Accelerating Dynamic Cardiac MR imaging using structured sparse representation.
Compressed sensing (CS) has produced promising results on dynamic cardiac MR imaging by exploiting the sparsity in image series. In this paper, we propose a new method to improve the CS reconstruction for dynamic cardiac MRI based on the theory of structured sparse representation. The proposed method user the PCA subdictionaries for adaptive sparse representation and suppresses the sparse coding noise to obtain good reconstructions. An accelerated iterative shrinkage algorithm is used to solve the optimization problem and achieve a fast convergence rate. Experimental results demonstrate that the proposed method improves the reconstruction quality of dynamic cardiac cine MRI over the state-of-the-art CS method.
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China.
Nian
N
Cai
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China ; Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China ; Shenzhen Key Laboratory for MRI, Guangdong, Shenzhen 518055, China.
Shengru
S
Wang
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China.
Shasha
S
Zhu
Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China ; Shenzhen Key Laboratory for MRI, Guangdong, Shenzhen 518055, China.
Dong
D
Liang
D000465:Algorithms; D044962:Data Compression; D006321:Heart / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine; D008962:Models, Theoretical; D009206:Myocardium / Q000473:pathology; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted