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Multi-objective optimization of inverse planning for accurate radiotherapy

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【作者】 曹瑞芬吴宜灿裴曦景佳李国丽程梦云李贵胡丽琴

【Author】 CAO Rui-Fen1,2 WU Yi-Can1,2,3 PEI Xi1,2 JING Jia1,3,5 LI Guo-Li4 CHENG Meng-Yun1,2 LI Gui1,2 HU Li-Qin1,2 1 Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China 2 Engineering Technology Research Center of Accurate Radiotherapy, Hefei 230031, China 3 School of Nuclear Science and Technology, University of Science and Technology of China, Hefei 230027, China 4 Zhejiang University of Technology, Hangzhou 310014, China 5 Hefei University of Technology, Hefei 230009, China

【机构】 Institute of Plasma Physics,Chinese Academy of SciencesEngineering Technology Research Center of Accurate RadiotherapySchool of Nuclear Science and Technology, University of Science and Technology of ChinaHefei University of TechnologyZhejiang University of Technology

【摘要】 The multi-objective optimization of inverse planning based on the Pareto solution set, according to the multi-objective character of inverse planning in accurate radiotherapy, was studied in this paper. Firstly, the clinical requirements of a treatment plan were transformed into a multi-objective optimization problem with multiple constraints. Then, the fast and elitist multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-) was introduced to optimize the problem. A clinical example was tested using this method. The results show that an obtained set of non-dominated solutions were uniformly distributed and the corresponding dose distribution of each solution not only approached the expected dose distribution, but also met the dosevolume constraints. It was indicated that the clinical requirements were better satisfied using the method and the planner could select the optimal treatment plan from the non-dominated solution set.

【Abstract】 The multi-objective optimization of inverse planning based on the Pareto solution set, according to the multi-objective character of inverse planning in accurate radiotherapy, was studied in this paper. Firstly, the clinical requirements of a treatment plan were transformed into a multi-objective optimization problem with multiple constraints. Then, the fast and elitist multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-) was introduced to optimize the problem. A clinical example was tested using this method. The results show that an obtained set of non-dominated solutions were uniformly distributed and the corresponding dose distribution of each solution not only approached the expected dose distribution, but also met the dosevolume constraints. It was indicated that the clinical requirements were better satisfied using the method and the planner could select the optimal treatment plan from the non-dominated solution set.

【基金】 Supported by National Natural Seience Foundation (30900386);Anhui Provincial Natural Science Foundation (090413095,11040606Q55)
  • 【文献出处】 中国物理C ,Chinese Physics C , 编辑部邮箱 ,2011年03期
  • 【分类号】R815
  • 【被引频次】35
  • 【下载频次】107
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