Abstract #1561
Fast and Simple Patch-Based Sparse Reconstruction Exploiting Local Image Correlations
Alicia W Yang 1,2 , Li Feng 1,2 , Daniel K Sodickson 1 , and Ricardo Otazo 1
1
Bernard and Irene Schwartz Center for
Biomedical Imaging, Department of Radiology, New York
University School of Medicine, New York, NY, United
States,
2
Sackler
Institute for Biomedical Sciences, New York University,
New York, NY, United States
A patch-based sparse image reconstruction method based
on local image correlations is proposed for compressed
sensing. The method divides the image into
non-overlapping blocks and sparsity is enforced in each
block separately by thresholding the Principal Component
Analysis (PCA) representation of a series of small
patches within the block. The method exploits
correlations directly in the image without the need of
an analytical transform and it is reference-less and
computationally efficient, removing the need to search
for similar patches in the whole image. We tested the
performance of the method to reconstruct undersampled 2D
and 3D MSK images.
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