Nguyen Dan, O'Connor Daniel, Yu Victoria Y, Ruan Dan, Cao Minsong, Low Daniel A, Sheng Ke
Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095.
Department of Mathematics, University of California Los Angeles, Los Angeles, California 90095.
Med Phys. 2015 Apr;42(4):1858-70. doi: 10.1118/1.4915286.
The advent of automated beam orientation and fluence optimization enables more complex intensity modulated radiation therapy (IMRT) planning using an increasing number of fields to exploit the expanded solution space. This has created a challenge in converting complex fluences to robust multileaf collimator (MLC) segments for delivery. A novel method to regularize the fluence map and simplify MLC segments is introduced to maximize delivery efficiency, accuracy, and plan quality.
In this work, we implemented a novel approach to regularize optimized fluences in the dose domain. The treatment planning problem was formulated in an optimization framework to minimize the segmentation-induced dose distribution degradation subject to a total variation regularization to encourage piecewise smoothness in fluence maps. The optimization problem was solved using a first-order primal-dual algorithm known as the Chambolle-Pock algorithm. Plans for 2 GBM, 2 head and neck, and 2 lung patients were created using 20 automatically selected and optimized noncoplanar beams. The fluence was first regularized using Chambolle-Pock and then stratified into equal steps, and the MLC segments were calculated using a previously described level reducing method. Isolated apertures with sizes smaller than preset thresholds of 1-3 bixels, which are square units of an IMRT fluence map from MLC discretization, were removed from the MLC segments. Performance of the dose domain regularized (DDR) fluences was compared to direct stratification and direct MLC segmentation (DMS) of the fluences using level reduction without dose domain fluence regularization.
For all six cases, the DDR method increased the average planning target volume dose homogeneity (D95/D5) from 0.814 to 0.878 while maintaining equivalent dose to organs at risk (OARs). Regularized fluences were more robust to MLC sequencing, particularly to the stratification and small aperture removal. The maximum and mean aperture sizes using the DDR were consistently larger than those from DMS for all tested number of segments.
The fluence map to MLC segmentation conversion problem was formulated as a secondary optimization problem in the dose domain to minimize the smoothness-regularized dose discrepancy. The large scale optimization problem was solved using a primal-dual algorithm that transformed complicated fluences into maps that were more robust to the MLC segmentation and sequencing, affording fewer and larger segments with minimal degradation to dose distribution.
自动射束方向和注量优化技术的出现，使得利用越来越多的射野进行更复杂的调强放射治疗（IMRT）计划成为可能，从而拓展了解决方案空间。这给将复杂注量转换为用于放疗的稳健多叶准直器（MLC）子野带来了挑战。本文介绍一种新方法，用于正则化注量图并简化MLC子野，以最大化放疗效率、精度和计划质量。
在本研究中，我们实施了一种在剂量域正则化优化注量的新方法。将治疗计划问题构建在一个优化框架中，以最小化分割引起的剂量分布退化，同时采用全变差正则化来促使注量图具有分段平滑性。使用一种称为Chambolle-Pock算法的一阶原始对偶算法求解优化问题。利用20个自动选择并优化的非共面射野，为2例胶质母细胞瘤、2例头颈部肿瘤和2例肺癌患者制定计划。首先使用Chambolle-Pock算法对注量进行正则化，然后将其分层为相等的步长，并使用先前描述的层级缩减方法计算MLC子野。从MLC子野中去除尺寸小于预设阈值（1 - 3个像素，像素是MLC离散化的IMRT注量图的方形单元）的孤立孔径。将剂量域正则化（DDR）注量的性能与未进行剂量域注量正则化的直接分层和直接MLC分割（DMS）注量进行比较。
对于所有6例患者，DDR方法将平均计划靶区剂量均匀性（D95/D5）从0.814提高到0.878，同时保持危及器官（OARs）的等效剂量。正则化注量对MLC排序更稳健，但对分层和小孔径去除尤其稳健。对于所有测试的子野数量，使用DDR方法时的最大和平均孔径尺寸始终大于DMS方法。
将注量图到MLC分割的转换问题构建为剂量域中的二次优化问题，以最小化平滑正则化的剂量差异。使用原始对偶算法解决大规模优化问题，该算法将复杂的注量转换为对MLC分割和排序更稳健的图，从而在剂量分布退化最小的情况下提供更少且更大的子野。
