Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, 90095, USA.
Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Med Phys. 2020 Jun;47(5):2072-2084. doi: 10.1002/mp.14083. Epub 2020 Mar 13.
Spot-scanning proton arc therapy (SPAT) is an emerging modality to improve plan conformality and delivery efficiency. A greedy and heuristic method is proposed in the existing SPAT algorithm to select energy layers and sequence energy switching with gantry rotation, which does not promise optimality in either dosimetry or efficiency. We aim to develop a method to solve the energy layer switching and dosimetry optimization problems in an integrated framework for SPAT.
In an integrated approach, energy layer optimization for spot-scanning proton arc therapy (ELO-SPAT) is formulated with a dose fidelity term, a group sparsity regularization, a log barrier regularization, and an energy sequencing (ES) penalty. The combination of L2,1/2-norm group sparsity regularization and log barrier function allows one energy layer being selected per control point. The ES regularization term sorts the delivery sequence from high energy to low energy to reduce the total energy layer switching time (ELST) and subsequently the total delivery time. Within the ES penalty, the gradient of layer weights between adjacent beams is first calculated along beam direction and then along energy direction. The gradients indicate energy switch patterns between two adjacent beams. The time-wise costly energy switch-up is more heavily penalized in the ES term. This ELO-SPAT method was tested on one frontal base-of-skull (BOS) patient, one chordoma (CHDM) patient with a simultaneous integrated boost, one bilateral head-and-neck (H&N) patient, and one lung (LNG) patient. We compared ELO-SPAT with intensity-modulated proton therapy (IMPT) using discrete beams and SPArc by Ding et al. For the two arc algorithms, both the plans with and without energy sequencing were created and compared.
Energy layer optimization for spot-scanning proton arc therapy reduced the runtime of optimization by 84% on average compared with the greedy SPArc method. In both the ELO-SPAT plans with and without ES, one energy layer per control point was selected. Without ES regularization, the energy sequence was arbitrary, with around 40-60 switch-up for the tested cases. After adding ES regularization, the number of energy switch-up was reduced to less than 20. Compared with the energy sequenced SPArc plans, the ELO-SPAT plans with ES led to 24% less total ELST for synchrotron plans and 14% less for cyclotron plans. Both the ELO-SPAT and SPArc plans achieved better sparing compared with the IMPT plans for most Organs-at-risks (OARs), with or without ES. Without ES, the ELO-SPAT plans achieved further improvement of the OARs compared with the SPArc plans, with an averaged reduction of OAR [Dmean, Dmax] by [1.57, 3.34] GyRBE. Adding the ES regularization degraded the plan quality, but the ELO-SPAT plans still had comparable or slightly better sparing than the SPArc plans with ES, with an averaged reduction of OAR [Dmean, Dmax] by [1.42, 2.34] GyRBE.
We developed a computationally efficient spot-scanning proton arc optimization method, which solved energy layer selection and sequencing in an integrated framework, generating plans with good dosimetry and high delivery efficiency.
点扫描质子弧治疗（SPAT）是一种新出现的方法，可以提高计划的适形性和递送效率。现有的 SPAT 算法中提出了一种贪婪和启发式方法，用于选择能层并结合机架旋转切换能层，这在剂量学或效率方面都不能保证最优。我们旨在开发一种方法，以在 SPAT 的综合框架中解决能层切换和剂量优化问题。
在综合方法中，点扫描质子弧治疗的能层优化（ELO-SPAT）通过剂量保真度项、组稀疏正则化、对数障碍正则化和能层排序（ES）罚函数来制定。L2,1/2-范数组稀疏正则化和对数障碍函数的组合允许每个控制点选择一个能层。ES 正则化项按从高到低的能量顺序对治疗顺序进行排序，以减少总能层切换时间（ELST），从而减少总治疗时间。在 ES 罚函数内，首先沿射束方向计算相邻射束之间的层权重梯度，然后沿能量方向计算。梯度表示两个相邻射束之间的能量切换模式。时间上昂贵的能量上切换在 ES 项中受到更严厉的惩罚。该 ELO-SPAT 方法在一个前颅底（BOS）患者、一个同时进行综合增量的脊索瘤（CHDM）患者、一个双侧头颈部（H&N）患者和一个肺（LNG）患者中进行了测试。我们将 ELO-SPAT 与基于离散射束的强度调制质子治疗（IMPT）和 Ding 等人的 SPArc 进行了比较。对于这两种弧形算法，我们都创建并比较了有和没有能层排序的计划。
与贪婪的 SPArc 方法相比，点扫描质子弧治疗的能层优化平均将优化运行时间缩短了 84%。在有和没有 ES 的 ELO-SPAT 计划中，每个控制点都选择了一个能层。没有 ES 正则化时，能量序列是任意的，测试病例中有大约 40-60 次上切换。加入 ES 正则化后，上切换次数减少到 20 次以下。与有 ES 的能量排序 SPArc 计划相比，ELO-SPAT 计划在同步加速器计划中减少了 24%的总 ELST，在回旋加速器计划中减少了 14%的总 ELST。无论是有还是没有 ES，ELO-SPAT 和 SPArc 计划都比 IMPT 计划在大多数危及器官（OARs）中提供了更好的保护，无论是否有 ES。没有 ES 时，ELO-SPAT 计划与 SPArc 计划相比，进一步改善了 OARs 的剂量学，平均减少 OAR[Dmean，Dmax]1.57-3.34GyRBE。加入 ES 正则化会降低计划质量，但 ELO-SPAT 计划仍然具有与 ES 计划相似或略好的保护效果，OAR[Dmean，Dmax]的平均减少量为 1.42-2.34GyRBE。
我们开发了一种计算效率高的点扫描质子弧治疗优化方法，该方法在综合框架中解决了能层选择和排序问题，生成了具有良好剂量学和高效率的治疗计划。
