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Abstract #3703

Fast non-local means reconstruction for multi-contrast compressed sensing

Kourosh Jafari-Khouzani 1 , Berkin Bilgic 1 , Jayashree Kalpathy-Cramer 1 , and Kawin Setsompop 1

1 Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States

This abstract proposes a non-local means technique to reconstruct partially sampled images in MRI compressed sensing. Instead of imposing total variation constraint, we use a fully-sampled contrast as a prior estimate to reconstruct other undersampled contrasts. Partial volume information is extracted from the prior estimate by a feature-based non-local means approach and then applied as constraint to the undersampled images. Experiments show that the proposed method is comparable to M-FOCUSS with prior estimate in terms of normalized root-mean-square (NRMSE) error while being up to 30 faster. It also attains 50% NRMSE reduction and 20 speed-up relative to the sparseMRI algorithm.

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