Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Department of Radiation Oncology, Maastricht University Medical Center, Maastricht, The Netherlands.
Med Phys. 2023 Jan;50(1):633-642. doi: 10.1002/mp.15897. Epub 2022 Sep 4.
The importance of robust proton treatment planning to mitigate the impact of uncertainty is well understood. However, its computational cost grows with the number of uncertainty scenarios, prolonging the treatment planning process.
We developed a fast and scalable distributed optimization platform that parallelizes the robust proton treatment plan computation over the uncertainty scenarios.
We modeled the robust proton treatment planning problem as a weighted least-squares problem. To solve it, we employed an optimization technique called the alternating direction method of multipliers with Barzilai-Borwein step size (ADMM-BB). We reformulated the problem in such a way as to split the main problem into smaller subproblems, one for each proton therapy uncertainty scenario. The subproblems can be solved in parallel, allowing the computational load to be distributed across multiple processors (e.g., CPU threads/cores). We evaluated ADMM-BB on four head-and-neck proton therapy patients, each with 13 scenarios accounting for 3 mm setup and 3.5% range uncertainties. We then compared the performance of ADMM-BB with projected gradient descent (PGD) applied to the same problem.
For each patient, ADMM-BB generated a robust proton treatment plan that satisfied all clinical criteria with comparable or better dosimetric quality than the plan generated by PGD. However, ADMM-BB's total runtime averaged about 6 to 7 times faster. This speedup increased with the number of scenarios.
ADMM-BB is a powerful distributed optimization method that leverages parallel processing platforms, such as multicore CPUs, GPUs, and cloud servers, to accelerate the computationally intensive work of robust proton treatment planning. This results in (1) a shorter treatment planning process and (2) the ability to consider more uncertainty scenarios, which improves plan quality.
稳健质子治疗计划对于减轻不确定性影响的重要性已得到充分认识。然而，随着不确定性场景数量的增加，其计算成本也会增加，从而延长治疗计划的过程。
我们开发了一种快速且可扩展的分布式优化平台，该平台通过对不确定性场景进行并行处理来加速稳健质子治疗计划的计算。
我们将稳健质子治疗计划问题建模为加权最小二乘问题。为了解决这个问题，我们采用了一种称为交替方向乘子法的优化技术，带有 Barzilai-Borwein 步长（ADMM-BB）。我们对问题进行了重新表述，以便将主要问题分解为更小的子问题，每个子问题对应一个质子治疗不确定性场景。子问题可以并行求解，从而将计算负载分布在多个处理器（例如，CPU 线程/内核）上。我们在四个头部和颈部质子治疗患者上评估了 ADMM-BB，每个患者有 13 个场景，考虑了 3mm 的设置和 3.5%的范围不确定性。然后，我们将 ADMM-BB 的性能与应用于相同问题的投影梯度下降（PGD）进行了比较。
对于每个患者，ADMM-BB 生成了一个稳健质子治疗计划，该计划满足所有临床标准，并且在剂量学质量上与 PGD 生成的计划相当或更好。然而，ADMM-BB 的总运行时间平均快约 6 到 7 倍。这种加速随着场景数量的增加而增加。
ADMM-BB 是一种强大的分布式优化方法，它利用并行处理平台，如多核 CPU、GPU 和云服务器，来加速稳健质子治疗计划的计算密集型工作。这导致（1）治疗计划过程更短，（2）能够考虑更多的不确定性场景，从而提高计划质量。
