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Jan Unkelbach - One of the best experts on this subject based on the ideXlab platform.
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optimization approaches to volumetric modulated arc Therapy Planning
Medical Physics, 2015Co-Authors: Jan Unkelbach, Thomas Bortfeld, David Craft, M Alber, Mark Bangert, Rasmus Bokrantz, Danny Z Chen, Lei Xing, Chunhua Men, S NillAbstract:Volumetric modulated arc Therapy (VMAT) has found widespread clinical application in recent years. A large number of treatment Planning studies have evaluated the potential for VMAT for different disease sites based on the currently available commercial implementations of VMAT Planning. In contrast, literature on the underlying mathematical optimization methods used in treatment Planning is scarce. VMAT Planning represents a challenging large scale optimization problem. In contrast to fluence map optimization in intensity-modulated radioTherapy Planning for static beams, VMAT Planning represents a nonconvex optimization problem. In this paper, the authors review the state-of-the-art in VMAT Planning from an algorithmic perspective. Different approaches to VMAT optimization, including arc sequencing methods, extensions of direct aperture optimization, and direct optimization of leaf trajectories are reviewed. Their advantages and limitations are outlined and recommendations for improvements are discussed.
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direct leaf trajectory optimization for volumetric modulated arc Therapy Planning with sliding window delivery
Medical Physics, 2013Co-Authors: David Papp, Jan UnkelbachAbstract:Purpose: The authors propose a novel optimization model for volumetric modulated arc Therapy (VMAT) Planning that directly optimizes deliverable leaf trajectories in the treatment plan optimization problem, and eliminates the need for a separate arc-sequencing step. Methods: In this model, a 360° arc is divided into a given number of arc segments in which the leaves move unidirectionally. This facilitates an algorithm that determines the optimal piecewise linear leaf trajectories for each arc segment, which are deliverable in a given treatment time. Multileaf collimator constraints, including maximum leaf speed and interdigitation, are accounted for explicitly. The algorithm is customized to allow for VMAT delivery using constant gantry speed and dose rate, however, the algorithm generalizes to variable gantry speed if beneficial. Results: The authors demonstrate the method for three different tumor sites: a head-and-neck case, a prostate case, and a paraspinal case. The authors first obtain a reference plan for intensity modulated radioTherapy (IMRT) using fluence map optimization and 20 intensity-modulated fields in equally spaced beam directions, which is beyond the standard of care. Modeling the typical clinical setup for the treatment sites considered, IMRT plans using seven or nine beams are also computed. Subsequently, VMAT plans are optimized bymore » dividing the 360° arc into 20 corresponding arc segments. Assuming typical machine parameters (a dose rate of 600 MU/min, and a maximum leaf speed of 3 cm/s), it is demonstrated that the optimized VMAT plans with 2–3 min delivery time are of noticeably better quality than the 7–9 beam IMRT plans. The VMAT plan quality approaches the quality of the 20-beam IMRT benchmark plan for delivery times between 3 and 4 min. Conclusions: The results indicate that high quality treatments can be delivered in a single arc with 20 arc segments if sufficient time is allowed for modulation in each segment.« less
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direct leaf trajectory optimization for volumetric modulated arc Therapy Planning with sliding window delivery
arXiv: Medical Physics, 2013Co-Authors: David Papp, Jan UnkelbachAbstract:We propose a novel optimization model for volumetric modulated arc Therapy (VMAT) Planning that directly optimizes deliverable leaf trajectories in the treatment plan optimization problem, and eliminates the need for a separate arc-sequencing step. In this model, a 360-degree arc is divided into a given number of arc segments in which the leaves move unidirectionally. This facilitates an algorithm that determines the optimal piecewise linear leaf trajectories for each arc segment, which are deliverable in a given treatment time. Multi-leaf collimator (MLC) constraints, including maximum leaf speed and interdigitation, are accounted for explicitly. The algorithm is customized to allow for VMAT delivery using constant gantry speed and dose rate, however, the algorithm generalizes to variable gantry speed if beneficial. We demonstrate the method for three different tumor sites: a head-and-neck case, a prostate case, and a paraspinal case. For that purpose, we first obtain a reference plan for intensity modulated radioTherapy (IMRT) using fluence map optimization and 20 equally spaced beam directions. Subsequently, VMAT plans are optimized by dividing the 360-degree arc into 20 corresponding arc segments. Assuming typical machine parameters (a dose rate of 600 MU/min, and a maximum leaf speed of 3 cm/sec), it is demonstrated that the quality of the optimized VMAT plans approaches the quality of the IMRT benchmark plan for delivery times between 3 and 4 minutes.
Radhe Mohan - One of the best experts on this subject based on the ideXlab platform.
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effectiveness of robust optimization in intensity modulated proton Therapy Planning for head and neck cancers
Medical Physics, 2013Co-Authors: Wei Liu, Steven J Frank, Peter C Park, L Dong, Ronald X Zhu, Radhe MohanAbstract:Purpose: Intensity-modulated proton Therapy (IMPT) is highly sensitive to uncertainties in beam range and patient setup. Conventionally, these uncertainties are dealt using geometrically expanded Planning target volume (PTV). In this paper, the authors evaluated a robust optimization method that deals with the uncertainties directly during the spot weight optimization to ensure clinical target volume (CTV) coverage without using PTV. The authors compared the two methods for a population of head and neck (H&N) cancer patients. Methods: Two sets of IMPT plans were generated for 14 H&N cases, one being PTV-based conventionally optimized and the other CTV-based robustly optimized. For the PTV-based conventionally optimized plans, the uncertainties are accounted for by expanding CTV to PTV via margins and delivering the prescribed dose to PTV. For the CTV-based robustly optimized plans, spot weight optimization was guided to reduce the discrepancy in doses under extreme setup and range uncertainties directly, while delivering the prescribed dose to CTV rather than PTV. For each of these plans, the authors calculated dose distributions under various uncertainty settings. The root-mean-square dose (RMSD) for each voxel was computed and the area under the RMSD-volume histogram curves (AUC) was used to relatively compare plan robustness. Data derived from the dose volume histogram in the worst-case and nominal doses were used to evaluate the plan optimality. Then the plan evaluation metrics were averaged over the 14 cases and were compared with two-sided paired t tests. Results: CTV-based robust optimization led to more robust (i.e., smaller AUCs) plans for both targets and organs. Under the worst-case scenario and the nominal scenario, CTV-based robustly optimized plans showed better target coverage (i.e., greater D95%), improved dose homogeneity (i.e., smaller D5% − D95%), and lower or equivalent dose to organs at risk. Conclusions: CTV-based robust optimization provided significantly more robust dose distributions to targets and organs than PTV-based conventional optimization in H&N using IMPT. Eliminating the use of PTV and Planning directly based on CTV provided better or equivalent normal tissue sparing.
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iterative solution methods for beam angle and fluence map optimization in intensity modulated radiation Therapy Planning
OR Spectrum, 2008Co-Authors: Jaewon Choi, Radhe MohanAbstract:We present computational approaches for optimizing beam angles and fluence maps in Intensity Modulated Radiation Therapy (IMRT) Planning. We assume that the number of angles to be used for the treatment is given by the treatment planner. A mixed integer programming (MIP) model and a linear programming (LP) model are used to find an optimal set of beam angles and their corresponding fluence maps. The MIP model is solved using the branch-and-bound method while the LP model is solved using the interior point method. In order to reduce the computational burden for solving the optimization models, we introduce iterative beam angle elimination algorithms in which an insignificant beam angle is eliminated in each iteration. Other techniques are also explored including feasible set reduction for LP and data reduction. Experiments are made to show the computational advantage of the iterative methods for optimizing angles using real patient cases.
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Iterative solution methods for beam angle and fluence map optimization in intensity modulated radiation Therapy Planning
OR Spectrum, 2007Co-Authors: Gino J. Lim, Jaewon Choi, Radhe MohanAbstract:We present computational approaches for optimizing beam angles and fluence maps in Intensity Modulated Radiation Therapy (IMRT) Planning. We assume that the number of angles to be used for the treatment is given by the treatment planner. A mixed integer programming (MIP) model and a linear programming (LP) model are used to find an optimal set of beam angles and their corresponding fluence maps. The MIP model is solved using the branch-and-bound method while the LP model is solved using the interior point method. In order to reduce the computational burden for solving the optimization models, we introduce iterative beam angle elimination algorithms in which an insignificant beam angle is eliminated in each iteration. Other techniques are also explored including feasible set reduction for LP and data reduction. Experiments are made to show the computational advantage of the iterative methods for optimizing angles using real patient cases.
David Craft - One of the best experts on this subject based on the ideXlab platform.
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optimization approaches to volumetric modulated arc Therapy Planning
Medical Physics, 2015Co-Authors: Jan Unkelbach, Thomas Bortfeld, David Craft, M Alber, Mark Bangert, Rasmus Bokrantz, Danny Z Chen, Lei Xing, Chunhua Men, S NillAbstract:Volumetric modulated arc Therapy (VMAT) has found widespread clinical application in recent years. A large number of treatment Planning studies have evaluated the potential for VMAT for different disease sites based on the currently available commercial implementations of VMAT Planning. In contrast, literature on the underlying mathematical optimization methods used in treatment Planning is scarce. VMAT Planning represents a challenging large scale optimization problem. In contrast to fluence map optimization in intensity-modulated radioTherapy Planning for static beams, VMAT Planning represents a nonconvex optimization problem. In this paper, the authors review the state-of-the-art in VMAT Planning from an algorithmic perspective. Different approaches to VMAT optimization, including arc sequencing methods, extensions of direct aperture optimization, and direct optimization of leaf trajectories are reviewed. Their advantages and limitations are outlined and recommendations for improvements are discussed.
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a fast optimization algorithm for multicriteria intensity modulated proton Therapy Planning
Medical Physics, 2010Co-Authors: Wei Chen, David Craft, T Madden, Kewu Zhang, Hanne M Kooy, Gabor T HermanAbstract:Purpose: To describe a fast projection algorithm for optimizing intensity modulated proton Therapy (IMPT) plans and to describe and demonstrate the use of this algorithm in multicriteria IMPT Planning. Methods: The authors develop a projection-based solver for a class of convex optimization problems and apply it to IMPT treatment Planning. The speed of the solver permits its use in multicriteria optimization, where several optimizations are performed which span the space of possible treatment plans. The authors describe a plan database generation procedure which is customized to the requirements of the solver. The optimality precision of the solver can be specified by the user. Results: The authors apply the algorithm to three clinical cases: A pancreas case, an esophagus case, and a tumor along the rib cage case. Detailed analysis of the pancreas case shows that the algorithm is orders of magnitude faster than industry-standard general purpose algorithms (MOSEK’s interior point optimizer, primal simplex optimizer, and dual simplex optimizer). Additionally, the projection solver has almost no memory overhead. Conclusions: The speed and guaranteed accuracy of the algorithm make it suitable for use in multicriteria treatment Planning, which requires the computation of several diverse treatment plans. Additionally, given the low memory overhead of the algorithm, the method can be extended to include multiple geometric instances and proton range possibilities, for robust optimization.
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analyzing the main trade offs in multiobjective radiation Therapy treatment Planning databases
Physics in Medicine and Biology, 2009Co-Authors: Tobias Spalke, David Craft, Thomas BortfeldAbstract:Multiobjective radioTherapy Planning aims to capture all clinically relevant trade-offs between the various Planning goals. This is accomplished by calculating a representative set of Pareto optimal solutions and storing them in a database. The structure of these representative Pareto sets is still not fully investigated. We propose two methods for a systematic analysis of multiobjective databases: principal component analysis and the isomap method. Both methods are able to extract the key trade-offs from a database and provide information which can lead to a better understanding of the clinical case and intensity-modulated radiation Therapy Planning in general.
Gregor Habl - One of the best experts on this subject based on the ideXlab platform.
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patterns of failure after radical prostatectomy in prostate cancer implications for radiation Therapy Planning after 68ga psma pet imaging
European Journal of Nuclear Medicine and Molecular Imaging, 2017Co-Authors: Kilian Schiller, K Sauter, Sabrina Dewes, Matthias Eiber, Tobias Maurer, J E Gschwend, Stephanie E Combs, Gregor HablAbstract:Salvage radioTherapy (SRT) after radical prostatectomy (RPE) and lymphadenectomy (LAE) is the appropriate radioTherapy option for patients with persistent/ recurrent prostate cancer (PC). 68Ga-PSMA-PET imaging has been shown to accurately detect PC lesions in a primary setting as well as for local recurrence or for lymph node (LN) metastases. In this study we evaluated the patterns of recurrence after RPE in patients with PC, putting a highlight on the differentiation between sites that would have been covered by a standard radiation Therapy (RT) field in consensus after the RTOG consensus and others that would have not. Thirty-one out of 83 patients (37%) with high-risk PC were the subject of our study. Information from 68Ga-PSMA-PET imaging was used to individualize treatment plans to include suspicious lesions as well as possibly boost sites with tracer uptake in LN or the prostate bed. For evaluation, 68Ga-PSMA-PET-positive LN were contoured in a patient dataset with a standard lymph drainage (RTOG consensus on CTV definition of pelvic lymph nodes) radiation field depicting color-coded nodes that would have been infield or outfield of that standard lymph drainage field and thereby visualizing typical patterns of failure of a “blind” radiation Therapy after RPE and LAE. Compared to negative conventional imaging (CT/MRI), lesions suspicious for PC were detected in 27/31 cases (87.1%) by 68Ga-PSMA-PET imaging, which resulted in changes to the radiation concept. There were 16/31 patients (51.6%) that received a simultaneous integrated boost (SIB) to a subarea of the prostate bed (in only three cases this dose escalation would have been planned without the additional knowledge of 68Ga-PSMA-PET imaging) and 18/31 (58.1%) to uncommon (namely presacral, paravesical, pararectal, preacetabular and obturatoric) LN sites. Furthermore, 14 patients (45.2%) had a changed TNM staging result by means of 68Ga-PSMA-PET imaging. Compared to conventional CT or MRI staging, 68Ga-PSMA-PET imaging detects more PC lesions and, thus, significantly influences radiation Planning in recurrent prostate cancer patients enabling individually tailored treatment.
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patterns of failure after radical prostatectomy in prostate cancer implications for radiation Therapy Planning after 68 ga psma pet imaging
European Journal of Nuclear Medicine and Molecular Imaging, 2017Co-Authors: Kilian Schiller, K Sauter, Sabrina Dewes, Matthias Eiber, Tobias Maurer, J E Gschwend, Stephanie E Combs, Gregor HablAbstract:Salvage radioTherapy (SRT) after radical prostatectomy (RPE) and lymphadenectomy (LAE) is the appropriate radioTherapy option for patients with persistent/ recurrent prostate cancer (PC). 68Ga-PSMA-PET imaging has been shown to accurately detect PC lesions in a primary setting as well as for local recurrence or for lymph node (LN) metastases. In this study we evaluated the patterns of recurrence after RPE in patients with PC, putting a highlight on the differentiation between sites that would have been covered by a standard radiation Therapy (RT) field in consensus after the RTOG consensus and others that would have not. Thirty-one out of 83 patients (37%) with high-risk PC were the subject of our study. Information from 68Ga-PSMA-PET imaging was used to individualize treatment plans to include suspicious lesions as well as possibly boost sites with tracer uptake in LN or the prostate bed. For evaluation, 68Ga-PSMA-PET-positive LN were contoured in a patient dataset with a standard lymph drainage (RTOG consensus on CTV definition of pelvic lymph nodes) radiation field depicting color-coded nodes that would have been infield or outfield of that standard lymph drainage field and thereby visualizing typical patterns of failure of a “blind” radiation Therapy after RPE and LAE. Compared to negative conventional imaging (CT/MRI), lesions suspicious for PC were detected in 27/31 cases (87.1%) by 68Ga-PSMA-PET imaging, which resulted in changes to the radiation concept. There were 16/31 patients (51.6%) that received a simultaneous integrated boost (SIB) to a subarea of the prostate bed (in only three cases this dose escalation would have been planned without the additional knowledge of 68Ga-PSMA-PET imaging) and 18/31 (58.1%) to uncommon (namely presacral, paravesical, pararectal, preacetabular and obturatoric) LN sites. Furthermore, 14 patients (45.2%) had a changed TNM staging result by means of 68Ga-PSMA-PET imaging. Compared to conventional CT or MRI staging, 68Ga-PSMA-PET imaging detects more PC lesions and, thus, significantly influences radiation Planning in recurrent prostate cancer patients enabling individually tailored treatment.
Rasmus Bokrantz - One of the best experts on this subject based on the ideXlab platform.
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optimization approaches to volumetric modulated arc Therapy Planning
Medical Physics, 2015Co-Authors: Jan Unkelbach, Thomas Bortfeld, David Craft, M Alber, Mark Bangert, Rasmus Bokrantz, Danny Z Chen, Lei Xing, Chunhua Men, S NillAbstract:Volumetric modulated arc Therapy (VMAT) has found widespread clinical application in recent years. A large number of treatment Planning studies have evaluated the potential for VMAT for different disease sites based on the currently available commercial implementations of VMAT Planning. In contrast, literature on the underlying mathematical optimization methods used in treatment Planning is scarce. VMAT Planning represents a challenging large scale optimization problem. In contrast to fluence map optimization in intensity-modulated radioTherapy Planning for static beams, VMAT Planning represents a nonconvex optimization problem. In this paper, the authors review the state-of-the-art in VMAT Planning from an algorithmic perspective. Different approaches to VMAT optimization, including arc sequencing methods, extensions of direct aperture optimization, and direct optimization of leaf trajectories are reviewed. Their advantages and limitations are outlined and recommendations for improvements are discussed.
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a critical evaluation of worst case optimization methods for robust intensity modulated proton Therapy Planning
Medical Physics, 2014Co-Authors: Albin Fredriksson, Rasmus BokrantzAbstract:Purpose: To critically evaluate and compare three worst case optimization methods that have been previously employed to generate intensity-modulated proton Therapy treatment plans that are robust against systematic errors. The goal of the evaluation is to identify circumstances when the methods behave differently and to describe the mechanism behind the differences when they occur. Methods: The worst case methods optimize plans to perform as well as possible under the worst case scenario that can physically occur (composite worst case), the combination of the worst case scenarios for each objective constituent considered independently (objectivewise worst case), and the combination of the worst case scenarios for each voxel considered independently (voxelwise worst case). These three methods were assessed with respect to treatment Planning for prostate under systematic setup uncertainty. An equivalence with probabilistic optimization was used to identify the scenarios that determine the outcome of the optimization. Results: If the conflict between target coverage and normal tissue sparing is small and no dose-volume histogram (DVH) constraints are present, then all three methods yield robust plans. Otherwise, they all have their shortcomings: Composite worst case led to unnecessarily low plan quality in boundary scenarios that were less difficult than the worst case ones. Objectivewise worst case generally led to nonrobust plans. Voxelwise worst case led to overly conservative plans with respect to DVH constraints, which resulted in excessive dose to normal tissue, and less sharp dose fall-off than the other two methods. Conclusions: The three worst case methods have clearly different behaviors. These behaviors can be understood from which scenarios that are active in the optimization. No particular method is superior to the others under all circumstances: composite worst case is suitable if the conflicts are not very severe or there are DVH constraints whereas voxelwise worst case is advantageous if there are severe conflicts but no DVH constraints. The advantages of composite and voxelwise worst case outweigh those of objectivewise worst case.
