Optimization of k-space trajectories for compressed sensing by Bayesian experimental design

Magnetic Resonance in Medicine

Volumn 63, Issue 1, 2010, Pages 116-126

  Seeger, Matthias ^a   Nickisch, Hannes ^b   Pohmann, Rolf ^b   Schölkopf, Bernhard ^b  

^a SAARLAND UNIVERSITY   (Germany)

^b MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

Bayesian inference; Compressed sensing; Experimental design; k space optimization; Sparse reconstruction; Sub Nyquist

Indexed keywords

BAYESIAN NETWORKS; BRAIN MAPPING; COMPRESSED SENSING; DESIGN OF EXPERIMENTS; IMAGE ENHANCEMENT; OPTIMIZATION; SPACE FLIGHT; STATISTICS; TRAJECTORIES;

BAYESIAN EXPERIMENTAL DESIGNS; BAYESIAN INFERENCE; K SPACE; K-SPACE TRAJECTORIES; NYQUIST; OPTIMIZATION ALGORITHMS; SPARSE RECONSTRUCTION; SPIRAL TRAJECTORIES;

INFERENCE ENGINES;

ALGORITHM; ARTICLE; BAYES THEOREM; BRAIN REGION; COMPRESSION; DENSITY; EXPERIMENTAL DESIGN; IMAGE RECONSTRUCTION; NUCLEAR MAGNETIC RESONANCE IMAGING; SIGNAL PROCESSING;

EID: 73349101812     PISSN: 07403194     EISSN: 15222594     Source Type: Journal    
DOI: 10.1002/mrm.22180     Document Type: Article

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