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