Ali Bilgin1,2, Yookyung Kim2, Feng Liu2, Mariappan S. Nadar3
1Biomedical Engineering, University of Arizona, Tucson, AZ, United States; 2Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States; 3Siemens Corporation, Corporate Research, Princeton, NJ, United States
The recently introduced Compressed Sensing (CS) theory promises to accelerate data acquisition in MRI. In this work, we propose a framework for designing and utilizing sparse dictionaries in CS MRI applications. Reconstruction results demonstrate that the proposed technique can yield significantly improved image quality compared to commonly used sparsity transforms in CS MRI.