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Abstract #1559

Incoherence Parameter Analysis for Optimized Compressed Sensing with Nonlinear Encoding Gradients

Leo K. Tam 1 , Gigi Galiana 1 , Haifeng Wang 1 , Emre Kopanoglu 1 , Andrew Dewdney 2 , Dana C. Peters 1 , and R. Todd Constable 1

1 Diagnostic Radiology, Yale University, New Haven, CT, United States, 2 Siemens Healthcare AG, Erlangen, Bavaria, Germany

Incoherence in compressed sensing is known to be important, but is there new understanding to be gained beyond the canonical method of selecting k-space coefficients in a psuedo-random manner. The incoherence parameter is studied, which dictates the largest subset of vectors in the sparse domain that may be exactly recovered via convex optimization with an L1 norm constraint. Incoherence parameter maps, showing the pairwise incoherence at each pixel are presented. The incoherence parameter is optimized using nonlinear encoding gradients, and experiments with a 3T Siemens Trio are presented that show an optimized incoherence parameter leads to reduced MSE.

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