Bayesian Experimental Design of Magnetic Resonance Imaging Sequences

We show how improved sequences for magnetic resonance imaging can be found through optimization of Bayesian design scores. Combining approximate Bayesian inference and natural image statistics with high-performance numerical computation, we propose the first Bayesian experimental design framework for this problem of high relevance to clinical and brain research. Our solution requires large-scale approximate inference for dense, non-Gaussian models. We propose a novel scalable variational inference algorithm, and show how powerful methods of numerical mathematics can be modified to compute primitives in our framework. Our approach is evaluated on raw data from a 3T MR scanner.


Published in:
Proceedings of the 22nd Annual Conference on Neural Information Processing Systems, 1441-1448
Presented at:
Neural Information Processing Systems 21, Vancouver, BC
Year:
2009
Keywords:
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 Record created 2010-12-01, last modified 2018-09-13

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