actual treatment on CyberKnife.

    View details for DOI 10.1097/COC.0b013e31818da9e0

    View details for PubMedID 19398901

  • TISSUE FEATURE-BASED AND SEGMENTED DEFORMABLE IMAGE REGISTRATION FOR IMPROVED MODELING OF SHEAR MOVEMENT OF LUNGS INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS Xie, Y., Chao, M., Xing, L. 2009; 74 (4): 1256-1265

    Abstract

    To report a tissue feature-based image registration strategy with explicit inclusion of the differential motions of thoracic structures.The proposed technique started with auto-identification of a number of corresponding points with distinct tissue features. The tissue feature points were found by using the scale-invariant feature transform method. The control point pairs were then sorted into different "colors" according to the organs in which they resided and used to model the involved organs individually. A thin-plate spline method was used to register a structure characterized by the control points with a given "color." The proposed technique was applied to study a digital phantom case and 3 lung and 3 liver cancer patients.For the phantom case, a comparison with the conventional thin-plate spline method showed that the registration accuracy was markedly improved when the differential motions of the lung and chest wall were taken into account. On average, the registration error and standard deviation of the 15 points against the known ground truth were reduced from 3.0 to 0.5 mm and from 1.5 to 0.2 mm, respectively, when the new method was used. A similar level of improvement was achieved for the clinical cases.The results of our study have shown that the segmented deformable approach provides a natural and logical solution to model the discontinuous organ motions and greatly improves the accuracy and robustness of deformable registration.

    View details for DOI 10.1016/j.ijrobp.2009.02.023

    View details for Web of Science ID 000267505000040

    View details for PubMedID 1