Purpose: To introduce a new optimization algorithm that improves DVH results and is designed for the type of heterogeneous dose distributions that occur in brachytherapy. Methods: The new optimization algorithm is based on a prior mathematical approach that uses mean doses of the DVH metric tails. The prior mean dose approach is referred to as conditional value-at-risk (CVaR), and unfortunately produces noticeably worse DVH metric results than gradient-based approaches. We have improved upon the CVaR approach, using the so-called Truncated CVaR (TCVaR), by excluding the hottest or coldest voxels in the structure from the calculations of the mean dose of the tail. Our approach applies an iterative sequence of convex approximations to improve the selection of the excluded voxels. Data Envelopment Analysis was used to quantify the sensitivity of TCVaR results to parameter choice and to compare the quality of a library of 256 TCVaR plans created for each of prostate, ...

中文翻译：

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一种新的 HDR 近距离放射治疗优化算法，可改进基于 DVH 的计划：截断条件风险价值 (TCVaR)

目的：介绍一种新的优化算法，可改善 DVH 结果，并针对近距离放射治疗中出现的异质剂量分布类型而设计。方法：新的优化算法基于使用 DVH 度量尾部的平均剂量的先前数学方法。先前的平均剂量方法被称为条件风险值 (CVaR)，不幸的是，它产生的 DVH 度量结果比基于梯度的方法明显更差。我们改进了 CVaR 方法，使用所谓的截断 CVaR (TCVaR)，通过从尾部平均剂量的计算中排除结构中最热或最冷的体素。我们的方法应用凸逼近的迭代序列来改进排除体素的选择。