Suppr超能文献

基于自适应 l(2,1)-最小化的调强放射治疗射束方向优化。

Beam orientation optimization for intensity modulated radiation therapy using adaptive l(2,1)-minimization.

机构信息

Center for Advanced Radiotherapy Technologies and Department of Radiation Oncology, University of California San Diego, La Jolla, CA 92037-0843, USA.

出版信息

Phys Med Biol. 2011 Oct 7;56(19):6205-22. doi: 10.1088/0031-9155/56/19/004. Epub 2011 Sep 2.

Abstract

Beam orientation optimization (BOO) is a key component in the process of intensity modulated radiation therapy treatment planning. It determines to what degree one can achieve a good treatment plan in the subsequent plan optimization process. In this paper, we have developed a BOO algorithm via adaptive l(2, 1)-minimization. Specifically, we introduce a sparsity objective function term into our model which contains weighting factors for each beam angle adaptively adjusted during the optimization process. Such an objective function favors a small number of beam angles. By optimizing a total objective function consisting of a dosimetric term and the sparsity term, we are able to identify unimportant beam angles and gradually remove them without largely sacrificing the dosimetric objective. In one typical prostate case, the convergence property of our algorithm, as well as how beam angles are selected during the optimization process, is demonstrated. Fluence map optimization (FMO) is then performed based on the optimized beam angles. The resulting plan quality is presented and is found to be better than that of equiangular beam orientations. We have further systematically validated our algorithm in the contexts of 5-9 coplanar beams for five prostate cases and one head and neck case. For each case, the final FMO objective function value is used to compare the optimized beam orientations with the equiangular ones. It is found that, in the majority of cases tested, our BOO algorithm leads to beam configurations which attain lower FMO objective function values than those of corresponding equiangular cases, indicating the effectiveness of our BOO algorithm. Superior plan qualities are also demonstrated by comparing DVH curves between BOO plans and equiangular plans.

摘要

束流方向优化(BOO)是调强放射治疗计划制定过程中的一个关键环节。它决定了在随后的计划优化过程中能够在多大程度上实现一个好的治疗计划。在本文中,我们通过自适应 l(2,1)-最小化开发了一种 BOO 算法。具体来说,我们在模型中引入了一个稀疏性目标函数项,其中包含在优化过程中自适应调整的每个射束角度的加权因子。这样的目标函数有利于使用少量的射束角度。通过优化由剂量学项和稀疏性项组成的总目标函数,我们能够识别不重要的射束角度,并在不大大牺牲剂量学目标的情况下逐渐去除它们。在一个典型的前列腺病例中,演示了我们算法的收敛特性,以及在优化过程中如何选择射束角度。然后基于优化的射束角度进行通量图优化(FMO)。呈现了生成的计划质量,并发现其优于等角射束方向。我们进一步在五个前列腺病例和一个头颈部病例的五共面射束背景下系统地验证了我们的算法。对于每个病例,最终的 FMO 目标函数值用于将优化后的射束方向与等角的射束方向进行比较。结果发现,在所测试的大多数情况下,我们的 BOO 算法导致的射束配置比相应的等角情况获得更低的 FMO 目标函数值,表明了我们的 BOO 算法的有效性。通过比较 BOO 计划和等角计划之间的 DVH 曲线,还展示了更好的计划质量。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验