Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Department of Radiation Therapy, BC Cancer, Abbotsford, British Columbia, Canada.
Med Phys. 2023 Jun;50(6):3637-3650. doi: 10.1002/mp.16358. Epub 2023 Mar 29.
Currently, the commercial treatment planning systems for magnetic-resonance guided linear accelerators (MR-linacs) only support step-and-shoot intensity-modulated radiation therapy (IMRT). However, recent studies have shown the feasibility of delivering arc therapy on MR-linacs, which is expected to improve dose distributions and delivery speed. By accurately accounting for the electron return effect in the presence of a magnetic field, a Monte Carlo (MC) algorithm is ideally suited for the inverse treatment planning of this technique.
We propose a novel MC-based continuous aperture optimization (MCCAO) algorithm for volumetric modulated arc therapy (VMAT), including applications to VMAT on MR-linacs and trajectory-based VMAT. A unique feature of MCCAO is that the continuous character of gantry rotation and multileaf collimator (MLC) motion is accounted for at every stage of the optimization.
The optimization process uses a multistage simulation of 4D dose distribution. A phase space is scored at the top surface of the MLC and the energy deposition of each particle history is mapped to its position in this phase space. A progressive sampling method is used, where both MLC leaf positions and monitor unit (MU) weights are randomly changed, while respecting the linac mechanical limits. Due to the continuous nature of the leaf motion, such changes affect not only a single control point, but propagate to the adjacent ones as well, and the corresponding dose distribution changes are accounted for. A dose-volume cost function is used, which includes the MC statistical uncertainty.
We applied our optimization technique to various treatment sites, using standard and flattening-filter-free (FFF) 6 MV beam models, with and without a 1.5 T magnetic field. MCCAO generates deliverable plans, whose dose distributions are in good agreement with measurements on ArcCHECK and stereotactic radiosurgery End-To-End Phantom.
We show that the novel MCCAO method generates VMAT plans that meet clinical objectives for both conventional and MR-linacs.
目前，商用磁共振引导直线加速器（MR-linacs）的治疗计划系统仅支持步进式强度调制放射治疗（IMRT）。然而，最近的研究表明，在 MR-linacs 上实施弧形治疗是可行的，这有望改善剂量分布和治疗速度。通过在磁场存在的情况下准确考虑电子返回效应，蒙特卡罗（MC）算法非常适合该技术的逆向治疗计划。
我们提出了一种新的基于 MC 的连续孔径优化（MCCAO）算法，用于容积调制弧形治疗（VMAT），包括在 MR-linacs 上的 VMAT 和基于轨迹的 VMAT 的应用。MCCAO 的一个独特特点是，在优化的每个阶段都考虑到了机架旋转和多叶准直器（MLC）运动的连续特性。
优化过程使用 4D 剂量分布的多阶段模拟。在 MLC 的顶部表面对相空间进行评分，并将每个粒子历史的能量沉积映射到该相空间中的位置。使用渐进式采样方法，其中 MLC 叶片位置和 MU 权重随机改变，同时遵守直线加速器的机械限制。由于叶片运动的连续性，这种变化不仅会影响单个控制点，还会传播到相邻的控制点，并且会考虑到相应的剂量分布变化。使用剂量-体积成本函数，其中包括 MC 统计不确定性。
我们将我们的优化技术应用于各种治疗部位，使用标准和无均化滤波器（FFF）6 MV 射束模型，有和没有 1.5 T 磁场。MCCAO 生成可交付的计划，其剂量分布与 ArcCHECK 和立体定向放射外科端到端 Phantom 的测量结果非常吻合。
我们表明，新的 MCCAO 方法生成的 VMAT 计划满足传统和 MR-linacs 的临床目标。
