文献详情
A singular value decomposition linear programming (SVDLP) optimization technique for circular cone based robotic radiotherapy
文献类型期刊
作者Liang, Bin[1];Li, Yongbao[2];Wei, Ran[3];Guo, Bin[4];Xu, Xuang[5];Liu, Bo[6];Li, Jiafeng[7];Wu, Qiuwen[8];Zhou, Fugen[9]
机构
2018
期刊名称PHYSICS IN MEDICINE AND BIOLOGY影响因子和分区
通讯作者Liu, B (reprint author), Beihang Univ, Image Proc Ctr, Beijing 100191, Peoples R China.
63
来源信息年:2018  卷:63  期:1  页码范围:015034  
1
期刊信息PHYSICS IN MEDICINE AND BIOLOGY影响因子和分区  ISSN:0031-9155
页码范围015034
关键词compressive sensing; SVD; linear programming; radiotherapy optimization; CyberKnife system
增刊正刊
摘要With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l(1) norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP approach is tested and compared with MultiPlan on three clinical cases of varying complexities. In general, the plans generated by the SVDLP achieve steeper dose gradient, better conformity and less damage to normal tissues. In conclusion, the SVDLP approach effectively improves the quality of treatment plan due to the use of the complete beam search space. This challenging optimization problem with the complete beam search space is effectively handled by the proposed SVD acceleration.
收录情况SCIE(WOS:000419467800002)  EI(20180304647781)  PubMed(29148432)  
所属部门宇航学院
链接地址https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040335457&doi=10.1088%2f1361-6560%2faa9b47&partnerID=40&md5=a43a05f4e984b8a7b4f1cf86d66f2978
DOI10.1088/1361-6560/aa9b47
百度学术A singular value decomposition linear programming (SVDLP) optimization technique for circular cone based robotic radiotherapy
语言外文
ISSN0031-9155
人气指数1402
下载次数6
浏览次数1378
基金National Key R&D Program of China [2017YFC0113100]; National Natural Science Foundation of China [61601012]
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