English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

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

MPS-Authors
/persons/resource/persons84109

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

/persons/resource/persons84145

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

/persons/resource/persons84193

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

External Resource
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


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