Integrated beam orientation and scanning-spot optimization in intensity-modulated proton therapy for brain and unilateral head and neck tumors.
Integrated beam orientation and scanning-spot optimization in intensity-modulated proton therapy for brain and unilateral head and neck tumors.
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DOI：
10.1002/mp.12788
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发表时间：
2018-04
期刊：
影响因子：
3.8
通讯作者：
Sheng K
中科院分区：
文献类型：
--
作者：
Gu W;O'Connor D;Nguyen D;Yu VY;Ruan D;Dong L;Sheng K
强度调制质子治疗（IMPT）是最先进的质子放射治疗方法。以往的研究主要集中在人工选择波束角度的扫描点优化上。由于计算的复杂性，无法实现同时优化光束方向和光斑图的潜在效益。在这项研究中，我们开发了一种新的集成束取向优化（BOO）和扫描点优化算法用于强度调制质子治疗（IMPT）。本研究纳入1例脑脊索瘤和3例单侧头颈部患者，最大靶尺寸为112.49 cm3。均匀分布在4个立体面上的1162个非共面候选光束被纳入优化。对于每个候选光束，计算覆盖PTV的所有扫描点的铅笔光束剂量和余量。光束角选择和光斑强度优化问题包括三个项：剂量保真度项，用于补偿PTV和OAR剂量与理想剂量分布的偏差；1范数稀疏项，减少活动点数量，提高传递效率；一种群稀疏性项，将有源波束的数量控制在2到4之间。对群稀疏项，凸L2,1-范数和非凸L2,1/2-范数进行了检验。对于剂量保真度项，采用二次函数和线性化等效均匀剂量（LEUD）代价函数。采用快速迭代收缩阈值算法求解优化问题。IMPT BOO方法在3例头颈部患者和1例颅底脊索瘤患者中进行了试验。结果与使用柱生成选择梁或手动选择梁创建的IMPT方案进行了比较。L2,1范数方案选择空间聚合梁，表明使用该范数的潜在简并。L2,1/2-范数能够选择空间分离的光束，与理想剂量的偏差较小。在保持相同PTV覆盖率的情况下，在L2、1/2-norm方案中，二次成本函数和LEUD成本函数下，OAR的[平均剂量、最大剂量]分别平均降低处方剂量的[2.38%、4.24%]和[2.32%、3.76%]。L2、1/2组稀疏度方案在剂量学上也优于柱生成方案。除了光束方向选择外，还观察到光斑稀疏现象。一般情况下，使用二次代价函数时，所选波束中30%~60%的点保持有效。利用LEUD成本函数，活性斑的比例在35%~85%之间。BOO-IMPT运行时间大约为20分钟。这项工作展示了在单一数学框架中集成非共面BOO和扫描点优化的第一个IMPT方法。该方法计算效率高，剂量学上优越，并产生易于交付的IMPT计划。
Intensity Modulated Proton Therapy (IMPT) is the state-of-the-art method of delivering proton radiotherapy. Previous research has been mainly focused on optimization of scanning spots with manually selected beam angles. Due to the computational complexity, the potential benefit of simultaneously optimizing beam orientations and spot pattern could not be realized. In this study, we developed a novel integrated beam orientation optimization (BOO) and scanning-spot optimization algorithm for intensity modulated proton therapy (IMPT). A brain chordoma and three unilateral head-and-neck patients with a maximal target size of 112.49 cm3 were included in this study. 1162 non-coplanar candidate beams evenly distributed across 4 steradians were included in the optimization. For each candidate beam, the pencil-beam doses of all scanning-spots covering the PTV and a margin were calculated. The beam angle selection and spot intensity optimization problem was formulated to include three terms: a dose fidelity term to penalize the deviation of PTV and OAR doses from ideal dose distribution; an L1-norm sparsity term to reduce the number of active spots and improve delivery efficiency; a group sparsity term to control the number of active beams to between 2 and 4. For the group sparsity term, convex L2,1-norm and nonconvex L2,1/2-norm were tested. For the dose fidelity term, both quadratic function and linearized equivalent uniform dose (LEUD) cost function were implemented. The optimization problem was solved using the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). The IMPT BOO method was tested on three head-and-neck patients and one skull base chordoma patient. The results were compared with IMPT plans created using column generation selected beams or manually selected beams. The L2,1-norm plan selected spatially aggregated beams, indicating potential degeneracy using this norm. L2,1/2-norm was able to select spatially separated beams and achieve smaller deviation from the ideal dose. In the L2,1/2-norm plans, the [mean dose, maximum dose] of OAR were reduced by an average of [2.38%, 4.24%] and[2.32%, 3.76%] of the prescription dose for the quadratic and LEUD cost function, respectively, compared with the IMPT plan using manual beam selection while maintaining the same PTV coverage. The L2,1/2 group sparsity plans were dosimetrically superior to the column generation plans as well. Besides beam orientation selection, spot sparsification was observed. Generally, with the quadratic cost function, 30%~60% spots in the selected beams remained active. With the LEUD cost function, the percentages of active spots were in the range of 35%~85%. The BOO-IMPT run time was approximately 20 minutes. This work shows the first IMPT approach integrating non-coplanar BOO and scanning-spot optimization in a single mathematical framework. This method is computationally efficient, dosimetrically superior and produces delivery-friendly IMPT plans.
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