立体定向放射外科治疗计划中扇区持续时间Optimization的多目标方法
A Multiobjective Approach for Sector Duration Optimization in Stereotactic Radiosurgery Treatment Planning
摘要：
扇区持续时间优化（SDO）是伽玛刀立体定向放射外科治疗计划中的一个问题。给定一组等中心点位置，SDO旨在选择准直器尺寸配置及其照射时间，使得靶组织在合理的治疗时间内接受规定剂量，而附近的健康组织则不受影响。我们提出了一个多目标的线性规划模型SDO产生不同的解决方案，使临床医生可以选择最合适的治疗。我们开发了一个通用的两阶段解决方案的基础上解决多目标最优化模型，其目的是系统地增加获得高质量的解决方案的数量，而不是进行传统的统一搜索的ε约束方法的策略。为了进一步提高解决方案的质量，加快程序，我们纳入了一些一般和特定问题的增强。此外，我们提出了一个替代版本的两阶段策略，它利用机器学习工具来减少计算工作量。在我们对8个先前处理过的真实测试案例的计算研究中，获得的解决方案的很大一部分优于临床结果和文献中的单目标模型。除了算法增强的显著好处外，我们的实验还说明了机器学习策略的有用性，可以将整体运行时间减少近一半，同时保持或优于临床实践。
英文摘要：
Sector duration optimization (SDO) is a problem arising in treatment planning for stereotactic radiosurgery on Gamma Knife. Given a set of isocenter locations, SDO aims to select collimator size configurations and irradiation times thereof such that target tissues receive prescribed doses in a reasonable amount of treatment time, while healthy tissues nearby are spared. We present a multiobjective linear programming model for SDO to generate a diverse collection of solutions so that clinicians can select the most appropriate treatment. We develop a generic two-phase solution strategy based on the epsilon-constraint method for solving multiobjective optimization models, which aims to systematically increase the number of high-quality solutions obtained, instead of conducting a traditional uniform search. To improve solution quality further and to accelerate the procedure, we incorporate some general and problem-specific enhancements. Moreover, we propose an alternative version of our two-phase strategy, which makes use of machine learning tools to reduce the computational effort. In our computational study on eight previously treated real test cases, a significant portion of obtained solutions outperformed clinical results and those from a single-objective model from the literature. In addition to significant benefits of the algorithmic enhancements, our experiments illustrate the usefulness of machine learning strategies to reduce the overall run times nearly by half while maintaining or besting the clinical practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
