Dynamic MRI reconstruction using low-rank and 3D sparsifying transform with separation of background and dynamic components

摘要：

　　Dynamic magnetic resonance imaging(MRI)is an important auxiliary diagnostic method,and higher resolution of images is more conducive to the doctor to diagnose.In this paper,we extend a method which is referred to as robust principal component analysis(RPCA)to reconstruct dynamic magnetic resonance data from under-sampled measurements based on the low-rank plus sparse decomposition model.We consider the dynamic MRI as the sum of the background and the dynamic components,where the background is enforced low-rank by a non-convex function and a 3D sparsifying transform is used to enforce sparsity in the dynamic components.The proposed optimization problem is solved based on variable splitting and alternative optimization.The results of the in-vivo dynamic cardiac dataset show the proposed method achieves superior reconstruction quality,compared to the state-of-the-art reconstruction methods.

关键词： Dynamic MRI RPCA low-rank 3D sparsifying transform image reconstruction

作者: Changfeng Xi Jinxu Tao Bensheng Qiu Zhongfu Ye Xu Xu Jinzhang Xu

作者单位: Department of Electronic Engineering and Information Science University of Science and Technology of Department of Electronic Science and Technology University of Science and Technology of China,Hefei, School of Electrical Engineering and Automation Hefei University of Technology,Hefei,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: 2563-2567

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）