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Keywords:

  • sparse reconstruction;
  • k-space optimization;
  • compressed sensing;
  • experimental design;
  • compressed sensing;
  • Bayesian inference;
  • sub-Nyquist

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. Magn Reson Med, 2010. © 2009 Wiley-Liss, Inc.