PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Optimization of k-Space Trajectories for Compressed Sensing by Bayesian Experimental Design
M. Seeger, H. Nickisch, R. Pohmann and B. Schölkopf
Magnetic Resonance in Medicine Volume 63, Number 1, pp. 116-126, 2010.


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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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Brain Computer Interfaces
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:6336
Deposited By:Bernhard Schölkopf
Deposited On:08 March 2010