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

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C170-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-7E26-7
Genre: Journal Article

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 Creators:
Seeger, M, Author              
Nickisch, H1, 2, Author              
Pohmann, R2, 3, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Former Department MRZ, Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_2528700              

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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.

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 Dates: 2010-01
 Publication Status: Published in print
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 Identifiers: DOI: 10.1002/mrm.22180
BibTex Citekey: 6039
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Title: Magnetic Resonance in Medicine
Source Genre: Journal
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Publ. Info: New York : Wiley-Liss
Pages: - Volume / Issue: 63 (1) Sequence Number: - Start / End Page: 116 - 126 Identifier: ISSN: 0740-3194
CoNE: https://pure.mpg.de/cone/journals/resource/954925538149