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An Improved Convex Programming Model for the Inverse Problem in Intensity-Modulated Radiation Therapy

Volume 14, Number 5, May 2018, pp. 871-884
DOI: 10.23940/ijpe.18.05.p5.871884

Yihua Lana,b, Xingang Zhanga,b, Jianyang Zhanga,b, Yang Wangc, and Chih-Cheng Hungd

aSchool of Computer and Information Technology, Nanyang Normal University, Nanyang, 473061, China
bInstitute of Image Processing and Pattern Recognition, Nanyang Normal University, Nanyang, 473061, China
cRadiology Department, Central Hospital of Nanyang, Nanyang, 473061, China
dLaboratory for Machine Vision and Security Research, College of Computing and Software Engineering, Kennesaw State University - Marietta Campus, 1100 South Marietta Parkway, Marietta, Georgia, 30067-2896, USA

(Submitted on February 14, 2018; Revised on March 21, 2018; Accepted on April 17, 2018)

Abstract:

Intensity modulated radiation therapy technology (IMRT) is one of the main approaches in cancer treatment because it can guarantee the killing of cancer cells while optimally protecting normal tissue from complications. Inverse planning, which is the core component of the entire IMRT system, is mainly based on accurate mathematical modeling and associated fast solving methods. In inverse planning, the fluence map optimization, which considers the multi-leaf collimator (MLC) modulation, is the current research focus. Although the hitting constrain problem with the unidirectional movement of leaf-sweeping has been solved, our goal is to solve the hitting constrain problem with the bidirectional movement of leaf-sweeping. In this study, we propose a non-synchronized type to solve the hitting constrain problem with the bidirectional movement of leaf-sweeping schemes for IMRT. In solving this problem, a new mathematical model is proposed under the framework of convex programming. The advantage of the convex model is to avoid the uncertainty and inaccuracy that occurs in the non-convex programming solving process. Experimental results for two clinical testing cases show that under the same condition of total number of monitoring units, the new proposed model produces better dose distribution than those of the total variance and quadratic models.

 

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        Yihua Lana,b, Xingang Zhanga,b, Jianyang Zhang a,b, Yang Wangc, and Chih-Cheng Hungd
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