Matthias Seeger1, Hannes Nickisch2, Rolf Pohmann2, Bernhard Schlkopf2
1Saarland University, Saarbrcken, Germany; 2Max Planck Institute for Biological Cybernetics, Tbingen, Germany
We show how improved sequences for magnetic resonance imaging can be found through automated sequential optimization of Bayesian design scores. Combining recent advances in approximate Bayesian inference and natural image statistics with high-performance numerical computation, we propose the first scalable Bayesian experimental design framework for this problem of high relevance to clinical and brain research. Our approach is evaluated on raw data from a 3T MR scanner.
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