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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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