DMCA
Optimization of k-space trajectories for compressed sensing by Bayesian experimental design (2009)
Cached
Download Links
| Venue: | MAGNETIC RESONANCE IN MEDICINE |
| Citations: | 16 - 3 self |
BibTeX
@INPROCEEDINGS{Seeger09optimizationof,
author = {Matthias Seeger and Hannes Nickisch and Rolf Pohmann and Bernhard Schölkopf},
title = { Optimization of k-space trajectories for compressed sensing by Bayesian experimental design},
booktitle = {MAGNETIC RESONANCE IN MEDICINE},
year = {2009},
pages = {116--126},
publisher = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
The optimization of k-space sampling for nonlinear sparse MRI reconstruction is phrased as 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 MR imaging are given.
Keyphrases
bayesian experimental design k-space trajectory variable density signal processing primitive nonlinear design optimization problem clinical resolution brain image data spiral trajectory efficient optimization algorithm mr imaging bayesian inference novel relaxation k-space sampling bayesian experimental design problem nonlinear sparse mri reconstruction
