Speedup of lexicographic optimization by superiorization and its applications to cancer radiotherapy treatment
Speedup of lexicographic optimization by superiorization and its applications to cancer radiotherapy treatment
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DOI：
10.1088/1361-6420/33/4/044012
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发表时间：
2017-04-01
期刊：
影响因子：
2.1
通讯作者：
Suss, Philipp
中科院分区：
文献类型：
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作者：
Bonacker, Esther;Gibali, Aviv;Suss, Philipp
多准则优化问题出现在许多真实的生活应用中，例如在癌症放射治疗中，特别是在调强放射治疗（IMRT）中。在这项工作中，我们专注于优化问题的多个目标，根据其重要性排名。我们解决这些问题的数值结合字典优化与我们最近提出的水平集方案，这产生了一系列的辅助凸可行性问题，通过投影方法解决这里。投影使我们能够联合收割机新引入的优越化方法与多准则优化方法，以加快计算，同时保证收敛的优化。我们证明了我们的计划与一个简单的2D学术的例子（在文献中使用），并提出了结果从四个真实的头颈部的情况下，调强放射治疗（放射肿瘤学的路德维希-马克西米利安大学，慕尼黑，德国）的两个不同的选择superiorization参数集适合产生快速收敛，为每种情况下单独或强大的行为，所有四种情况。
Multicriteria optimization problems occur in many real life applications, for example in cancer radiotherapy treatment and in particular in intensity modulated radiation therapy (IMRT). In this work we focus on optimization problems with multiple objectives that are ranked according to their importance. We solve these problems numerically by combining lexicographic optimization with our recently proposed level set scheme, which yields a sequence of auxiliary convex feasibility problems; solved here via projection methods. The projection enables us to combine the newly introduced superiorization methodology with multicriteria optimization methods to speed up computation while guaranteeing convergence of the optimization. We demonstrate our scheme with a simple 2D academic example (used in the literature) and also present results from calculations on four real head neck cases in IMRT (Radiation Oncology of the Ludwig-Maximilians University, Munich, Germany) for two different choices of superiorization parameter sets suited to yield fast convergence for each case individually or robust behavior for all four cases.