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

2010

Article

ei


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.

Author(s): Seeger, M. and Nickisch, H. and Pohmann, R. and Schölkopf, B.
Journal: Magnetic Resonance in Medicine
Volume: 63
Number (issue): 1
Pages: 116-126
Year: 2010
Month: January
Day: 0

Department(s): Empirical Inference
Research Project(s): Medical and Neuroscientific Imaging
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1002/mrm.22180
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web

BibTex

@article{6039,
  title = {Optimization of k-Space Trajectories for Compressed Sensing by Bayesian Experimental Design},
  author = {Seeger, M. and Nickisch, H. and Pohmann, R. and Sch{\"o}lkopf, B.},
  journal = {Magnetic Resonance in Medicine},
  volume = {63},
  number = {1},
  pages = {116-126},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jan,
  year = {2010},
  month_numeric = {1}
}