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.