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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