Yookyung Kim1, Mariappan S. Nadar2, Ali Bilgin, 1,3
1Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States; 2Siemens Corporation, Corporate Research, Princeton, NJ, United States; 3Biomedical Engineering, University of Arizona, Tucson, AZ, United States
While initial Compressed Sensing (CS) techniques assumed that sparsity transform coefficients are independently distributed, recent results indicate that dependencies between transform coefficients can be exploited for improved performance. In this paper, we propose the use of a Gaussian Scale Mixture (GSM) model for exploiting the dependencies between wavelet coefficients in CS MRI. Our results indicate that the proposed model can significantly reduce the reconstruction artifacts and reconstruction time in wavelet-based CS MRI.