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Optimization of k-Space Trajectories by Bayesian Experimental Design




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.

Author(s): Seeger, M. and Nickisch, H. and Pohmann, R. and Schölkopf, B.
Volume: 17
Number (issue): 2627
Year: 2009
Month: April
Day: 0

Department(s): Empirical Inference
Bibtex Type: Poster (poster)

Digital: 0
Event Name: 17th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2009)
Event Place: Honolulu, HI, USA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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  title = {Optimization of k-Space Trajectories by Bayesian Experimental Design},
  author = {Seeger, M. and Nickisch, H. and Pohmann, R. and Sch{\"o}lkopf, B.},
  volume = {17},
  number = {2627},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = apr,
  year = {2009},
  month_numeric = {4}