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Bayesian Experimental Design of Magnetic Resonance Imaging Sequences

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84205

Seeger,  MW
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84109

Nickisch,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84145

Pohmann,  R
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Seeger, M., Nickisch, H., Pohmann, R., & Schölkopf, B. (2009). Bayesian Experimental Design of Magnetic Resonance Imaging Sequences. Advances in neural information processing systems 21: 22nd Annual Conference on Neural Information Processing Systems 2008, 1441-1448.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C46D-4
Zusammenfassung
We show how improved sequences for magnetic resonance imaging can be found through automated 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 solution requires approximate inference for dense, non-Gaussian models on a scale seldom addressed before. 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 a realistic setup with raw data from a 3T MR scanner.