A practical algorithm for VMAT optimization using column generation techniques
A practical algorithm for VMAT optimization using column generation techniques
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DOI：
10.1002/mp.15776
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发表时间：
2022-06-07
期刊：
影响因子：
3.8
通讯作者：
Lu，Bo
中科院分区：
文献类型：
--
作者：
Wang，Yuanbo;Liu，Hongcheng;Lu，Bo
目的容量调节弧疗(VMAT)的逆规划问题作为一个具有挑战性但又十分重要的优化问题，一直是研究的热点。列生成(CG)型方法是迄今为止最有效的求解方案之一。然而，它往往依赖于导致产出与实际可行计划之间存在巨大差距的简化。本文提出了一种新的列生成(NCG)方法，使规划结果更接近实际。方法NCG算法配备了多个新的质量增强和计算便利模块：(1)对剂量率和治疗时间进行灵活的约束，以分别适应机器能力和规划者的偏好；(2)引入交叉控制点中间孔径模拟，以更好地符合底层物理；(3)采用新的定价和剪枝子程序，以获得更好的优化输出。为了评估这种NCG的有效性，使用所提出的NCG计算了五个VMAT计划，即三个前列腺病例和两个头颈部病例。结果NCG生成的计划质量明显好于基准规划算法。对于前列腺病例，NCG计划满足所有计划目标体积(PTV)标准，而CG计划在所有病例的PTV超过9GY或更多的D10%标准上失败。同样，对于头颈部病例，NCG计划满足所有PTVS标准，而CG计划在D10%的PTVS标准上失败，所有病例的PTVS超过3GY或更多，1例患者的脊髓和脑干的最大剂量标准均超过13Gy.结论NCG继承了传统CG的计算优势，同时更真实地刻画了机器性能和底层物理特性。NCG的产出解决方案更接近实际执行。
PurposeAs a challenging but important optimization problem, the inverse planning for volumetric modulated arc therapy (VMAT) has attracted much research attention. The column generation (CG) type method is so far one of the most effective solution schemes. However, it often relies on simplifications leading to significant gaps between the output and the actual feasible plan. This paper presents a novel column generation (NCG) approach to push the planning results substantially closer to practice.MethodsThe proposed NCG algorithm is equipped with multiple new quality‐enhancing and computation‐facilitating modules as below: (1) Flexible constraints are enabled on both dose rates and treatment time to adapt to machine capabilities as well as planner's preferences, respectively; (2) a cross‐control‐point intermediate aperture simulation is incorporated to better conform to the underlying physics; (3) new pricing and pruning subroutines are adopted to achieve better optimization outputs. To evaluate the effectiveness of this NCG, five VMAT plans, that is, three prostate cases and two head‐and‐neck cases, were computed using proposed NCG. The planning results were compared with those yielded by a historical benchmark planning scheme.ResultsThe NCG generated plans of significantly better quality than the benchmark planning algorithm. For prostate cases, NCG plans satisfied all planning target volume (PTV) criteria whereas CG plans failed on D10% criteria of PTVs for over 9 Gy or more on all cases. For head‐and‐neck cases, again, NCG plans satisfied all PTVs criteria while CG plans failed on D10% criteria of PTVs for over 3 Gy or more on all cases as well as the max dose criteria of both cord and brain stem for over 13 Gy on one case. Moreover, the pruning scheme was found to be effective in enhancing the optimization quality.ConclusionsThe proposed NCG inherits the computational advantages of the traditional CG, while capturing a more realistic characterization of the machine capability and underlying physics. The output solutions of the NCG are substantially closer to practical implementation.