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Abstract #2861

SpRING: Sparse Reconstruction of Images using the space Method & GRAPPA

Daniel Stuart Weller1, Jonathan R. Polimeni2,3, Leo Grady4, Lawrence L. Wald2,3, Elfar Adalsteinsson1, Vivek Goyal1

1EECS, Massachusetts Institute of Technology, Cambridge, MA, United States; 2A. A. Martinos Center, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; 3Dept. of Radiology, Harvard Medical School, Boston, MA, United States; 4Dept. of Image Analytics & Informatics, Siemens Corporate Research, Princeton, NJ, United States

SpRING combines compressed sensing (CS) with GRAPPA to recover a sparse image from multi-channel, undersampled k-space data. The combined method operates in the space of the observation matrix, holding the acquired data fixed without resorting to complicated procedures for constrained optimization. Whereas GRAPPA amplifies the noise present in the image and CS over-smoothes non-sparse details, the combined method strikes a balance to improve SNR and preserve details. We analyze the noise amplification properties of the combined algorithm using g-factors computed using Monte Carlo trials to illustrate its superiority over GRAPPA and CS alone for real data.