Cristoforetti Remo, Hardt Jennifer Josephine, Wahl Niklas
Department of Medical Physics in Radiation Oncology, German Cancer Research Center - DKFZ, Heidelberg, Germany.
Heidelberg Institute for Radiation Oncology - HIRO, Heidelberg, Germany.
Med Phys. 2025 Jul;52(7):e17905. doi: 10.1002/mp.17905. Epub 2025 May 25.
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 is 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 performance of the scenario-free algorithm in terms of plan quality, robustness, and computational workload.
The implemented scenario-free approach achieves plan quality similar to traditional robust optimization algorithms, and it reduces the distribution of standard deviation within selected structures when variance reduction objectives are defined. Avoiding the storage of individual scenario information allows for the solution of treatment plan optimization problems, including an arbitrary number of error scenarios. The observed computational time required for optimization is close to a nominal, non-robust algorithm and substantially lower compared to the traditional robust approach. Estimated gains in relative runtime range from approximately - times with respect to the traditional approach.
This work introduces a novel scenario-free optimization approach relying on the precomputation of probabilistic quantities while preserving compatibility with state-of-the-art uncertainty modeling. The measured runtime and memory footprint are independent of the number of included error scenarios and similar to those of non-robust margin-based optimization algorithms, while achieving the required dose and robustness specifications under multiple different optimization conditions. These properties make the scenario-free approach suitable and beneficial for 3D and 4D robust optimization involving a high number of error scenarios and/or CT phases.
用于调强质子治疗（IMPT）和调强放射治疗（IMRT）的强大治疗计划算法，通过明确纳入误差情况，可减少所交付剂量分布的不确定性。由于维度灾难，此类算法的应用很容易在计算上变得难以承受。
本研究提出一种无场景概率鲁棒优化算法，该算法克服了传统鲁棒算法典型的运行时和内存限制。
无场景方法使基于预期剂量分布和总方差评估的成本函数最小化。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，因此无需存储场景进行优化。该算法在matRad中开发，并在光子和质子照射计划的几种优化配置中进行测试。使用传统鲁棒优化算法和基于边缘的方法作为参考，在计划质量、鲁棒性和计算工作量方面对无场景算法的性能进行基准测试。
所实现的无场景方法实现了与传统鲁棒优化算法相似的计划质量，并且当定义方差减少目标时，它减少了所选结构内的标准差分布。避免存储单个场景信息允许解决治疗计划优化问题，包括任意数量的误差情况。观察到的优化所需计算时间接近标称的非鲁棒算法，与传统鲁棒方法相比大幅降低。相对于传统方法，估计相对运行时的增益范围约为 - 倍。
本研究引入了一种新颖的无场景优化方法，该方法依赖于概率量的预先计算，同时保持与最新不确定性建模的兼容性。测量的运行时和内存占用与所包含误差情况的数量无关，并且与基于边缘的非鲁棒优化算法相似，同时在多种不同优化条件下实现所需的剂量和鲁棒性规范。这些特性使无场景方法适用于并有利于涉及大量误差情况和/或CT相位的3D和4D鲁棒优化。
