title: Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.
abstract: Compressed sensing magnetic resonance imaging has shown great capacity for accelerating magnetic resonance imaging if an image can be sparsely represented. How the image is sparsified seriously affects its reconstruction quality. In the present study, a graph-based redundant wavelet transform is introduced to sparsely represent magnetic resonance images in iterative image reconstructions. With this transform, image patches is viewed as vertices and their differences as edges, and the shortest path on the graph minimizes the total difference of all image patches. Using the l1 norm regularized formulation of the problem solved by an alternating-direction minimization with continuation algorithm, the experimental results demonstrate that the proposed method outperforms several state-of-the-art reconstruction methods in removing artifacts and achieves fewer reconstruction errors on the tested datasets.
authors.affiliation: Departments of Electronic Science and Communication Engineering, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China.
authors.forename: Zongying
authors.initials: Z
authors.lastname: Lai
authors.affiliation: Departments of Electronic Science and Communication Engineering, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China. Electronic address: quxiaobo@xmu.edu.cn.
authors.forename: Xiaobo
authors.initials: X
authors.lastname: Qu
authors.affiliation: Departments of Electronic Science and Communication Engineering, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China.
authors.forename: Yunsong
authors.initials: Y
authors.lastname: Liu
authors.affiliation: School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China.
authors.forename: Di
authors.initials: D
authors.lastname: Guo
authors.affiliation: Departments of Electronic Science and Communication Engineering, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Ye
authors.affiliation: Departments of Electronic Science and Communication Engineering, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China.
authors.forename: Zhifang
authors.initials: Z
authors.lastname: Zhan
authors.affiliation: Departments of Electronic Science and Communication Engineering, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China. Electronic address: chenz@xmu.edu.cn.
authors.forename: Zhong
authors.initials: Z
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D003196:Computer Graphics; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D058067:Wavelet Analysis
keywords: Compressed sensing; Graph; Image reconstruction; MRI; Wavelet