1EECS, Massachusetts Institute of Technology, Cambridge, MA, United States; 2A. A. Martinos Center for Biomedical Imaging, Dept. of Radiology, MGH, Charlestown, MA, United States; 3Harvard Medical School, Boston , MA, United States; 4A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States; 5Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA; 6EECS, MIT, Cambridge, MA, United States
Significant benefit in Compressed Sensing (CS) reconstruction of Diffusion Spectrum Imaging (DSI) data from undersampled q-space was demonstrated when a dictionary trained for sparse representation was utilized rather than wavelet and Total Variation (TV). However, computation times of both dictionary-based and Wavelet+TV methods are on the order of days for full-brain processing. We present two algorithms that are 3 orders of magnitude faster than these CS methods with reconstruction quality comparable to the previous dictionary-CS approach.