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Phys Med Biol. 2019 Apr 8;64(8):085008. doi: 10.1088/1361-6560/ab091c.
Volumetric-modulated arc therapy (VMAT) treatment planning is an efficient treatment technique with a high degree of flexibility in terms of dose rate, gantry speed, and aperture shapes during rotation around the patient. However, the dynamic nature of VMAT results in a large-scale nonconvex optimization problem. Determining the priority of the tissues and voxels to obtain clinically acceptable treatment plans poses additional challenges for VMAT optimization. The main purpose of this paper is to develop an automatic planning approach integrating dose-volume histogram (DVH) criteria in direct aperture optimization for VMAT, by adjusting the model parameters during the algorithm. The proposed algorithm is based on column generation, an optimization technique that sequentially generates the apertures and optimizes the corresponding intensities. We take the advantage of iterative procedure in this method to modify the weight vector of the penalty function based on the DVH criteria and decrease the use of trial-and-error in the search for clinically acceptable plans. We evaluate the efficiency of the algorithm and treatment quality using a clinical prostate case and a challenging head-and-neck case. In both cases, we generate 15 random initial weight vectors to assess the robustness of the algorithm. In the prostate case, our methodology obtained clinically acceptable plans in all instances with only a 10% increase in the computational time, while simple VMAT optimization found just three acceptable plans. To have an idea with respect to the existing software, we compared the obtained DVH to a commercial software. The quality of the diagrams of the proposed method, especially for the healthy tissues, is significantly better while the computational time is less. In the head-and-neck case, 93.3% of the clinically acceptable plans are obtained while no plan was acceptable in simple VMAT. In sum, the results demonstrate the ability of the proposed optimization algorithm to obtain clinically acceptable plans without human intervention and also its robustness to weight parameters. Moreover, our proposed weight adjustment procedure proves to reduce the symmetry in the solution space and the time required for the post-optimization phase.
容积旋转调强弧形治疗（VMAT）计划是一种高效的治疗技术，在围绕患者旋转时，具有很高的剂量率、机架速度和孔径形状的灵活性。然而，VMAT 的动态性质导致了一个大规模的非凸优化问题。确定组织和体素的优先级，以获得临床可接受的治疗计划，为 VMAT 优化带来了额外的挑战。本文的主要目的是开发一种自动规划方法，通过在算法过程中调整模型参数，将剂量-体积直方图（DVH）标准集成到直接孔径优化中。该算法基于列生成，这是一种优化技术，它依次生成孔径并优化相应的强度。我们利用该方法的迭代过程，根据 DVH 标准修改罚函数的权重向量，并减少在寻找临床可接受计划时的试错次数。我们使用一个临床前列腺病例和一个具有挑战性的头颈部病例来评估算法的效率和治疗质量。在这两种情况下，我们生成了 15 个随机初始权重向量，以评估算法的稳健性。在前列腺病例中，我们的方法在所有情况下都获得了临床可接受的计划，计算时间仅增加了 10%，而简单的 VMAT 优化只找到了 3 个可接受的计划。为了了解现有的软件，我们将获得的 DVH 与商业软件进行了比较。与现有软件相比，所提出方法的图表质量，特别是对于健康组织的图表质量，要好得多，而计算时间却更少。在头颈部病例中，93.3%的临床可接受计划是在简单 VMAT 中获得的，而没有计划是可接受的。总之，结果表明，所提出的优化算法能够在没有人为干预的情况下获得临床可接受的计划，并且对权重参数具有稳健性。此外，我们提出的权重调整过程证明可以减少解空间的对称性和后优化阶段所需的时间。
