PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Optimization of k-Space Trajectories by Bayesian Experimental Design
M. Seeger, H. Nickisch, R. Pohmann and B. Schölkopf
In: 17th Annual Scientific Meeting of the ISMRM(2009).

Abstract

MR image reconstruction from undersampled k-space can be improved by nonlinear denoising estimators since they incorporate statistical prior knowledge about image sparsity. Reconstruction quality depends crucially on the undersampling design (k-space trajectory), in a manner complicated by the nonlinear and signal-dependent characteristics of these methods. We propose an algorithm to assess and optimize k-space trajectories for sparse MRI reconstruction, based on Bayesian experimental design, which is scaled up to full MR images by a novel variational relaxation to iteratively reweighted FFT or gridding computations. Designs are built sequentially by adding phase encodes predicted to be most informative, given the combination of previous measurements with image prior information.

EPrint Type:Conference or Workshop Item (Other)
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:6333
Deposited By:Bernhard Schölkopf
Deposited On:08 March 2010