标题： 无需场景的鲁棒优化算法用于IMRT和IMPT治疗计划制定 显示英文标题

标题： Scenario-free robust optimization algorithm for IMRT and IMPT treatment planning

摘要： 针对调强质子治疗（IMPT）和调强放射治疗（IMRT）的鲁棒性治疗计划算法，通过显式包含误差场景，可以降低传递剂量分布的不确定性。由于维度灾难，此类算法的应用可能会变得计算上不可行。本研究提出了一种无需场景的概率鲁棒优化算法，克服了传统鲁棒性算法在运行时间和内存方面的限制。无需场景的方法在预期剂量分布和总方差上最小化成本函数。这些量的计算依赖于预先计算的预期剂量影响矩阵和总方差影响矩阵，因此在优化过程中不需要存储任何场景。该算法是在matRad中开发的，并在几种优化配置中对光子和质子照射计划进行了测试。使用传统的鲁棒优化算法和基于边距的方法作为参考，以计划质量、鲁棒性和计算工作量为基准，评估无需场景算法的性能。无需场景的方法实现了与传统鲁棒优化算法相当的计划质量，并且在定义方差减少目标时减少了选定结构中的标准差。避免存储单个场景信息允许包含任意数量的误差场景。观察到的优化时间与所包含场景的数量无关，与名义上的非鲁棒算法兼容，并且显著低于传统的鲁棒方法。这些特性使得无需场景的方法适用于涉及大量误差场景和CT阶段（如4D鲁棒优化）的鲁棒计划优化。 显示英文摘要

摘要： Robust treatment planning algorithms for Intensity Modulated Proton Therapy (IMPT) and Intensity Modulated Radiation Therapy (IMRT) allow for uncertainty reduction in the delivered dose distributions through explicit inclusion of error scenarios. Due to the curse of dimensionality, application of such algorithms can easily become computationally prohibitive. This work proposes a scenario-free probabilistic robust optimization algorithm that overcomes both the runtime and memory limitations typical of traditional robustness algorithms. The scenario-free approach minimizes cost-functions evaluated on expected-dose distributions and total variance. Calculation of these quantities relies on precomputed expected-dose-influence and total-variance-influence matrices, such that no scenarios need to be stored for optimization. The algorithm was developed within matRad and tested in several optimization configurations for photon and proton irradiation plans. A traditional robust optimization algorithm and a margin-based approach are used as a reference to benchmark the performances of the scenario-free algorithm in terms of plan quality, robustness and computational workload. The scenario-free approach achieves plan quality compatible with traditional robust optimization algorithms and it reduces the standard deviation within selected structures when variance reduction objectives are defined. Avoiding the storage of individual scenario information allows for the inclusion of an arbitrary number of error scenarios. The observed optimization time is independent on the number of included scenarios, compatible with a nominal, non-robust algorithm and significantly lower than the traditional robust approach. These properties make the scenario-free approach suitable for the optimization of robust plans involving a high number of error scenarios and CT phases as 4D robust optimization.