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Abstract:
The optimization of k-space sampling for nonlinear sparse
MRI reconstruction is phrased as a Bayesian experimental
design problem. Bayesian inference is approximated by a novel
relaxation to standard signal processing primitives, resulting
in an efficient optimization algorithm for Cartesian and spiral
trajectories. On clinical resolution brain image data from
a Siemens 3T scanner, automatically optimized trajectories
lead to significantly improved images, compared to standard
low-pass, equispaced, or variable density randomized
designs. Insights into the nonlinear design optimization problem
for MRI are given.
