Yong Yang

All Publications

• 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

• 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

• 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

• 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

• An overview of artificial intelligence in medical physics and radiation oncology JOURNAL OF THE NATIONAL CANCER CENTER Liu, J., Xiao, H., Fan, J., Hu, W., Yang, Y., Dong, P., Xing, L., Cai, J. 2023; 3 (3): 211-221

  View details for DOI 10.1016/j.jncc.2023.08.002

  View details for Web of Science ID 001087134800001

• An overview of artificial intelligence in medical physics and radiation oncology. Journal of the National Cancer Center Liu, J., Xiao, H., Fan, J., Hu, W., Yang, Y., Dong, P., Xing, L., Cai, J. 2023; 3 (3): 211-221

  Abstract

  Artificial intelligence (AI) is developing rapidly and has found widespread applications in medicine, especially radiotherapy. This paper provides a brief overview of AI applications in radiotherapy, and highlights the research directions of AI that can potentially make significant impacts and relevant ongoing research works in these directions. Challenging issues related to the clinical applications of AI, such as robustness and interpretability of AI models, are also discussed. The future research directions of AI in the field of medical physics and radiotherapy are highlighted.

  View details for DOI 10.1016/j.jncc.2023.08.002

  View details for PubMedID 39035195

  View details for PubMedCentralID PMC11256546

• 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

• 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

• 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

• 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

• 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

  View details for DOI 10.1109/TRPMS.2021.3098507

  View details for Web of Science ID 000750257400011

• The Stanford VMAT TBI Technique. Practical radiation oncology Kovalchuk, N., Simiele, E., Skinner, L., Yang, Y., Howell, N., Lewis, J., Hui, C., Blomain, E. S., Hoppe, R. T., Hiniker, S. M. 2022

  Abstract

  In this work, we describe the technical aspects of the XXX VMAT TBI technique, compare it to other VMAT TBI techniques, and share our initial experience.From September 2019 to August 2021, 35 patients were treated with VMAT TBI at our institution. Treatment planning was performed using in-house developed automated planning scripts. Organ sparing depended on the regimen: myeloablative (lungs, kidneys, and lenses); non-myeloablative with benign disease (lungs, kidneys, lenses, gonads, brain, and thyroid). Quality assurance was performed using EPID portal dosimetry and Mobius3D. Robustness was evaluated for the first ten patients by performing local and global isocenter shifts of 5 mm. Treatment was delivered using IGRT for every isocenter and every fraction. In-vivo measurements were performed on the matchline between the VMAT and AP/PA fields and on the testes for the first fraction.The lungs, lungs-1cm, and kidneys Dmean were consistently spared to 57.6±4.4%, 40.7±5.5%, and 70.0±9.9% of the prescription dose, respectively. Gonadal sparing (Dmean=0.69±0.13 Gy) was performed for all patients with benign disease. The average PTV D1cc was 120.7±6.4% for all patients. The average Gamma passing rate for the VMAT plans was 98.1±1.6% (criteria of 3%/2mm). Minimal differences were observed between Mobius3D- and EclipseAAA-calculated PTV Dmean (0.0±0.3%) and lungs Dmean (-2.5±1.2%). Robustness evaluation showed that the PTV Dmax and lungs Dmean are insensitive to small positioning deviations between the VMAT isocenters (1.1±2.4% and 1.2±1.0%, respectively). The average matchline dose measurement indicated patient setup was reproducible (96.1±4.5% relative to prescription dose). Treatment time, including patient setup and beam-on, was 47.5±9.5 min.The XXX VMAT TBI technique, from simulation to treatment delivery, was presented and compared to other VMAT TBI techniques. Together with publicly shared autoplanning scripts, our technique may provide the gateway for wider adaptation of this technology and the possibility of multi-institutional studies in the cooperative group setting.

  View details for DOI 10.1016/j.prro.2021.12.007

  View details for PubMedID 35182803

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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)

  View details for DOI 10.1088/1361-6560/ab2146

  View details for Web of Science ID 000518929400007

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 4D VMAT planning and verification technique for dynamic tracking using a direct aperture deformation (DAD) method JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS Zhang, Y., Yang, Y., Fu, W., Li, X., Li, T., Heron, D. E., Huq, M. S. 2017; 18 (2): 50-61

  Abstract

  We developed a four-dimensional volumetric modulated arc therapy (4D VMAT) planning technique for moving targets using a direct aperture deformation (DAD) method and investigated its feasibility for clinical use. A 3D VMAT plan was generated on a reference phase of a 4D CT dataset. The plan was composed of a set of control points including the beam angle, MLC apertures and weights. To generate the 4D VMAT plan, these control points were assigned to the closest respiratory phases using the temporal information of the gantry angle and respiratory curve. Then, a DAD algorithm was used to deform the beam apertures at each control point to the corresponding phase to compensate for the tumor motion and shape changes. Plans for a phantom and five lung cases were included in this study to evaluate the proposed technique. Dosimetric comparisons were performed between 4D and 3D VMAT plans. Plan verification was implemented by delivering the 4D VMAT plans on a moving QUASAR™ phantom driven with patient-specific respiratory curves. The phantom study showed that the 4D VMAT plan generated with the DAD method was comparable to the ideal 3D VMAT plan. DVH comparisons indicated that the planning target volume (PTV) coverages and minimum doses were nearly invariant, and no significant difference in lung dosimetry was observed. Patient studies revealed that the GTV coverage was nearly the same; although the PTV coverage dropped from 98.8% to 94.7%, and the mean dose decreased from 64.3 to 63.8 Gy on average. For the verification measurements, the average gamma index pass rate was 98.6% and 96.5% for phantom 3D and 4D VMAT plans with 3%/3 mm criteria. For patient plans, the average gamma pass rate was 96.5% (range 94.5-98.5%) and 95.2% (range 94.1-96.1%) for 3D and 4D VMAT plans. The proposed 4D VMAT planning technique using the DAD method is feasible to incorporate the intra-fraction organ motion and shape change into a 4D VMAT planning. It has great potential to provide high plan quality and delivery efficiency for moving targets.

  View details for DOI 10.1002/acm2.12053

  View details for Web of Science ID 000397498300008

  View details for PubMedID 28300367

• 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

• Evaluation of on-board kV cone beam CT (CBCT)-based dose calculation PHYSICS IN MEDICINE AND BIOLOGY Yang, Y., Schreibmann, E., Li, T., Wang, C., Xing, L. 2007; 52 (3): 685-705

  Abstract

  On-board CBCT images are used to generate patient geometric models to assist patient setup. The image data can also, potentially, be used for dose reconstruction in combination with the fluence maps from treatment plan. Here we evaluate the achievable accuracy in using a kV CBCT for dose calculation. Relative electron density as a function of HU was obtained for both planning CT (pCT) and CBCT using a Catphan-600 calibration phantom. The CBCT calibration stability was monitored weekly for 8 consecutive weeks. A clinical treatment planning system was employed for pCT- and CBCT-based dose calculations and subsequent comparisons. Phantom and patient studies were carried out. In the former study, both Catphan-600 and pelvic phantoms were employed to evaluate the dosimetric performance of the full-fan and half-fan scanning modes. To evaluate the dosimetric influence of motion artefacts commonly seen in CBCT images, the Catphan-600 phantom was scanned with and without cyclic motion using the pCT and CBCT scanners. The doses computed based on the four sets of CT images (pCT and CBCT with/without motion) were compared quantitatively. The patient studies included a lung case and three prostate cases. The lung case was employed to further assess the adverse effect of intra-scan organ motion. Unlike the phantom study, the pCT of a patient is generally acquired at the time of simulation and the anatomy may be different from that of CBCT acquired at the time of treatment delivery because of organ deformation. To tackle the problem, we introduced a set of modified CBCT images (mCBCT) for each patient, which possesses the geometric information of the CBCT but the electronic density distribution mapped from the pCT with the help of a BSpline deformable image registration software. In the patient study, the dose computed with the mCBCT was used as a surrogate of the 'ground truth'. We found that the CBCT electron density calibration curve differs moderately from that of pCT. No significant fluctuation was observed in the calibration over the period of 8 weeks. For the static phantom, the doses computed based on pCT and CBCT agreed to within 1%. A notable difference in CBCT- and pCT-based dose distributions was found for the motion phantom due to the motion artefacts which appeared in the CBCT images (the maximum discrepancy was found to be approximately 3.0% in the high dose region). The motion artefacts-induced dosimetric inaccuracy was also observed in the lung patient study. For the prostate cases, the mCBCT- and CBCT-based dose calculations yielded very close results (<2%). Coupled with the phantom data, it is concluded that the CBCT can be employed directly for dose calculation for a disease site such as the prostate, where there is little motion artefact. In the prostate case study, we also noted a large discrepancy between the original treatment plan and the CBCT (or mCBCT)-based calculation, suggesting the importance of inter-fractional organ movement and the need for adaptive therapy to compensate for the anatomical changes in the future.

  View details for DOI 10.1088/0031-9155/52/3/011

  View details for Web of Science ID 000243684600011

  View details for PubMedID 17228114

• Four-dimensional cone-beam computed tomography using an on-board imager MEDICAL PHYSICS Li, T., Xing, L., Munro, P., McGuinness, C., Chao, M., Yang, Y., Loo, B., Koong, A. 2006; 33 (10): 3825-3833

  Abstract

  On-board cone-beam computed tomography (CBCT) has recently become available to provide volumetric information of a patient in the treatment position, and holds promises for improved target localization and irradiation dose verification. The design of currently available on-board CBCT, however, is far from optimal. Its quality is adversely influenced by many factors, such as scatter, beam hardening, and intra-scanning organ motion. In this work we quantitatively study the influence of organ motion on CBCT imaging and investigate a strategy to acquire high quality phase-resolved [four-dimensional (4D)] CBCT images based on phase binning of the CBCT projection data. An efficient and robust method for binning CBCT data according to the patient's respiratory phase derived in the projection space was developed. The phase-binned projections were reconstructed using the conventional Feldkamp algorithm to yield 4D CBCT images. Both phantom and patient studies were carried out to validate the technique and to optimize the 4D CBCT data acquisition protocol. Several factors that are important to the clinical implementation of the technique, such as the image quality, scanning time, number of projections, and radiation dose, were analyzed for various scanning schemes. The general references drawn from this study are: (i) reliable phase binning of CBCT projections is accomplishable with the aid of external or internal marker and simple analysis of its trace in the projection space, and (ii) artifact-free 4D CBCT images can be obtained without increasing the patient radiation dose as compared to the current 3D CBCT scan.

  View details for DOI 10.1118/1.2349692

  View details for Web of Science ID 000241424100024

  View details for PubMedID 17089847

• Overview of image-guided radiation therapy MEDICAL DOSIMETRY Xing, L., Thorndyke, B., Schreibmann, E., Yang, Y., Li, T., Kim, G., Luxton, G., Koong, A. 2006; 31 (2): 91-112

  Abstract

  Radiation therapy has gone through a series of revolutions in the last few decades and it is now possible to produce highly conformal radiation dose distribution by using techniques such as intensity-modulated radiation therapy (IMRT). The improved dose conformity and steep dose gradients have necessitated enhanced patient localization and beam targeting techniques for radiotherapy treatments. Components affecting the reproducibility of target position during and between subsequent fractions of radiation therapy include the displacement of internal organs between fractions and internal organ motion within a fraction. Image-guided radiation therapy (IGRT) uses advanced imaging technology to better define the tumor target and is the key to reducing and ultimately eliminating the uncertainties. The purpose of this article is to summarize recent advancements in IGRT and discussed various practical issues related to the implementation of the new imaging techniques available to radiation oncology community. We introduce various new IGRT concepts and approaches, and hope to provide the reader with a comprehensive understanding of the emerging clinical IGRT technologies. Some important research topics will also be addressed.

  View details for DOI 10.1016/j.meddos.2005.12.004

  View details for Web of Science ID 000237818000002

  View details for PubMedID 16690451

• Model-based image reconstruction for four-dimensional PET MEDICAL PHYSICS Li, T., Thorndyke, B., Schreibmann, E., Yang, Y., Xing, L. 2006; 33 (5): 1288-1298

  Abstract

  Positron emission tonography (PET) is useful in diagnosis and radiation treatment planning for a variety of cancers. For patients with cancers in thoracic or upper abdominal region, the respiratory motion produces large distortions in the tumor shape and size, affecting the accuracy in both diagnosis and treatment. Four-dimensional (4D) (gated) PET aims to reduce the motion artifacts and to provide accurate measurement of the tumor volume and the tracer concentration. A major issue in 4D PET is the lack of statistics. Since the collected photons are divided into several frames in the 4D PET scan, the quality of each reconstructed frame degrades as the number of frames increases. The increased noise in each frame heavily degrades the quantitative accuracy of the PET imaging. In this work, we propose a method to enhance the performance of 4D PET by developing a new technique of 4D PET reconstruction with incorporation of an organ motion model derived from 4D-CT images. The method is based on the well-known maximum-likelihood expectation-maximization (ML-EM) algorithm. During the processes of forward- and backward-projection in the ML-EM iterations, all projection data acquired at different phases are combined together to update the emission map with the aid of deformable model, the statistics is therefore greatly improved. The proposed algorithm was first evaluated with computer simulations using a mathematical dynamic phantom. Experiment with a moving physical phantom was then carried out to demonstrate the accuracy of the proposed method and the increase of signal-to-noise ratio over three-dimensional PET. Finally, the 4D PET reconstruction was applied to a patient case.

  View details for DOI 10.1118/1.2192581

  View details for Web of Science ID 000237673600012

  View details for PubMedID 16752564

• Optimization of radiotherapy dose-time fractionation with consideration of tumor specific biology MEDICAL PHYSICS Yang, Y., Xing, L. 2005; 32 (12): 3666-3677

  Abstract

  The "four Rs" of radiobiology play an important role in the design of radiation therapy treatment protocol. The purpose of this work is to explore their influence on external beam radiotherapy for fast and slowly proliferating tumors and develop an optimization framework for tumor-biology specific dose-time-fractionation scheme. The linear quadratic model is used to describe radiation response of tumor, in which the time dependence of sublethal damage repair and the redistribution and reoxygenation effects are included. The optimum radiotherapeutic strategy is defined as the treatment scheme that maximizes tumor biologically effective dose (BED) while keeping normal tissue BED constant. The influence of different model parameters on total dose, overall treatment time, fraction size, and intervals is also studied. The results showed that, for fast proliferating tumors, the optimum overall time is similar to the assumed kickoff time T(k) and almost independent of interval patterns. Significant increase in tumor control can be achieved using accelerated schemes for the tumors with doubling time smaller than 3 days, but little is gained for those with doubling time greater than 5 days. The incomplete repair of normal tissues between two consecutive fractions in standard fractionation has almost no influence on the fractional doses, even for the hyperfractionation with an interval time of 8 h. However, when the resensitization effect is included, the fractional doses at the beginning and end of each irradiated week become obviously higher than others in the optimum scheme and the hyperfractionation scheme has little advantage over the standard or hypofractionation one. For slowly proliferating tumors, provided that the alpha/beta ratio of the tumor is comparable to that of the normal tissues, a hypofractionation is more favorable. The overall treatment time should be larger than a minimum, which is predominantly determined by the resensitization time. The proposed technique provides a useful tool to systematically optimize radiotherapy for fast and slow proliferating tumors and sheds important insight into the complex problem of dose-time fractionation.

  View details for DOI 10.1118/1.2126167

  View details for Web of Science ID 000234643700018

  View details for PubMedID 16475766

• Towards biologically conformal radiation therapy (BCRT): Selective IMRT dose escalation under the guidance of spatial biology distribution MEDICAL PHYSICS Yang, Y., Xing, L. 2005; 32 (6): 1473-1484

  Abstract

  It is well known that the spatial biology distribution (e.g., clonogen density, radiosensitivity, tumor proliferation rate, functional importance) in most tumors and sensitive structures is heterogeneous. Recent progress in biological imaging is making the mapping of this distribution increasingly possible. The purpose of this work is to establish a theoretical framework to quantitatively incorporate the spatial biology data into intensity modulated radiation therapy (IMRT) inverse planning. In order to implement this, we first derive a general formula for determining the desired dose to each tumor voxel for a known biology distribution of the tumor based on a linear-quadratic model. The desired target dose distribution is then used as the prescription for inverse planning. An objective function with the voxel-dependent prescription is constructed with incorporation of the nonuniform dose prescription. The functional unit density distribution in a sensitive structure is also considered phenomenologically when constructing the objective function. Two cases with different hypothetical biology distributions are used to illustrate the new inverse planning formalism. For comparison, treatments with a few uniform dose prescriptions and a simultaneous integrated boost are also planned. The biological indices, tumor control probability (TCP) and normal tissue complication probability (NTCP), are calculated for both types of plans and the superiority of the proposed technique over the conventional dose escalation scheme is demonstrated. Our calculations revealed that it is technically feasible to produce deliberately nonuniform dose distributions with consideration of biological information. Compared with the conventional dose escalation schemes, the new technique is capable of generating biologically conformal IMRT plans that significantly improve the TCP while reducing or keeping the NTCPs at their current levels. Biologically conformal radiation therapy (BCRT) incorporates patient-specific biological information and provides an outstanding opportunity for us to truly individualize radiation treatment. The proposed formalism lays a technical foundation for BCRT and allows us to maximally exploit the technical capacity of IMRT to more intelligently escalate the radiation dose.

  View details for Web of Science ID 000229908600004

  View details for PubMedID 16013703

• Measurement of ionizing radiation using carbon nanotube field effect transistor PHYSICS IN MEDICINE AND BIOLOGY Tang, X. W., Yang, Y., Kim, W., Wang, Q., Qi, P. F., Dai, H. J., Xing, L. 2005; 50 (3): N23-N31

  Abstract

  Single-walled carbon nanotubes (SWNTs) are a new class of highly promising nanomaterials for future nano-electronics. Here, we present an initial investigation of the feasibility of using SWNT field effect transistors (SWNT-FETs) formed on silicon-oxide substrates and suspended FETs for radiation dosimetry applications. Electrical measurements and atomic force microscopy (AFM) revealed the intactness of SWNT-FET devices after exposure to over 1 Gy of 6 MV therapeutic x-rays. The sensitivity of SWNT-FET devices to x-ray irradiation is elucidated by real-time dose monitoring experiments and accumulated dose reading based on threshold voltage shift. SWNT-FET devices exhibit sensitivities to x-rays that are at least comparable to or orders of magnitude higher than commercial MOSFET (metal-oxide semiconductor field effect transistor) dosimeters and could find applications as miniature dosimeters for microbeam profiling and implantation.

  View details for DOI 10.1088/0031-9155/50/3/N02

  View details for PubMedID 15773731

• Clinical knowledge-based inverse treatment planning PHYSICS IN MEDICINE AND BIOLOGY Yang, Y., Xing, L. 2004; 49 (22): 5101-5117

  Abstract

  Clinical IMRT treatment plans are currently made using dose-based optimization algorithms, which do not consider the nonlinear dose-volume effects for tumours and normal structures. The choice of structure specific importance factors represents an additional degree of freedom of the system and makes rigorous optimization intractable. The purpose of this work is to circumvent the two problems by developing a biologically more sensible yet clinically practical inverse planning framework. To implement this, the dose-volume status of a structure was characterized by using the effective volume in the voxel domain. A new objective function was constructed with the incorporation of the volumetric information of the system so that the figure of merit of a given IMRT plan depends not only on the dose deviation from the desired distribution but also the dose-volume status of the involved organs. The conventional importance factor of an organ was written into a product of two components: (i) a generic importance that parametrizes the relative importance of the organs in the ideal situation when the goals for all the organs are met; (ii) a dose-dependent factor that quantifies our level of clinical/dosimetric satisfaction for a given plan. The generic importance can be determined a priori, and in most circumstances, does not need adjustment, whereas the second one, which is responsible for the intractable behaviour of the trade-off seen in conventional inverse planning, was determined automatically. An inverse planning module based on the proposed formalism was implemented and applied to a prostate case and a head-neck case. A comparison with the conventional inverse planning technique indicated that, for the same target dose coverage, the critical structure sparing was substantially improved for both cases. The incorporation of clinical knowledge allows us to obtain better IMRT plans and makes it possible to auto-select the importance factors, greatly facilitating the inverse planning process. The new formalism proposed also reveals the relationship between different inverse planning schemes and gives important insight into the problem of therapeutic plan optimization. In particular, we show that the EUD-based optimization is a special case of the general inverse planning formalism described in this paper.

  View details for DOI 10.1088/0031-9155/49/22/006

  View details for Web of Science ID 000225629200006

  View details for PubMedID 15609561

• Inverse treatment planning with adaptively evolving voxel-dependent penalty scheme MEDICAL PHYSICS Yong, Y., Lei, X. 2004; 31 (10): 2839-2844

  Abstract

  In current inverse planning algorithms it is common to treat all voxels within a target or sensitive structure equally and use structure specific prescriptions and weighting factors as system parameters. In reality, the voxels within a structure are not identical in complying with their dosimetric goals and there exists strong intrastructural competition. Inverse planning objective function should not only balance the competing objectives of different structures but also that of the individual voxels in various structures. In this work we propose to model the intrastructural tradeoff through the modulation of voxel-dependent importance factors and deal with the challenging problem of how to obtain a sensible set of importance factors with a manageable amount of computing. Instead of letting the values of voxel-dependent importance to vary freely during the search process, an adaptive algorithm, in which the importance factors were tied to the local radiation doses through a heuristically constructed relation, was developed. It is shown that the approach is quite general and the EUD-based optimization is a special case of the proposed framework. The new planning tool was applied to study a hypothetical phantom case and a prostate case. Comparison of the results with that obtained using conventional inverse planning technique with structure specific importance factors indicated that the dose distributions from the conventional inverse planning are at best suboptimal and can be significantly improved with the help of the proposed nonuniform penalty scheme.

  View details for DOI 10.1118/1.1799311

  View details for Web of Science ID 000224743200017

  View details for PubMedID 15543792

• Quantitative measurement of MLC leaf displacements using an electronic portal image device 45th Annual Meeting of the American-Society-for-Therapeutic-Radiology-and-Oncology (ASTRO) Yang, Y., Xing, L. IOP PUBLISHING LTD. 2004: 1521–33

  Abstract

  The success of an IMRT treatment relies on the positioning accuracy of the MLC (multileaf collimator) leaves for both step-and-shoot and dynamic deliveries. In practice, however, there exists no effective and quantitative means for routine MLC QA and this has become one of the bottleneck problems in IMRT implementation. In this work we present an electronic portal image device (EPID) based method for fast and accurate measurement of MLC leaf positions at arbitrary locations within the 40 cm x 40 cm radiation field. The new technique utilizes the fact that the integral signal in a small region of interest (ROI) is a sensitive and reliable indicator of the leaf displacement. In this approach, the integral signal at a ROI was expressed as a weighted sum of the contributions from the displacements of the leaf above the point and the adjacent leaves. The weighting factors or linear coefficients of the system equations were determined by fitting the integral signal data for a group of pre-designed MLC leaf sequences to the known leaf displacements that were intentionally introduced during the creation of the leaf sequences. Once the calibration is done, the system can be used for routine MLC leaf positioning QA to detect possible leaf errors. A series of tests was carried out to examine the functionality and accuracy of the technique. Our results show that the proposed technique is potentially superior to the conventional edge-detecting approach in two aspects: (i) it deals with the problem in a systematic approach and allows us to take into account the influence of the adjacent MLC leaves effectively; and (ii) it may improve the signal-to-noise ratio and is thus capable of quantitatively measuring extremely small leaf positional displacements. Our results indicate that the technique can detect a leaf positional error as small as 0.1 mm at an arbitrary point within the field in the absence of EPID set-up error and 0.3 mm when the uncertainty is considered. Given its simplicity, efficiency and accuracy, we believe that the technique is ideally suitable for routine MLC leaf positioning QA.

  View details for DOI 10.1088/0031-9155/49/8/010

  View details for Web of Science ID 000221250800010

  View details for PubMedID 15152689

• Incorporating leaf transmission and head scatter corrections into step-and-shoot leaf sequences for IMRT INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS Yang, Y., Xing, L. 2003; 55 (4): 1121-1134

  Abstract

  Leaf transmission and head scatter are two important factors that influence intensity-modulated radiation therapy (IMRT) delivery and should be correctly taken into account when generating multileaf collimator (MLC) sequences. Significant discrepancies between the desired and delivered intensity profiles could otherwise result. The purpose of this article is to propose a reliable algorithm to minimize the dosimetric effects caused by the two factors in step-and-shoot mode.The goal of the algorithm is to minimize the difference between the desired fluence map and the fluence map actually delivered. For this purpose, an error function, defined as the least-square difference between the desired and the delivered fluence maps, is introduced. The effects of transmission and head scatter are minimized by adjusting the fractional monitor units (MUs) in the initial MLC sequences, created by using the desired fluence map without inclusion of the contributions from the two factors. Computationally, a downhill simplex optimization method is used to minimize the error function with respect to the fractional MUs. A three-source model is used to evaluate the relative head scatter distribution for each segment at the beginning of the calculation. The algorithm has been assessed by comparing the dose distributions delivered by the corrected leaf sequence files and the theoretic predication, calculated by Monte Carlo simulation using the desired fluence maps, for an intuitive test field and several clinical IMRT cases.The deviations between the desired fluence maps and those calculated using the corrected leaf sequence files are <0.3% of the maximum MU for the test field and <1.0% for the clinical IMRT cases. The experimental data show that both absolute and relative dose distributions delivered by the corrected leaf sequences agree with the desired ones within 2.5% of the maximum dose or 2 mm in high-dose gradient regions. Compared with the results obtained by using the leaf sequences in which only the transmission or none of the two effects is corrected, significant improvements in the fluence and dose distributions have been observed.Transmission and head scatter play important roles in the dosimetric behavior of IMRT delivery. A larger error may result if only one factor is considered because of the opposite effects of the two factors. We noted that the influence of the two effects is more pronounced in absolute dose than in the relative dose. The algorithm proposed in this work accurately corrects for these two effects in step-and-shoot delivery and provides a reliable tool for clinical IMRT application.

  View details for DOI 10.1016/S0360-3016(02)04417-6

  View details for Web of Science ID 000181269600031

  View details for PubMedID 12605992

• Using the volumetric effect of a finite-sized detector for routine quality assurance of multileaf collimator leaf positioning MEDICAL PHYSICS Yang, Y., Xing, L. 2003; 30 (3): 433-441

  Abstract

  Intensity modulated radiation therapy (IMRT) is an advanced form of radiation therapy and promises to improve dose conformation while reducing the irradiation to the sensitive structures. The modality is, however, more complicated than conventional treatment and requires much more stringent quality assurance (QA) to ensure what has been planned can be achieved accurately. One of the main QA tasks is the assurance of positioning accuracy of multileaf collimator (MLC) leaves during IMRT delivery. Currently, the routine quality assurance of MLC in most clinics isbeing done using radiographic films with specially designed MLC leaf sequences. Besides being time consuming, the results of film measurements are difficult to quantify and interpret. In this work, we propose a new and effective technique for routine MLC leaf positioning QA. The technique utilizes the fact that, when a finite-sized detector is placed under a leaf, the relative output of the detector will depend on the relative fractional volume irradiated. A small error in leaf positioning would change the fractional volume irradiated and lead to a deviation of the relative output from the normal reading. For a given MLC and detector system, the relation between the relative output and the leaf displacement can be easily established through experimental measurements and used subsequently as a quantitative means for detecting possible leaf positional errors. The method was tested using a linear accelerator with an 80-leaf MLC. Three different locations, including two locations on central plane (X1 = X2 = 0) and one point on an off-central plane location (X1 = -7.5, X = 7.5), were studied. Our results indicated that the method could accurately detect a leaf positional change of approximately 0.1 mm. The method was also used to monitor the stability of MLC leaf positioning for five consecutive weeks. In this test, we intentionally introduced two positional errors in the testing MLC leaf sequences: -0.2 mm and 1.2 mm. The technique was found to be robust and could detect the positional inaccuracy in each week's test. The influence of other possible error sources, including the ion chamber placement, jaw settings, gantry and collimator angle read-outs, and the positioning errors of the adjacent leaves, on detection accuracy were also investigated. The principle of our method is independent of the types of the MLC and the detector and may have significant practical implications in facilitating routine MLC QA for IMRT delivery.

  View details for DOI 10.1118/1.1543150

  View details for Web of Science ID 000181587500019

  View details for PubMedID 12674244

• A three-source model for the calculation of head scatter factors MEDICAL PHYSICS Yang, Y., Xing, L., Boyer, A. L., Song, Y. X., Hu, Y. M. 2002; 29 (9): 2024-2033

  Abstract

  Accurate determination of the head scatter factor Sc is an important issue, especially for intensity modulated radiation therapy, where the segmented fields are often very irregular and much less than the collimator jaw settings. In this work, we report an Sc calculation algorithm for symmetric, asymmetric, and irregular open fields shaped by the tertiary collimator (a multileaf collimator or blocks) at different source-to-chamber distance. The algorithm was based on a three-source model, in which the photon radiation to the point of calculation was treated as if it originated from three effective sources: one source for the primary photons from the target and two extra-focal photon sources for the scattered photons from the primary collimator and the flattening filter, respectively. The field mapping method proposed by Kim et al. [Phys. Med. Biol. 43, 1593-1604 (1998)] was extended to two extra-focal source planes and the scatter contributions were integrated over the projected areas (determined by the detector's eye view) in the three source planes considering the source intensity distributions. The algorithm was implemented using Microsoft Visual C/C++ in the MS Windows environment. The only input data required were head scatter factors for symmetric square fields, which are normally acquired during machine commissioning. A large number of different fields were used to evaluate the algorithm and the results were compared with measurements. We found that most of the calculated Sc's agreed with the measured values to within 0.4%. The algorithm can also be easily applied to deal with irregular fields shaped by a multileaf collimator that replaces the upper or lower collimator jaws.

  View details for DOI 10.1118/1.1500767

  View details for Web of Science ID 000178093000010

  View details for PubMedID 12349923