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

MPG-Autoren
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Nickisch,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Pohmann,  R
Former Department MRZ, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen

https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.22180
(Verlagsversion)

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Zitation

Seeger, M., Nickisch, H., Pohmann, R., & Schölkopf, B. (2010). Optimization of k-Space Trajectories for Compressed Sensing by Bayesian Experimental Design. Magnetic Resonance in Medicine, 63(1), 116-126. doi:10.1002/mrm.22180.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-C170-3
Zusammenfassung
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.