arrow
返回

Solving the volumetric modulated arc therapy (VMAT) problem using a sequential convex programming method

delete2021-04-14
delete5
delete
OA
AI
P
Pınar Dursun
M
Masoud Zarepisheh *
G
Gourav Jhanwar
J
Joseph O. Deasy
DOI:10.1088/1361-6560/abee58delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
The volumetric modulated arc therapy (VMAT) problem is highly non-convex and much more difficult than the fixed-field intensity modulated radiotherapy optimization problem. To solve it efficiently, we propose a sequential convex programming algorithm that solves a sequence of convex optimization problems. Beginning by optimizing the aperture weights of many (72) evenly distributed beams using the beam's eye view of the target from each direction as the initial aperture shape, the search space is constrained to allowing the leaves to move within a pre-defined step-size. A convex approximation problem is introduced and solved to optimize the leaf positions and the aperture weights within the search space. The algorithm is equipped with both local and global search strategies, whereby a global search is followed by a local search: a large step-size results in a global search with a less accurate convex approximation, followed by a small step-size local search with an accurate convex approximation. The performance of the proposed algorithm is tested on three patients with three different disease sites (paraspinal, prostate and oligometastasis). The algorithm generates VMAT plans comparable to the ideal 72-beam fluence map optimized plans (i.e. IMRT plans before leaf sequencing) in 14 iterations and 36 mins on average. The algorithm is also tested on a small down-sampled prostate case for which we could computationally afford to obtain the ground-truth by solving the non-convex mixed-integer optimization problem exactly. This general algorithm is able to produce results essentially equivalent to the ground-truth but 12 times faster. The algorithm is also scalable and can handle real clinical cases, whereas the ground-truth solution using mixed-integer optimization can only be obtained for highly down-sampled cases.
Keyword:
volumetric modulated arc therapy
external radiation therapy
direct machine parameter optimization
sequential convex programming

期刊

Physics in Medicine and Biology 封面图
Physics in Medicine and Biology
IF:
3.4
论文数:
1.4W
被引数:
3.1W

机构

M
Memorial Sloan Kettering Cancer Center
学者数:
3.4W
论文数: 2.4W
被引数: 4.6W
引用论文

引用论文

Automated intensity modulated treatment planning: The expedited constrained hierarchical optimization (ECHO) system
err2019-05-29
err37
errOAAI
errZarepisheh, Masoud; Hong, Linda; Zhou, Ying; Oh, Jung Hun; Mechalakos, James G.; Hunt, Margie A.; Mageras, Gig S.; Deasy, Joseph O.
err分享
err收藏
Multicriteria VMAT optimization
err2012-01-12
err109
errOAAI
errCraft, David; McQuaid, Dualta; Wala, Jeremiah; Chen, Wei; Salari, Ehsan; Bortfeld, Thomas
err分享
err收藏
Integrating soft and hard dose-volume constraints into hierarchical constrained IMRT optimization
err2019-12-04
err15
errOAAI
errMukherjee, Sovanlal; Hong, Linda; Deasy, Joseph O.; Zarepisheh, Masoud
err分享
err收藏
err分享
err收藏
A new column-generation-based algorithm for VMAT treatment plan optimization
err2012-06-22
err53
errOAAI
errPeng, Fei; Jia, Xun; Gu, Xuejun; Epelman, Marina A.; Romeijn, H. Edwin; Jiang, Steve B.
err分享
err收藏
A column generation heuristic for VMAT planning with adaptive CVaR constraints
err2019-10-21
err2
PREAI
errDursun, Pinar; Taskin, Z. Caner; Altinel, I. Kuban; Bilge, Hatice; Kesen, Nazmiye Donmez; Okutan, Murat; Oral, Ethem Nezih
err分享
err收藏
err分享
err收藏
学者 查看更多内容