Hybrid Weighted l1-Total Variation Constrained Reconstruction for MR Image

作     者：ZHAO Di DU Huiqian MEI Wenbo 

作者机构：School of Information and ElectronicsBeijing Institute of Technology 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2014年第23卷第4期

页      面：747-752页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by Natural Science Foundation of China(No.61077022) 

主　　题：Compressed sensing(CS) Magnetic resonance imaging(MRI) Image reconstruction Weighted l 1 norm Weighted Total variation(TV) 

摘      要：Compressed sensing based Magnetic resonance(MR) image reconstruction can be done by minimizing the sum of least square data fitting item, the Total variation(TV) and l1 norm regularizations. In this paper, inspired by the conventional constrained reconstruction model, we propose a hybrid weighted l1-TV minimization method to reconstruct MR image. We introduce the iterative mechanism to update the weights for l1 and TV norms adaptively. The weights vary at each element of the image matrix according to the presented weights selection strategy. Experiments on Shepp-Logan phantom and practical MR images demonstrate the proposed method can preserve image details and obtain improved reconstruction quality compared to the traditional CS-MRI method and other weighted methods.