背景:虽然在运动管理方面最小化计划交付时间对质子治疗有益,患者舒适度,和处理吞吐量,它通常会与优化计划质量进行权衡。计划交付时间的一个关键组成部分是能量转换时间,这大约与能量层的数量成正比,也就是说,基数。
目的:这项工作旨在开发一种新颖的优化方法,该方法可以有效地计算计划质量和能量层基数之间的pareto曲面,让计划者通过这种质量和效率权衡,并选择平衡权衡的适当计划。
方法:提出了一种新的IMPT方法CARD,该方法(1)明确地将能量层基数最小化作为优化目标,和(2)自动地按能量层数的降序顺序生成一组计划。能量层基数通过具有上限的l1,0范数正则化来惩罚,并且上界单调降低,以计算一系列处理计划,其在质量和效率帕累托表面上的能量层基数逐渐降低。对于任何给定的治疗计划,使用剂量-体积计划目标实施计划最优性,并通过最小监测单位(MMU)约束实施计划可交付性,基于迭代凸松弛的优化求解算法。
结果:与所有能量层(P0)的基准计划相比,新方法CARD得到了验证,和一种叫做MMSEL的最先进的方法,使用前列腺,头颈部(HN),肺,胰腺,肝脏和大脑病例。虽然MMSEL需要劳动密集型和耗时的手动参数调整来生成预定义能量层基数的计划,CARD一次自动高效地计算所有计划,并依次降低预定义的能量层基数。具有可接受的计划质量(即,不超过P0总优化目标值的110%),CARD将能量层数减少到52%(从77减少到40),48%(从135到65),59%(从85到50),67%(从52到35),80%(从50到40),和30%(从66到20),对于前列腺,HN,肺,胰腺,肝脏,和大脑病例,分别,与P0相比,整体计划质量优于MMSEL。此外,由于MMU约束的非凸性,CARD提供了与P0相似甚至更小的优化目标,同时具有更少的能量层数,也就是说,前列腺的55对77,85对135,45对52,25对66,HN,胰腺,和大脑病例,分别。
结论:我们开发了一种新颖的优化算法CARD,可以高效自动地按顺序计算任何给定能量层的一系列治疗计划,这允许计划者浏览计划质量和能量层基数权衡,并选择平衡权衡的适当计划。
BACKGROUND: While minimizing plan delivery time is beneficial for proton therapy in terms of motion management, patient comfort, and treatment throughput, it often poses a tradeoff with optimizing plan quality. A key component of plan delivery time is the energy switching time, which is approximately proportional to the number of energy layers, that is, the cardinality.
OBJECTIVE: This work aims to develop a novel optimization method that can efficiently compute the pareto surface between plan quality and energy layer cardinality, for the planner to navigate through this quality-and-efficiency tradeoff and select the appropriate plan of a balanced tradeoff.
METHODS: A new IMPT method CARD is proposed that (1) explicitly incorporates the minimization of energy layer cardinality as an optimization objective, and (2) automatically generates a set of plans sequentially with a descending order in number of energy layers. The energy layer cardinality is penalized through the l1,0-norm regularization with an upper bound, and the upper bound is monotonically decreased to compute a series of treatment plans with gradually decreased energy layer cardinality on the quality-and-efficiency pareto surface. For any given treatment plan, the plan optimality is enforced using dose-volume planning objectives and the plan deliverability is imposed through minimum-monitor-unit (MMU) constraints, with optimization solution algorithm based on iterative convex relaxation.
RESULTS: The new method CARD was validated in comparison with the benchmark plan of all energy layers (P0), and a state-of-the-art method called MMSEL, using prostate, head-and-neck (HN), lung, pancreas, liver and brain cases. While labor-intensive and time-consuming manual parameter tuning was needed for MMSEL to generate plans of predefined energy layer cardinality, CARD automatically and efficiently computed all plans with sequentially decreasing predefined energy layer cardinality all at once. With the acceptable plan quality (i.e., no more than 110% of total optimization objective value from P0), CARD achieved the reduction of number of energy layers to 52% (from 77 to 40), 48% (from 135 to 65), 59% (from 85 to 50), 67% (from 52 to 35), 80% (from 50 to 40), and 30% (from 66 to 20), for prostate, HN, lung, pancreas, liver, and brain cases, respectively, compared to P0, with overall better plan quality than MMSEL. Moreover, due to the nonconvexity of the MMU constraint, CARD provided the similar or even smaller optimization objective than P0, at the same time with fewer number of energy layers, that is, 55 versus 77, 85 versus 135, 45 versus 52, and 25 versus 66 for prostate, HN, pancreas, and brain cases, respectively.
CONCLUSIONS: We have developed a novel optimization algorithm CARD that can efficiently and automatically compute a series of treatment plans of any given energy layer sequentially, which allows the planner to navigate through the plan-quality and energy-layer-cardinality tradeoff and select the appropriate plan of a balanced tradeoff.