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.
Keywords