The Experts below are selected from a list of 69 Experts worldwide ranked by ideXlab platform
R Berbeco - One of the best experts on this subject based on the ideXlab platform.
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registration of clinical volumes to Beams Eye View images for real time tracking
Medical Physics, 2014Co-Authors: Jonathan H Bryant, J Rottmann, J Lewis, P Mishra, P Keall, R BerbecoAbstract:Purpose: The authors combine the registration of 2D beam’s Eye View (BEV) images and 3D planning computed tomography (CT) images, with relative, markerless tumor tracking to provide automatic absolute tracking of physician defined volumes such as the gross tumor volume (GTV). Methods: During treatment of lung SBRT cases, BEV images were continuously acquired with an electronic portal imaging device (EPID) operating in cine mode. For absolute registration of physician-defined volumes, an intensity based 2D/3D registration to the planning CT was performed using the end-of-exhale (EoE) phase of the four dimensional computed tomography (4DCT). The volume was converted from Hounsfield units into electron density by a calibration curve and digitally reconstructed radiographs (DRRs) were generated for each beam geometry. Using normalized cross correlation between the DRR and an EoE BEV image, the best in-plane rigid transformation was found. The transformation was applied to physician-defined contours in the planning CT, mapping them into the EPID image domain. A robust multiregion method of relative markerless lung tumor tracking quantified deviations from the EoE position. Results: The success of 2D/3D registration was demonstrated at the EoE breathing phase. By registering at this phase and then employing a separate technique for relative tracking, the authors are able to successfully track target volumes in the BEV images throughout the entire treatment delivery. Conclusions: Through the combination of EPID/4DCT registration and relative tracking, a necessary step toward the clinical implementation of BEV tracking has been completed. The knowledge of tumor volumes relative to the treatment field is important for future applications like real-time motion management, adaptive radiotherapy, and delivered dose calculations.
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we a 134 11 registration of clinical volumes to Beams Eye View images for real time tracking
Medical Physics, 2013Co-Authors: Jonathan H Bryant, J Rottmann, J Lewis, P Keall, R BerbecoAbstract:Purpose: To develop the 2D/3D registration of cine mode electronic portal imaging device (EPID) images acquired during radiotherapy treatment to the planning computed tomography (CT) images and combine it with relative, markerless EPID tumor tracking. Together the methods will provide an automatic absolute tracking between physician defined volumes such as the gross tumor volume (GTV) the treatment field. Methods: During treatment of lung SBRT cases, EPID images were continuously acquired. The relative motion was tracked using a markerless multitemplate algorithm whose accuracy was previously confirmed and assessed with manual tracking. Each image then underwent an intensity based 2D/3D registration to the planning CT. In order to minimize the effect of motion blur, the end‐of‐exhale phase of the four dimensional computed tomography (4DCT) was used. The volume was converted from Hounsfield units into electron density by a calibration curve and DRRs were generated for the beam geometry. Using normalized cross correlation (NCC) between the DRR and EPID image, the best in plane rigid transformation was found. It was then applied to contours in the planning CT, mapping them into the EPID image domain. The best breathing phase for the registration was found with a large set of patient data. Results: The success of 2D/3D registration proved accurate only over certain phases of the breathing cycle. By registering at this time and using relative tracking, we successfully track target volumes in the EPID images throughout the entire treatment delivery. Conclusions: Through the combination of relative tracking and phase dependent EPID/4DCT registration, it is possible to track clinical volumes on EPID images. This knowledge of tumor volumes relative to the treatment field provides powerful information for future applications like motion management, adaptive radiotherapy and delivered dose calculations.
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su ee a3 05 mutual information for Beams Eye View lung tumor tracking without radiopaque markers
Medical Physics, 2009Co-Authors: J Rottmann, M Aristophanous, So Yeon Park, R BerbecoAbstract:Purpose: To keep safety margins in lung stereotactic body radiation therapy(SBRT) small and provide retrospective calculation of the delivereddose, the tumor motion should be monitored. We propose a tumor tracking algorithm that can estimate the tumor location from portal images taken during the treatment without the help of fiducial markers. Method and Materials: An algorithm based on a normalized mutual information technique was developed for tumor tracking. First a tumor template and a search region are identified on a DRR set reconstructed from a 4DCT acquired prior to the treatment. The set consists of 10 images relating to 10 equally sized breathing phase bins. The template is then used to track the tumor over the sequence of portal images. To estimate the tracking precision a dynamic thorax phantom was employed. Results: The phantom study showed a sub millimeter tracking accuracy in the superior‐ inferior direction for anterior‐posterior and lateral fields. In a preliminary retrospective patient study the algorithm was able to track the tumor motion throughout the whole imagesequence. Manual verification yielded a tracking magnitude error of xy = (3.4 ± 0.8) mm. Furthermore the algorithm's robustness was tested with portal imagesequences from two other patients with different tumor motion amplitude and contrast. The accuaracy was estimated by comparison with manual tracking and yielded xy = (1.7 ± 1.9) mm and xy = (1.2 ± 0.8) mm, respectively. Conclusion: The algorithm has shown great potential for markerless lungtumor tracking. First test results showed that it can perform tumor tracking on portal images and DRRs even if the tracking template was defined in the other modality respectively. Conflict of Interest: Varian Medical Systems, Inc.
J Rottmann - One of the best experts on this subject based on the ideXlab platform.
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registration of clinical volumes to Beams Eye View images for real time tracking
Medical Physics, 2014Co-Authors: Jonathan H Bryant, J Rottmann, J Lewis, P Mishra, P Keall, R BerbecoAbstract:Purpose: The authors combine the registration of 2D beam’s Eye View (BEV) images and 3D planning computed tomography (CT) images, with relative, markerless tumor tracking to provide automatic absolute tracking of physician defined volumes such as the gross tumor volume (GTV). Methods: During treatment of lung SBRT cases, BEV images were continuously acquired with an electronic portal imaging device (EPID) operating in cine mode. For absolute registration of physician-defined volumes, an intensity based 2D/3D registration to the planning CT was performed using the end-of-exhale (EoE) phase of the four dimensional computed tomography (4DCT). The volume was converted from Hounsfield units into electron density by a calibration curve and digitally reconstructed radiographs (DRRs) were generated for each beam geometry. Using normalized cross correlation between the DRR and an EoE BEV image, the best in-plane rigid transformation was found. The transformation was applied to physician-defined contours in the planning CT, mapping them into the EPID image domain. A robust multiregion method of relative markerless lung tumor tracking quantified deviations from the EoE position. Results: The success of 2D/3D registration was demonstrated at the EoE breathing phase. By registering at this phase and then employing a separate technique for relative tracking, the authors are able to successfully track target volumes in the BEV images throughout the entire treatment delivery. Conclusions: Through the combination of EPID/4DCT registration and relative tracking, a necessary step toward the clinical implementation of BEV tracking has been completed. The knowledge of tumor volumes relative to the treatment field is important for future applications like real-time motion management, adaptive radiotherapy, and delivered dose calculations.
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we a 134 11 registration of clinical volumes to Beams Eye View images for real time tracking
Medical Physics, 2013Co-Authors: Jonathan H Bryant, J Rottmann, J Lewis, P Keall, R BerbecoAbstract:Purpose: To develop the 2D/3D registration of cine mode electronic portal imaging device (EPID) images acquired during radiotherapy treatment to the planning computed tomography (CT) images and combine it with relative, markerless EPID tumor tracking. Together the methods will provide an automatic absolute tracking between physician defined volumes such as the gross tumor volume (GTV) the treatment field. Methods: During treatment of lung SBRT cases, EPID images were continuously acquired. The relative motion was tracked using a markerless multitemplate algorithm whose accuracy was previously confirmed and assessed with manual tracking. Each image then underwent an intensity based 2D/3D registration to the planning CT. In order to minimize the effect of motion blur, the end‐of‐exhale phase of the four dimensional computed tomography (4DCT) was used. The volume was converted from Hounsfield units into electron density by a calibration curve and DRRs were generated for the beam geometry. Using normalized cross correlation (NCC) between the DRR and EPID image, the best in plane rigid transformation was found. It was then applied to contours in the planning CT, mapping them into the EPID image domain. The best breathing phase for the registration was found with a large set of patient data. Results: The success of 2D/3D registration proved accurate only over certain phases of the breathing cycle. By registering at this time and using relative tracking, we successfully track target volumes in the EPID images throughout the entire treatment delivery. Conclusions: Through the combination of relative tracking and phase dependent EPID/4DCT registration, it is possible to track clinical volumes on EPID images. This knowledge of tumor volumes relative to the treatment field provides powerful information for future applications like motion management, adaptive radiotherapy and delivered dose calculations.
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su ee a3 05 mutual information for Beams Eye View lung tumor tracking without radiopaque markers
Medical Physics, 2009Co-Authors: J Rottmann, M Aristophanous, So Yeon Park, R BerbecoAbstract:Purpose: To keep safety margins in lung stereotactic body radiation therapy(SBRT) small and provide retrospective calculation of the delivereddose, the tumor motion should be monitored. We propose a tumor tracking algorithm that can estimate the tumor location from portal images taken during the treatment without the help of fiducial markers. Method and Materials: An algorithm based on a normalized mutual information technique was developed for tumor tracking. First a tumor template and a search region are identified on a DRR set reconstructed from a 4DCT acquired prior to the treatment. The set consists of 10 images relating to 10 equally sized breathing phase bins. The template is then used to track the tumor over the sequence of portal images. To estimate the tracking precision a dynamic thorax phantom was employed. Results: The phantom study showed a sub millimeter tracking accuracy in the superior‐ inferior direction for anterior‐posterior and lateral fields. In a preliminary retrospective patient study the algorithm was able to track the tumor motion throughout the whole imagesequence. Manual verification yielded a tracking magnitude error of xy = (3.4 ± 0.8) mm. Furthermore the algorithm's robustness was tested with portal imagesequences from two other patients with different tumor motion amplitude and contrast. The accuaracy was estimated by comparison with manual tracking and yielded xy = (1.7 ± 1.9) mm and xy = (1.2 ± 0.8) mm, respectively. Conclusion: The algorithm has shown great potential for markerless lungtumor tracking. First test results showed that it can perform tumor tracking on portal images and DRRs even if the tracking template was defined in the other modality respectively. Conflict of Interest: Varian Medical Systems, Inc.
Klaus Schilling - One of the best experts on this subject based on the ideXlab platform.
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Model Predictive Control for Tumor Motion Compensation in Robot Assisted Radiotherapy
IFAC Proceedings Volumes, 2016Co-Authors: Christian Herrmann, Lei Ma, Klaus SchillingAbstract:Abstract This paper presents model predictive control (MPC) of a HexaPOD treatment couch used in robot assisted radiotherapy for motion compensation of lung tumors. The HexaPOD, carrying the patient during treatment, performs translational movements such that the tumor motion in the Beams-Eye-View of the linear accelerator is eliminated. We first briefly discuss appropriate dynamic models of the treatment couch usable for MPC. It is shown how MPC can be applied to the problem of motion compensation. The influence of horizon parameters and control increment weighting in MPC as well as the performance w.r.t. the tracking error for several test and patient datasets is evaluated with experiments on the hardware.
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Improving patient comfort using model predictive control in robot-assisted radiotherapy
2013 IEEE International Conference on Robotics and Automation, 2013Co-Authors: Christian Herrmann, Klaus SchillingAbstract:Moving tumors, especially in the vicinity of lungs, pose a challenging problem in radiotherapy as healthy tissue should not be irradiated. We developed a system to compensate tumor motion with the robotic treatment couch HexaPOD to improve treatment quality. The HexaPOD, carrying the patient, counteracts the tumor motion so that it is eliminated in the Beams-Eye-View of the linear accelerator. The focus of this work is on two control methods for the HexaPOD in order to realize reference tracking. The first method is a simple position control scheme to enable reference tracking. It is reformulated as second method to be adopted by a model predictive controller to better account for patient comfort and to maintain tracking accuracy. The performance of both methods is compared in experiments with real hardware using prerecorded patient trajectories and human volunteers whose breathing motion was compensated.
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ICRA - Improving patient comfort using model predictive control in robot-assisted radiotherapy
2013 IEEE International Conference on Robotics and Automation, 2013Co-Authors: Christian Herrmann, Klaus SchillingAbstract:Moving tumors, especially in the vicinity of lungs, pose a challenging problem in radiotherapy as healthy tissue should not be irradiated. We developed a system to compensate tumor motion with the robotic treatment couch HexaPOD to improve treatment quality. The HexaPOD, carrying the patient, counteracts the tumor motion so that it is eliminated in the Beams-Eye-View of the linear accelerator. The focus of this work is on two control methods for the HexaPOD in order to realize reference tracking. The first method is a simple position control scheme to enable reference tracking. It is reformulated as second method to be adopted by a model predictive controller to better account for patient comfort and to maintain tracking accuracy. The performance of both methods is compared in experiments with real hardware using prerecorded patient trajectories and human volunteers whose breathing motion was compensated.
Jonathan H Bryant - One of the best experts on this subject based on the ideXlab platform.
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registration of clinical volumes to Beams Eye View images for real time tracking
Medical Physics, 2014Co-Authors: Jonathan H Bryant, J Rottmann, J Lewis, P Mishra, P Keall, R BerbecoAbstract:Purpose: The authors combine the registration of 2D beam’s Eye View (BEV) images and 3D planning computed tomography (CT) images, with relative, markerless tumor tracking to provide automatic absolute tracking of physician defined volumes such as the gross tumor volume (GTV). Methods: During treatment of lung SBRT cases, BEV images were continuously acquired with an electronic portal imaging device (EPID) operating in cine mode. For absolute registration of physician-defined volumes, an intensity based 2D/3D registration to the planning CT was performed using the end-of-exhale (EoE) phase of the four dimensional computed tomography (4DCT). The volume was converted from Hounsfield units into electron density by a calibration curve and digitally reconstructed radiographs (DRRs) were generated for each beam geometry. Using normalized cross correlation between the DRR and an EoE BEV image, the best in-plane rigid transformation was found. The transformation was applied to physician-defined contours in the planning CT, mapping them into the EPID image domain. A robust multiregion method of relative markerless lung tumor tracking quantified deviations from the EoE position. Results: The success of 2D/3D registration was demonstrated at the EoE breathing phase. By registering at this phase and then employing a separate technique for relative tracking, the authors are able to successfully track target volumes in the BEV images throughout the entire treatment delivery. Conclusions: Through the combination of EPID/4DCT registration and relative tracking, a necessary step toward the clinical implementation of BEV tracking has been completed. The knowledge of tumor volumes relative to the treatment field is important for future applications like real-time motion management, adaptive radiotherapy, and delivered dose calculations.
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we a 134 11 registration of clinical volumes to Beams Eye View images for real time tracking
Medical Physics, 2013Co-Authors: Jonathan H Bryant, J Rottmann, J Lewis, P Keall, R BerbecoAbstract:Purpose: To develop the 2D/3D registration of cine mode electronic portal imaging device (EPID) images acquired during radiotherapy treatment to the planning computed tomography (CT) images and combine it with relative, markerless EPID tumor tracking. Together the methods will provide an automatic absolute tracking between physician defined volumes such as the gross tumor volume (GTV) the treatment field. Methods: During treatment of lung SBRT cases, EPID images were continuously acquired. The relative motion was tracked using a markerless multitemplate algorithm whose accuracy was previously confirmed and assessed with manual tracking. Each image then underwent an intensity based 2D/3D registration to the planning CT. In order to minimize the effect of motion blur, the end‐of‐exhale phase of the four dimensional computed tomography (4DCT) was used. The volume was converted from Hounsfield units into electron density by a calibration curve and DRRs were generated for the beam geometry. Using normalized cross correlation (NCC) between the DRR and EPID image, the best in plane rigid transformation was found. It was then applied to contours in the planning CT, mapping them into the EPID image domain. The best breathing phase for the registration was found with a large set of patient data. Results: The success of 2D/3D registration proved accurate only over certain phases of the breathing cycle. By registering at this time and using relative tracking, we successfully track target volumes in the EPID images throughout the entire treatment delivery. Conclusions: Through the combination of relative tracking and phase dependent EPID/4DCT registration, it is possible to track clinical volumes on EPID images. This knowledge of tumor volumes relative to the treatment field provides powerful information for future applications like motion management, adaptive radiotherapy and delivered dose calculations.
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WE‐A‐134‐11: Registration of Clinical Volumes to Beams‐Eye‐View Images for Real‐Time Tracking
Medical Physics, 2013Co-Authors: Jonathan H Bryant, P Keall, Joerg Rottmann, John H. Lewis, Ross BerbecoAbstract:Purpose: To develop the 2D/3D registration of cine mode electronic portal imaging device (EPID) images acquired during radiotherapy treatment to the planning computed tomography (CT) images and combine it with relative, markerless EPID tumor tracking. Together the methods will provide an automatic absolute tracking between physician defined volumes such as the gross tumor volume (GTV) the treatment field. Methods: During treatment of lung SBRT cases, EPID images were continuously acquired. The relative motion was tracked using a markerless multitemplate algorithm whose accuracy was previously confirmed and assessed with manual tracking. Each image then underwent an intensity based 2D/3D registration to the planning CT. In order to minimize the effect of motion blur, the end‐of‐exhale phase of the four dimensional computed tomography (4DCT) was used. The volume was converted from Hounsfield units into electron density by a calibration curve and DRRs were generated for the beam geometry. Using normalized cross correlation (NCC) between the DRR and EPID image, the best in plane rigid transformation was found. It was then applied to contours in the planning CT, mapping them into the EPID image domain. The best breathing phase for the registration was found with a large set of patient data. Results: The success of 2D/3D registration proved accurate only over certain phases of the breathing cycle. By registering at this time and using relative tracking, we successfully track target volumes in the EPID images throughout the entire treatment delivery. Conclusions: Through the combination of relative tracking and phase dependent EPID/4DCT registration, it is possible to track clinical volumes on EPID images. This knowledge of tumor volumes relative to the treatment field provides powerful information for future applications like motion management, adaptive radiotherapy and delivered dose calculations.
Christian Herrmann - One of the best experts on this subject based on the ideXlab platform.
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Model Predictive Control for Tumor Motion Compensation in Robot Assisted Radiotherapy
IFAC Proceedings Volumes, 2016Co-Authors: Christian Herrmann, Lei Ma, Klaus SchillingAbstract:Abstract This paper presents model predictive control (MPC) of a HexaPOD treatment couch used in robot assisted radiotherapy for motion compensation of lung tumors. The HexaPOD, carrying the patient during treatment, performs translational movements such that the tumor motion in the Beams-Eye-View of the linear accelerator is eliminated. We first briefly discuss appropriate dynamic models of the treatment couch usable for MPC. It is shown how MPC can be applied to the problem of motion compensation. The influence of horizon parameters and control increment weighting in MPC as well as the performance w.r.t. the tracking error for several test and patient datasets is evaluated with experiments on the hardware.
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Improving patient comfort using model predictive control in robot-assisted radiotherapy
2013 IEEE International Conference on Robotics and Automation, 2013Co-Authors: Christian Herrmann, Klaus SchillingAbstract:Moving tumors, especially in the vicinity of lungs, pose a challenging problem in radiotherapy as healthy tissue should not be irradiated. We developed a system to compensate tumor motion with the robotic treatment couch HexaPOD to improve treatment quality. The HexaPOD, carrying the patient, counteracts the tumor motion so that it is eliminated in the Beams-Eye-View of the linear accelerator. The focus of this work is on two control methods for the HexaPOD in order to realize reference tracking. The first method is a simple position control scheme to enable reference tracking. It is reformulated as second method to be adopted by a model predictive controller to better account for patient comfort and to maintain tracking accuracy. The performance of both methods is compared in experiments with real hardware using prerecorded patient trajectories and human volunteers whose breathing motion was compensated.
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ICRA - Improving patient comfort using model predictive control in robot-assisted radiotherapy
2013 IEEE International Conference on Robotics and Automation, 2013Co-Authors: Christian Herrmann, Klaus SchillingAbstract:Moving tumors, especially in the vicinity of lungs, pose a challenging problem in radiotherapy as healthy tissue should not be irradiated. We developed a system to compensate tumor motion with the robotic treatment couch HexaPOD to improve treatment quality. The HexaPOD, carrying the patient, counteracts the tumor motion so that it is eliminated in the Beams-Eye-View of the linear accelerator. The focus of this work is on two control methods for the HexaPOD in order to realize reference tracking. The first method is a simple position control scheme to enable reference tracking. It is reformulated as second method to be adopted by a model predictive controller to better account for patient comfort and to maintain tracking accuracy. The performance of both methods is compared in experiments with real hardware using prerecorded patient trajectories and human volunteers whose breathing motion was compensated.