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Abstract #2893

Space Imaging with Compressed Sensing for Rapid Parallel Imaging

Leo K. Tam1, Jason P. Stockmann1, Gigi Galiana2, Robert Todd Constable1,2

1Biomedical Engineering, Yale University, New Haven, CT, United States; 2Diagnostic Radiology & Neurosurgery, Yale University, New Haven, CT

Recent advances in accelerating MRI scans include compressed sensing and the utilization of nonlinear magnetic encoding fields. The two methods collect data in a targeted manner and disperse residual aliasing artifacts to make them less apparent. In the current work, a parallel nonlinear magnetic encoding method using first and second order in-plane spherical harmonics, Space Imaging (NSI), is combined with a compressed sensing algorithm to reconstruct highly accelerated images with fidelity. The work suggests that compressed sensing and parallel imaging with higher order gradients may be a synergistic approach towards robust reconstructions of accelerated scans.