Direct leaf trajectory optimization for volumetric modulated arc therapy planning with sliding window delivery

Papp, Dávid; Unkelbach, Jan

doi:10.1118/1.4835435

Cited by 44 publications

(69 citation statements)

References 19 publications

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“…38 considers a sliding window delivery of VMAT and directly optimizes leaf trajectories based on the dosimetric objective function f . In the DAO formulation, a VMAT plan is described by specifying leaf positions at particular time points.…”

Section: E Direct Leaf Trajectory Optimization For Sliding Window mentioning

confidence: 99%

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Optimization approaches to volumetric modulated arc therapy planning

et al. 2015

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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. C 2015 American Association of Physicists in Medicine. [http://dx

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Section: E Direct Leaf Trajectory Optimization For Sliding Window mentioning

confidence: 99%

“…In Ref. 38, approximately 20 arc sectors are suggested. For typical leaf speed constraints, and depending on the treatment site, a 20 field FMO plan can be closely reproduced within 3-5 min.…”

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Optimization approaches to volumetric modulated arc therapy planning

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“…Cassioli and Unkelbach 22 suggested to use the trust-region-based local search approach in combination with column generation to optimize the aperture shapes. Hardemark et al 23 proposed a local search technique on top of the two-stage method to refine the aperture shapes, and Papp and Unkelbach 24 employed a convex formulation of the problem by using some approximations.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous beam sampling and aperture shape optimization for SPORT

Zarepisheh

et al. 2015

Medical Physics

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Purpose: Station parameter optimized radiation therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital linear accelerators, in which the station parameters of a delivery system, such as aperture shape and weight, couch position/angle, gantry/collimator angle, can be optimized simultaneously. SPORT promises to deliver remarkable radiation dose distributions in an efficient manner, yet there exists no optimization algorithm for its implementation. The purpose of this work is to develop an algorithm to simultaneously optimize the beam sampling and aperture shapes. Methods: The authors build a mathematical model with the fundamental station point parameters as the decision variables. To solve the resulting large-scale optimization problem, the authors devise an effective algorithm by integrating three advanced optimization techniques: column generation, subgradient method, and pattern search. Column generation adds the most beneficial stations sequentially until the plan quality improvement saturates and provides a good starting point for the subsequent optimization. It also adds the new stations during the algorithm if beneficial. For each update resulted from column generation, the subgradient method improves the selected stations locally by reshaping the apertures and updating the beam angles toward a descent subgradient direction. The algorithm continues to improve the selected stations locally and globally by a pattern search algorithm to explore the part of search space not reachable by the subgradient method. By combining these three techniques together, all plausible combinations of station parameters are searched efficiently to yield the optimal solution. Results: A SPORT optimization framework with seamlessly integration of three complementary algorithms, column generation, subgradient method, and pattern search, was established. The proposed technique was applied to two previously treated clinical cases: a head and neck and a prostate case. It significantly improved the target conformality and at the same time critical structure sparing compared with conventional intensity modulated radiation therapy (IMRT). In the head and neck case, for example, the average PTV coverage D99% for two PTVs, cord and brainstem max doses, and right parotid gland mean dose were improved, respectively, by about 7%, 37%, 12%, and 16%. Conclusions: The proposed method automatically determines the number of the stations required to generate a satisfactory plan and optimizes simultaneously the involved station parameters, leading to improved quality of the resultant treatment plans as compared with the conventional IMRT plans. C 2015 American Association of Physicists in Medicine. [http://dx

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“…Moreover, these parameters are subject to strong hardware constraints, which have to be satisfied by a treatment plan to ensure its deliverability. Over the years, a number of research efforts have been devoted to developing novel algorithms to solve the VMAT optimization problem [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] . In particular, we have previously developed a column-generation approach 16,18 , and our test results on real patient cases have demonstrated the potential of this method in terms of generating a plan with all the degrees of freedom considered.…”

Section: Introductionmentioning

confidence: 99%

Multi-GPU implementation of a VMAT treatment plan optimization algorithm

et al. 2015

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Purpose: VMAT optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. Highperformance graphics processing units (GPUs) have been used to speed up the computations. However, GPU's relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation based VMAT algorithm, previously developed in our group, on a multi-GPU platform to solve the memory limitation problem. While the column-generation based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. We also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. Methods: The column-generation approach generates VMAT apertures sequentially by solving a pricing problem (PP) and a master problem (MP) iteratively. In our method, the sparse DDC matrix is first stored on CPU in coordinate list format (COO). On the GPU side, this matrix is split into four sub-matrices according to beam angles, which are stored on four GPUs in compressed sparse row (CSR) format. Computation of beamlet price, the first step in PP, is accomplished using multi-GPUs. A fast inter-GPU data transfer scheme is designed using peer-to-peer (P2P) access. The remaining steps of PP and MP problems are implemented on CPU or a single GPU due to their modest problem scale and computational loads. Barzilai and Borwein (BB) algorithm with subspace step scheme is adopted here to solve the MP problem. A head and neck (H&N) cancer case was used to validate our method. We also compare our multi-GPU implementation with three different single GPU implementation strategies: truncating DDC matrix (S1), repeatedly transferring DDC matrix between CPU and GPU (S2), and porting computations involving DDC matrix to CPU (S3), in terms of both plan quality and computational efficiency. Two 2 more H&N patient cases and three prostate cases were also used to demonstrate the advantages of our method. Results: Our multi-GPU implementation can finish the optimization process within ~1 minute for the H&N patient case. S1 leads to an inferior plan quality although its total time was 10 seconds shorter than the multi-GPU implementation due to the reduced matrix size. S2 and S3 yield the same plan quality as the multi-GPU implementation but take ~4 minutes and ~6 minutes, respectively. High computational efficiency was consistently achieved for the other 5 patient cases tested, with VMAT plans of clinically acceptable quality obtained within 23~46 seconds. Conversely, to obtain clinically comparable or acceptable plans for all these 6 VMAT cases that we have tested in t...

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Direct leaf trajectory optimization for volumetric modulated arc therapy planning with sliding window delivery

Cited by 44 publications

References 19 publications

Optimization approaches to volumetric modulated arc therapy planning

Optimization approaches to volumetric modulated arc therapy planning

Simultaneous beam sampling and aperture shape optimization for SPORT

Multi-GPU implementation of a VMAT treatment plan optimization algorithm

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