Department of Industrial Engineering, University of Houston, Houston, TX 77204, USA.
Phys Med Biol. 2013 Aug 7;58(15):5113-25. doi: 10.1088/0031-9155/58/15/5113. Epub 2013 Jul 9.
The purpose of this study is to investigate the feasibility and impact of incorporating deliverable monitor unit (MU) constraints into spot intensity optimization (SIO) in intensity-modulated proton therapy (IMPT) treatment planning. The current treatment planning system (TPS) for IMPT disregards deliverable MU constraints in the SIO routine. It performs a post-processing procedure on an optimized plan to enforce deliverable MU values that are required by the spot scanning proton delivery system. This procedure can create a significant dose distribution deviation between the optimized and post-processed deliverable plans, especially when small spot spacings are used. In this study, we introduce a two-stage linear programming approach to optimize spot intensities and constrain deliverable MU values simultaneously, i.e., a deliverable SIO (DSIO) model. Thus, the post-processing procedure is eliminated and the associated optimized plan deterioration can be avoided. Four prostate cancer cases at our institution were selected for study and two parallel opposed beam angles were planned for all cases. A quadratic programming based model without MU constraints, i.e., a conventional SIO (CSIO) model, was also implemented to emulate commercial TPS. Plans optimized by both the DSIO and CSIO models were evaluated for five different settings of spot spacing from 3 to 7 mm. For all spot spacings, the DSIO-optimized plans yielded better uniformity for the target dose coverage and critical structure sparing than did the CSIO-optimized plans. With reduced spot spacings, more significant improvements in target dose uniformity and critical structure sparing were observed in the DSIO than in the CSIO-optimized plans. Additionally, better sparing of the rectum and bladder was achieved when reduced spacings were used for the DSIO-optimized plans. The proposed DSIO approach ensures the deliverability of optimized IMPT plans that take into account MU constraints. This eliminates the post-processing procedure required by the TPS as well as the resultant deteriorating effect on ultimate dose distributions. This approach therefore allows IMPT plans to adopt all possible spot spacings optimally. Moreover, dosimetric benefits can be achieved using smaller spot spacings.
本研究旨在探讨在强度调制质子治疗（IMPT）计划中纳入可交付 Monitor Unit（MU）限制条件的可行性和影响。目前的 IMPT 治疗计划系统（TPS）在强度优化（SIO）过程中忽略了可交付 MU 限制条件。它对优化后的计划执行后处理程序，以强制符合点扫描质子输送系统所需的可交付 MU 值。当使用较小的点间距时，此过程可能会导致优化和后处理可交付计划之间产生显著的剂量分布偏差。在本研究中，我们引入了一种两阶段线性规划方法来同时优化点强度并限制可交付 MU 值，即可交付 SIO（DSIO）模型。因此，消除了后处理过程，并避免了相关的优化计划恶化。选择了我们机构的四个前列腺癌病例进行研究，并为所有病例规划了两个平行的对向射束角度。还实施了一个基于二次规划的无 MU 限制模型，即常规 SIO（CSIO）模型，以模拟商业 TPS。使用五种不同的点间距（3 至 7mm）评估了由 DSIO 和 CSIO 模型优化的计划。对于所有点间距，DSIO 优化计划在靶区剂量覆盖和关键结构保护方面比 CSIO 优化计划具有更好的均匀性。随着点间距的减小，DSIO 优化计划在靶区剂量均匀性和关键结构保护方面的改善比 CSIO 优化计划更为显著。此外，当使用较小的间距时，DSIO 优化计划可以更好地保护直肠和膀胱。所提出的 DSIO 方法确保了考虑 MU 限制条件的优化 IMPT 计划的可交付性。这消除了 TPS 所需的后处理程序以及对最终剂量分布的不利影响。因此，该方法允许 IMPT 计划最佳地采用所有可能的点间距。此外，还可以通过使用较小的点间距来实现剂量学优势。
