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-  2018 

一种基于梯度信息的直接子野优化算法

DOI: doi:10.7507/1001-5515.201609041

Keywords: 直接子野优化算法, 模拟退火算法, 带约束最小存储拟牛顿算法, 调强放射治疗

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Abstract:

针对传统直接子野优化算法(DAO)收敛速度慢、易停滞、全局搜索能力低的缺点,本文提出一种基于梯度信息的直接子野优化方法(GDAO)。在 GDAO 中分别采用不同的优化方法对子野形状和子野权重进行迭代优化。首先为提高子野形状优化时每次搜索的有效性,对传统模拟退火算法(SA)进行了改进,将梯度信息融合在 SA 算法中。采用基于梯度的 SA 法确定子野形状,并在优化同时充分考虑多叶准直器(MLC)叶片间的约束条件,保证优化后的子野形状满足临床放射治疗的要求。之后再利用计算量少、迭代代价低、收敛快且稳定的梯度类具有求解大规模约束优化问题能力的带约束最小存储拟牛顿算法(L-BFGS-B)优化子野权重。实验结果表明,与传统 SA 算法相比,新算法计算时间减少了 15.90%,同时得到的治疗方案靶区最低剂量提高了 0.29%,最高剂量降低了 0.45%;危及器官膀胱最高剂量降低了 0.25%;危及器官直肠最高剂量降低了 0.09%,说明在调强放射治疗(IMRT)中采用 GDAO 方法直接优化子野,可在短时间内得到满足临床要求并可直接实施照射的治疗方案,具有较好的临床实用价值

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