Abstract：
Objective. Propose a highly automated treatment plan re-optimization strategy suitable for online adaptive proton therapy. The strategy includes a rapid re-optimization method that generates quality replans and a novel solution that efficiently addresses the planning constraint infeasibility issue that can significantly prolong the re-optimization process.Approach. We propose a systematic reference point method (RPM) model that minimizes the l-infinity norm from the initial treatment plan in the daily objective space for online re-optimization. This model minimizes the largest objective value deviation among the objectives of the daily replan from their reference values, leading to a daily replan similar to the initial plan. Whether a set of planning constraints is feasible with respect to the daily anatomy cannot be known before solving the corresponding optimization problem. The conventional trial-and-error-based relaxation process can cost a significant amount of time. To that end, we propose an optimization problem that first estimates the magnitude of daily violation of each planning constraint. Guided by the violation magnitude and clinical importance of the constraints, the constraints are then iteratively converted into objectives based on their priority until the infeasibility issue is solved.Main results.The proposed RPM-based strategy generated replans similar to the offline manual replans within the online time requirement for six head and neck and four breast patients. The average targetD95and relevant organ at risk sparing parameter differences between the RPM replans and clinical offline replans were -0.23, -1.62 Gy for head and neck cases and 0.29, -0.39 Gy for breast cases. The proposed constraint relaxation solution made the RPM problem feasible after one round of relaxation for all four patients who encountered the infeasibility issue.Significance. We proposed a novel RPM-based re-optimization strategy and demonstrated its effectiveness on complex cases, regardless of whether constraint infeasibility is encountered.
摘要：
目的：提出一种适用于在线自适应质子治疗的高度自动化的治疗计划重新优化策略。该策略包括一种快速的重新优化方法，该方法可以生成质量重新计划，以及一种新颖的解决方案，该解决方案可以有效地解决计划约束的不可行性问题，可以显着延长重新优化过程。＆＃xD;＆＃xD;方法：我们提出了一种系统的参考点方法（RPM）模型，该模型从每日目标空间中的初始治疗计划中最小化l-infinity范数，以进行在线重新优化。该模型最大限度地减少了每日重新计划的目标与参考值之间的最大目标值偏差，导致类似于最初计划的每日重新计划。
一组规划约束对于日常解剖结构是否可行,在求解相应的优化问题之前无法得知。传统的基于试错的松弛过程可能花费大量时间。为此,我们提出了一个优化问题，首先估计每天违反每个计划约束的程度。在约束的违反程度和临床重要性的指导下，然后，约束根据其优先级迭代地转换为目标，直到不可行性问题得到解决。＆＃xD;＆＃xD;主要结果：拟议的基于RPM的策略在六个头颈部和四个乳房患者的在线时间要求内生成了类似于离线手动重新计划的重新计划。RPM重新计划和临床离线重新计划之间的平均目标$D_{95}$和相关的危险器官保留参数差异对于头颈部病例为-0.23，-1.62Gy，对于乳腺病例为0.29，-0.39Gy。对于遇到不可行性问题的所有四名患者，所提出的约束松弛解决方案使RPM问题在一轮松弛后变得可行。＆＃xD;＆＃xD;意义：我们提出了一种新颖的基于RPM的重新优化策略，并证明了其在复杂情况下的有效性，无论是否遇到约束不可行。
