Yanhua Wang1, Leslie Ying1
1Department of Biomedical Engineering, Department of Electrical Engineering, The State University of New York at Buffalo, Buffalo, NY, United States
We propose a patch-based dictionary learning model for dynamic MRI reconstruction. The image sequence is divided into overlapping patches along both spatial and temporal directions. A set of temporal dependent dictionaries with three-dimensional atoms are adopted to provide sparse representations for compressed sensing reconstruction. This model adapts to specific local spatial-temporal features. Results on cardiac cine dataset demonstrate that the proposed method is capable of preserving both spatial structures and temporal variations.