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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