质子疗法
计算机科学
放射治疗
放射治疗计划
概率逻辑
分数(化学)
稳健优化
医学物理学
核医学
医学
数学优化
人工智能
数学
放射科
有机化学
化学
作者
Jan Unkelbach,M. Alber,Mark Bangert,Rasmus Bokrantz,Timothy C. Y. Chan,Joseph O. Deasy,Albin Fredriksson,Bram L. Gorissen,Marcel van Herk,Wei Liu,Houra Mahmoudzadeh,Omid Nohadani,J Siebers,Marnix G. Witte,Huijun Xu
标识
DOI:10.1088/1361-6560/aae659
摘要
Motion and uncertainty in radiotherapy is traditionally handled via margins.The clinical target volume (CTV) is expanded to a larger planning target volume (PTV), which is irradiated to the prescribed dose.However, the PTV concept has several limitations, especially in proton therapy.Therefore, robust and probabilistic optimization methods have been developed that directly incorporate motion and uncertainty into treatment plan optimization for intensity modulated radiotherapy (IMRT) and intensity modulated proton therapy (IMPT).Thereby, the explicit definition of a PTV becomes obsolete and treatment plan optimization is directly based on the CTV.Initial work focused on random and systematic setup errors in IMRT.Later, inter-fraction prostate motion and intra-fraction lung motion became a research focus.Over the past 10 years, IMPT has emerged as a new application for robust planning methods.In proton therapy, range or setup errors may lead to dose degradation and misalignment of dose contributions from different beams a problem that cannot generally be addressed by margins.Therefore, IMPT has led to the first implementations of robust planning methods in commercial planning systems, making these methods available for clinical use.This paper first summarizes the limitations of the PTV concept.Subsequently, robust optimization methods are introduced and their applications in IMRT and IMPT planning are reviewed.
科研通智能强力驱动
Strongly Powered by AbleSci AI