Optimization of k-Space Trajectories for Compressed Sensing by Bayesian Experimental Design

The optimization of k-space sampling for nonlinear sparse MRI reconstruction is phrased as 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 MR imaging are given.


Published in:
Magnetic Resonance in Medicine, 61, 1, 116-126
Year:
2010
Publisher:
Wiley-Blackwell
ISSN:
0740-3194
Keywords:
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 Record created 2010-12-01, last modified 2018-10-01

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