ACCELERATING CARDIOVASCULAR IMAGING BY EXPLOITING REGIONAL LOW-RANK STRUCTURE VIA GROUP SPARSITY.
Sparse sampling of (k,t)-space has proved useful for cardiac MRI. This paper builds on previous work on using partial separability (PS) and spatial-spectral sparsity for high-quality image reconstruction from highly undersampled (k,t)-space data. This new method uses a more flexible control over the PS-induced low-rank constraint via group-sparse regularization. A novel algorithm is also described to solve the corresponding (1,2)-norm regularized inverse problem. Reconstruction results from simulated cardiovascular imaging data are presented to demonstrate the performance of the proposed method.
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign ; Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign.
Anthony G
AG
Christodoulou
Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign.
S Derin
SD
Babacan
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign ; Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign.
Zhi-Pei
ZP
Liang
Cardiovascular MRI; Group sparsity; Inverse problems; Low-rank modeling; Partial separability