Bin Han

All Publications

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

  Abstract

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

  View details for DOI 10.1016/j.ijrobp.2024.07.2149

  View details for PubMedID 39117164

• 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

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

  Abstract

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

  View details for DOI 10.1016/j.ejmp.2024.103318

  View details for PubMedID 38382210

• First-Year Experience of Stereotactic Body Radiation Therapy/Intensity Modulated Radiation Therapy Treatment Using a Novel Biology-Guided Radiation Therapy Machine. Advances in radiation oncology Shi, M., Simiele, E., Han, B., Pham, D., Palomares, P., Aguirre, M., Gensheimer, M., Vitzthum, L., Le, Q., Surucu, M., Kovalchuk, N. 2024; 9 (1): 101300

  Abstract

  Purpose: The aim of this study was to present the first-year experience of treating patients using intensity modulated radiation therapy (IMRT) and stereotactic body radiation therapy (SBRT) with a biology-guided radiation therapy machine, the RefleXion X1 system, installed in a clinical setting.Methods and Materials: A total of 78 patients were treated on the X1 system using IMRT and SBRT from May 2021 to May 2022. Clinical and technical data including treatment sites, number of pretreatment kilovoltage computed tomography (kVCT) scans, beam-on time, patient setup time, and imaging time were collected and analyzed. Machine quality assurance (QA) results, machine performance, and user satisfactory survey were also collected and reported.Results: The most commonly treated site was the head and neck (63%), followed by the pelvis (23%), abdomen (8%), and thorax (6%). Except for 5 patients (6%) who received SBRT treatments for bony metastases in the pelvis, all treatments were conventionally fractionated IMRT. The number of kVCT scans per fraction was 1.2 ± 0.5 (mean ± standard deviation). The beam-on time was 9.2 ± 3.5 minutes. The patient setup time and imaging time per kVCT was 4.8 ± 2.6 minutes and 4.6 ± 1.5 minutes, respectively. The daily machine output deviation was 0.4 ± 1.2% from the baseline. The patient QA had a passing rate of 97.4 ± 2.8% at 3%/2 mm gamma criteria. The machine uptime was 92% of the total treatment time. The daily QA and kVCT image quality received the highest level of satisfaction. The treatment workflow for therapists received the lowest level of satisfaction.Conclusions: One year after the installation, 78 patients were successfully treated with the X1 system using IMRT and/or SBRT. With the recent Food and Drug Administration clearance of biology-guided radiation therapy, our department is preparing to treat patients using positron emission tomography-guidance via a new product release, which will address deficiencies in the current image-guided radiation therapy workflow.

  View details for DOI 10.1016/j.adro.2023.101300

  View details for PubMedID 38260216

• BIOGUIDE-X: A First-in-Human Study of the Performance of Positron Emission Tomography-Guided Radiotherapy. International journal of radiation oncology, biology, physics Vitzthum, L. K., Surucu, M., Gensheimer, M. F., Kovalchuk, N., Han, B., Pham, D., Chang, D., Shirvani, S. M., Aksoy, D., Maniyedath, A., Narayanan, M., Da Silva, A. J., Mazin, S., Feghali, K. A., Iyengar, P., Dan, T., Pompos, A., Timmerman, R., Öz, O., Cai, B., Garant, A. 2023

  Abstract

  SCINTIX® Biology-guided radiotherapy (BgRT) is a novel tracked dose delivery modality that uses real-time positron emission tomography (PET) to guide radiotherapy beamlets. The BIOGUIDE-X study was performed with sequential cohorts of participants to (1) identify the fluorodeoxyglucose (FDG) dose for SCINTIX therapy and (2) confirm that the emulated dose distribution was consistent with a physician-approved radiotherapy plan.This prospective study included participants with at least 1 FDG-avid targetable primary or metastatic tumor (≥2cm and ≤5cm) in the lung or bone. For Cohort I, a modified 3 + 3 design was used to determine the FDG dose that would result in adequate signal for SCINTIX therapy. For Cohort II, PET imaging data were collected on the X1 system before the first and last fractions among patients undergoing conventional stereotactic body radiotherapy. SCINTIX therapy dose distributions were modeled on the patient's CT anatomy using the collected PET data at each fraction as input to an "emulated delivery" and compared to the physician-approved plan.Cohort I demonstrated adequate FDG activity in 6/6 (100.0%) evaluable participants with the first injected dose level of 15 mCi FDG. In Cohort II, 4 patients with lung tumors and 5 with bone tumors were enrolled, and evaluable emulated delivery data points were collected for 17 treatment fractions. Sixteen of the 17 emulated deliveries resulted in SCINTIX dose distributions that were accurate with respect to the approved SCINTIX therapy plan. The 17th data point was just below the 95% threshold for accuracy (DVH Score = 94.6%). All emulated fluences were physically deliverable. No toxicities were attributed to multiple FDG administrations.SCINTIX therapy is a novel radiotherapy modality in which a radiolabeled tumor can act as its own fiducial for radiotherapy targeting. Emulated SCINTIX therapy dose distributions calculated from continuously acquired real-time PET data were accurate and machine-deliverable in tumors that were 2-5 cm in size with adequate FDG signal characteristics.

  View details for DOI 10.1016/j.ijrobp.2023.12.019

  View details for PubMedID 38147912

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

  Abstract

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

  View details for DOI 10.1109/TPAMI.2023.3289667

  View details for PubMedID 37363838

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

  Abstract

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

  View details for DOI 10.1016/j.ijrobp.2023.04.026

  View details for PubMedID 37141982

• Mitigation of IMRT/SBRT treatment planning errors on the RefleXion X1 system using FMEA within Six Sigma framework Advances in Radiation Oncology Simiele, E., Han, B., Skinner, L., Pham, D., Lewis, J., Gensheimer, M., Vitzthum, L., Chang, D., Surucu, M., Kovalchuk, N. 2023

  View details for DOI 10.1016/j.adro.2023.101186

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

  Abstract

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

  View details for DOI 10.1259/bjr.20220387

  View details for PubMedID 36317922

• Treatment planning system commissioning of the first clinical biology-guided radiotherapy machine. Journal of applied clinical medical physics Simiele, E., Capaldi, D., Breitkreutz, D., Han, B., Yeung, T., White, J., Zaks, D., Owens, M., Maganti, S., Xing, L., Surucu, M., Kovalchuk, N. 2022: e13638

  Abstract

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

  View details for DOI 10.1002/acm2.13638

  View details for PubMedID 35644039

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

  Abstract

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

  View details for DOI 10.1002/acm2.13607

  View details for PubMedID 35482018

• 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

• IMRT and SBRT Treatment Planning Study for the First Clinical Biology-Guided Radiotherapy System. Technology in cancer research & treatment Pham, D., Simiele, E., Breitkreutz, D., Capaldi, D., Han, B., Surucu, M., Oderinde, S., Vitzthum, L., Gensheimer, M., Bagshaw, H., Chin, A., Xing, L., Chang, D. T., Kovalchuk, N. 2022; 21: 15330338221100231

  Abstract

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

  View details for DOI 10.1177/15330338221100231

  View details for PubMedID 35579876

• Small field measurement and monte carlo model validation of a novel image-guided radiotherapy system. Medical physics Shi, M., Chuang, C. F., Kovalchuk, N., Bush, K. K., Zaks, D., Xing, L., Surucu, M., Han, B. 2021

  Abstract

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

  View details for DOI 10.1002/mp.15273

  View details for PubMedID 34628666

• 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

• MR to Ultrasound Image Registration with Segmentation-Based Learning for HDR Prostate Brachytherapy Chen, Y., Xing, L., Yu, L., Liu, W., Fahimian, B., Niedermayr, T., Bagshaw, H., Buyyounouski, M., Han, B. WILEY. 2021

  View details for Web of Science ID 000673145402120

• MR to ultrasound image registration with segmentation-based learning for HDR prostate brachytherapy. Medical physics Chen, Y. n., Xing, L. n., Yu, L. n., Liu, W. n., Fahimian, B. P., Niedermayr, T. n., Bagshaw, H. P., Buyyounouski, M. n., Han, B. n. 2021

  Abstract

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

  View details for DOI 10.1002/mp.14901

  View details for PubMedID 33905566

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

  Abstract

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

  View details for DOI 10.1002/mp.14328

  View details for PubMedID 32542758

• Densely Connected Neural Network With Unbalanced Discriminant and Category Sensitive Constraints for Polyp Recognition IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING Yuan, Y., Qin, W., Ibragimov, B., Zhang, G., Han, B., Meng, M., Xing, L. 2020; 17 (2): 574–83

  View details for DOI 10.1109/TASE.2019.2936645

  View details for Web of Science ID 000528673100003

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

  Abstract

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

  View details for DOI 10.1002/mp.14517

  View details for PubMedID 33012016

• 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

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

  View details for DOI 10.1002/mp.13367

  View details for Web of Science ID 000459616200032

• Feasibility of Image Registration for Ultrasound-Guided Prostate Radiotherapy Based on Similarity Measurement by a Convolutional Neural Network TECHNOLOGY IN CANCER RESEARCH & TREATMENT Zhu, N., Najafi, M., Han, B., Hancock, S., Hristov, D. 2019; 18

  View details for DOI 10.1177/1533033818821964

  View details for Web of Science ID 000482811100001

• 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

• Feasibility of Image Registration for Ultrasound-Guided Prostate Radiotherapy Based on Similarity Measurement by a Convolutional Neural Network. Technology in cancer research & treatment Zhu, N., Najafi, M., Han, B., Hancock, S., Hristov, D. 2019; 18: 1533033818821964

  Abstract

  PURPOSE:: Registration of 3-dimensional ultrasound images poses a challenge for ultrasound-guided radiation therapy of the prostate since ultrasound image content changes significantly with anatomic motion and ultrasound probe position. The purpose of this work is to investigate the feasibility of using a pretrained deep convolutional neural network for similarity measurement in image registration of 3-dimensional transperineal ultrasound prostate images.METHODS:: We propose convolutional neural network-based registration that maximizes a similarity score between 2 identical in size 3-dimensional regions of interest: one encompassing the prostate within a simulation (reference) 3-dimensional ultrasound image and another that sweeps different spatial locations around the expected prostate position within a pretreatment 3-dimensional ultrasound image. The similarity score is calculated by (1) extracting pairs of corresponding 2-dimensional slices (patches) from the regions of interest, (2) providing these pairs as an input to a pretrained convolutional neural network which assigns a similarity score to each pair, and (3) calculating an overall similarity by summing all pairwise scores. The convolutional neural network method was evaluated against ground truth registrations determined by matching implanted fiducial markers visualized in a pretreatment orthogonal pair of x-ray images. The convolutional neural network method was further compared to manual registration and a standard commonly used intensity-based automatic registration approach based on advanced normalized correlation.RESULTS:: For 83 image pairs from 5 patients, convolutional neural network registration errors were smaller than 5 mm in 81% of the cases. In comparison, manual registration errors were smaller than 5 mm in 61% of the cases and advanced normalized correlation registration errors were smaller than 5 mm only in 25% of the cases.CONCLUSION:: Convolutional neural network evaluation against manual registration and an advanced normalized correlation -based registration demonstrated better accuracy and reliability of the convolutional neural network. This suggests that with training on a large data set of transperineal ultrasound prostate images, the convolutional neural network method has potential for robust ultrasound-to-ultrasound registration.

  View details for PubMedID 30803364

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

  Abstract

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

  View details for PubMedID 30633760

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

  Abstract

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

  View details for PubMedID 30597561

• Evaluation of transperineal ultrasound imaging as a potential solution for target tracking during hypofractionated radiotherapy for prostate cancer. Radiation oncology (London, England) Han, B., Najafi, M., Cooper, D. T., Lachaine, M., von Eyben, R., Hancock, S., Hristov, D. 2018; 13 (1): 151

  Abstract

  BACKGROUND: Emerging hypofractionated prostate radiotherapy regimens require solutions for accurate target tracking during beam delivery. The goal of this study is to evaluate the performance of the Clarity ultrasound monitoring system for prostate motion tracking.METHODS: Five prostate patients underwent continuous perineum ultrasound imaging during their daily treatments. Initial absolute 3D positions of fiducials implanted in the prostate were estimated from the KV images. Fiducial positions in MV images acquired during beam delivery were compared with predicted positions based on Clarity 3D tracking. The uncertainty in the comparison results was evaluated in a phantom validation study.RESULTS: Continuous real-time ultrasound motion tracking was recorded in 5 patients and 167 fractions for overall of 39.7h. Phantom validation of the proposed procedure demonstrated that predicted and observed fiducial positions agree within 1.1mm. In patients agreement between predicted and actual fiducial positions varied between 1.3mm and 3.3mm. On average ultrasound tracking reduced the maximum localization error in patients by 20% on average. With the motion corrected, the duration prostate beyond 1mm from its initial treatment position can be reduced from 37 to 22% of the total treatment time.CONCLUSION: Real-time ultrasound tracking reduces uncertainty in prostate position due to intra-fractional motion.TRIAL REGISTRATION: IRB Protocol #27372 . Date of registration of trial: 12/17/2013.

  View details for PubMedID 30126434

• 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

• RIIS-DenseNet: Rotation-Invariant and Image Similarity Constrained Densely Connected Convolutional Network for Polyp Detection Yuan, Y., Qin, W., Ibragimov, B., Han, B., Xing, L., Frangi, A. F., Schnabel, J. A., Davatzikos, C., AlberolaLopez, C., Fichtinger, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 620–28

  View details for DOI 10.1007/978-3-030-00934-2_69

  View details for Web of Science ID 000477921700069

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

  Abstract

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

  View details for DOI 10.1002/acm2.12007

  View details for Web of Science ID 000393176200002

  View details for PubMedCentralID PMC5393354

• Pixel response-based EPID dosimetry for patient specific QA. Journal of applied clinical medical physics Han, B., Ding, A., Lu, M., Xing, L. 2017; 18 (1): 9-17

  Abstract

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

  View details for DOI 10.1002/acm2.12007

  View details for PubMedID 28291939

  View details for PubMedCentralID PMC5393354

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

  Abstract

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

  View details for PubMedID 27806603

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

  Abstract

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

  View details for DOI 10.1118/1.4922400

  View details for PubMedID 26133618

  View details for PubMedCentralID PMC4474956

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

  Abstract

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

  View details for DOI 10.1016/j.ijrobp.2013.08.015

  View details for PubMedID 24113060

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

  Abstract

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

  View details for DOI 10.1016/j.ijrobp.2013.08.015

  View details for PubMedID 24113060

• Cone beam CT imaging with limited angle of projections and prior knowledge for volumetric verification of non-coplanar beam radiation therapy: a proof of concept study. Physics in medicine and biology Meng, B., Xing, L., Han, B., Koong, A., Chang, D., Cheng, J., Li, R. 2013; 58 (21): 7777-7789

  Abstract

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

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

  View details for PubMedID 24140954

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

  Abstract

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

  View details for DOI 10.1118/1.4771935

  View details for Web of Science ID 000313033200003

  View details for PubMedID 23298069

  View details for PubMedCentralID PMC3537718

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

  View details for DOI 10.1117/12.2005765

  View details for Web of Science ID 000322832800032

• Fidelity of dose delivery at high dose rate of volumetric modulated arc therapy in a truebeam linac with flattening filter free beams. Journal of medical physics / Association of Medical Physicists of India Kalantzis, G., Qian, J., Han, B., Luxton, G. 2012; 37 (4): 193-199

  Abstract

  The purpose of this study is to assess fidelity of radiation delivery between high and low dose rates of the flattening filter free (FFF) modes of a new all-digital design medical linear accelerator (Varian TrueBeam™), particularly for plans optimized for volumetric modulated arc therapy (VMAT). Measurements were made for the two energies of flattening filter free photon beams with a Varian TrueBeam™ linac: 6 MV (6 XFFF) at 400 and 1400 MU/min, and 10 MV (10 XFFF) at 400 and 2400 MU/min. Data acquisition and analysis was performed with both ionization chambers and diode detector system Delta(4), for square radiation fields and for 8 VMAT treatment plans optimized for SBRT treatment of lung tumors. For the square fields, a percent dose difference between high and low dose rate of the order of 0.3-0.4% for both photon energies was seen with the ionization chambers, while the contribution to the difference from ion recombination was found to be negligible. For both the VMAT and square-field deliveries, the Delta(4) showed the same average percent dose difference between the two dose rates of ~0.8% and ~0.6% for 10 MV and 6 MV, respectively, with the lower dose rate values giving the greater measured dose compared to the high dose rate. Thus, the VMAT deliveries introduced negligible dose differences between high and low dose rate. Finally, reproducibility of dose measurements was good for both energies.

  View details for DOI 10.4103/0971-6203.103604

  View details for PubMedID 23293450

  View details for PubMedCentralID PMC3532747

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

  Abstract

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

  View details for DOI 10.1118/1.4704729

  View details for Web of Science ID 000303604300039

  View details for PubMedID 22559639

  View details for PubMedCentralID PMC3344884