Magnetic Resonance Image Reconstruction via the Augmented TV-regularized Problem

摘要：

　　In order to perfectly recover MR image using compressed sensing(CS)with TV-regularization term,one has to minimize ill-conditioned optimization functions on large datasets,which are usually not tractable.To address this problem,we propose a novel algorithm for solving an augmented total variation(TV)regularized model with linear constraints,which can obtain accurate solutions.The classical alternating direction method of multiplies(ADMM)framework is utilized to reformulate this model.A linearization strategy is designed to simplify operation.In addition,we combine weighted solution procedure as well as backtracking strategy for improving the reconstruction quality.Experimental results demonstrate that the proposed approach achieves better reconstruction performance than traditional CS-based methods.

关键词： compressed sensing ADMM Linearization TV-regularization

作者: Shanshan Chen Hongwei Du Linna Wu Jiaquan Jin

作者单位: Centers for Biomedical Engineering,University of Science and Technology of China,Hefei,Anhui 230027,China

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 2666-2669

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）