Minimum-monitor-unit optimization via a stochastic coordinate descent method.
Minimum-monitor-unit optimization via a stochastic coordinate descent method.
复制标题
DOI：
10.1088/1361-6560/ac4212
复制
发表时间：
2022-01-17
影响因子：
3.5
通讯作者：
中科院分区：
文献类型：
--
作者：
可测量的质子点受到最小监测单元（MMU）的约束。由于MMU的强非凸性，具有较大MMU阈值的MMU优化问题在数学上仍然具有挑战性。然而，MMU优化是质子放射治疗（RT）的基础，包括有效的IMPT和质子弧输送（ARC）。本研究旨在发展一种新的最佳化演算法，有效地解决MMU问题。我们的新算法主要是基于随机坐标下降（SCD）方法。它涉及三个主要步骤：首先通过迭代凸松弛方法将剂量-体积-直方图（DVH）规划约束的有效集确定与MMU问题解耦;其次通过SCD处理MMU约束的非凸性以定位非零点的索引集;再次通过投影梯度下降方法求解投影到非零点的凸集合的凸子问题。对该方法进行了验证，并与交替方向乘子法（ADMM）进行了比较。结果表明SCD的计划质量优于ADMM，例如，肺部病例的适形指数（CI）在IMPT期间从0.56提高到0.69，在ARC期间从0.28提高到0.80。此外，SCD成功地处理了ADMM未能处理的来自大MMU阈值的非凸性，在这个意义上，（1）来自ARC的计划质量比IMPT差（例如，当使用ADMM时，IMPT的CI为0.28，ARC的CI为0.56）;（2）相反，使用SCD时，ARC的计划质量优于IMPT（例如，对于肺部病例，IMPT的CI为0.69，ARC的CI为0.80），与IMPT相比，ARC的优化自由度更高。据我们所知，我们的新的MMU优化方法通过SCD可以有效地处理非凸性从大MMU阈值，没有目前的方法可以解决。因此，我们已经通过SCD开发了一种独特的MMU优化算法，该算法可以用于高效的IMPT、质子ARC和其他需要大MMU阈值的粒子RT应用（例如，用于输送高剂量率或/和大量的点）。
Deliverable proton spots are subject to the minimum monitor-unit (MMU) constraint. The MMU optimization problem with relatively large MMU threshold remains mathematically challenging due to its strong nonconvexity. However, the MMU optimization is fundamental to proton radiotherapy (RT), including efficient IMPT and proton arc delivery (ARC). This work aims to develop a new optimization algorithm that is effective in solving the MMU problem. Our new algorithm is primarily based on stochastic coordinate decent (SCD) method. It involves three major steps: first to decouple the determination of active sets for dose-volume-histogram (DVH) planning constraints from the MMU problem via iterative convex relaxation method; second to handle the nonconvexity of the MMU constraint via SCD to localize the index set of nonzero spots; third to solve convex subproblems projected to this convex set of nonzero spots via projected gradient descent method. Our new method SCD is validated and compared with alternating direction method of multipliers (ADMM) for IMPT and ARC. The results suggest SCD had better plan quality than ADMM, e.g., the improvement of conformal index (CI) from 0.56 to 0.69 during IMPT, and from 0.28 to 0.80 during ARC for the lung case. Moreover, SCD successfully handled the nonconvexity from large MMU threshold that ADMM failed to handle, in the sense that (1) the plan quality from ARC was worse than IMPT (e.g., CI was 0.28 with IMPT and 0.56 with ARC for the lung case), when ADMM was used; (2) in contrast, with SCD, ARC achieved better plan quality than IMPT (e.g., CI was 0.69 with IMPT and 0.80 with ARC for the lung case), which is compatible with more optimization degrees of freedom from ARC compared to IMPT. To the best of our knowledge, our new MMU optimization method via SCD can effectively handle the nonconvexity from large MMU threshold that none of the current methods can solve. Therefore, we have developed a unique MMU optimization algorithm via SCD that can be used for efficient IMPT, proton ARC, and other particle RT applications where large MMU threshold is desirable (e.g., for the delivery of high dose rates or/and a large number of spots).
登录
查看更多内容
影响因子：
3.8
作者：
Gao, Hao;Lin, Bowen;Bradley, Jeffery
通讯作者：
Bradley, Jeffery
影响因子：
3.8
作者：
Howard, Michelle;Beltran, Chris;Herman, Michael G.
通讯作者：
Herman, Michael G.
影响因子：
3.8
作者：
Wu, QW;Mohan, R
通讯作者：
Mohan, R
影响因子：
3.5
作者：
Cao W;Lim G;Li X;Li Y;Zhu XR;Zhang X
通讯作者：
Zhang X
DOI：
10.1016/j.ijrobp.2016.08.049
发表时间：
2016-12-01
影响因子：
7
作者：
Ding, Xuanfeng;Li, Xiaoqiang;Yan, Di
通讯作者：
Yan, Di