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Bayesian Experimental Design of Magnetic Resonance Imaging Sequences

2009

Conference Paper

ei


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.

Author(s): Seeger, MW. and Nickisch, H. and Pohmann, R. and Schölkopf, B.
Book Title: Advances in neural information processing systems 21
Journal: Advances in neural information processing systems 21 : 22nd Annual Conference on Neural Information Processing Systems 2008
Pages: 1441-1448
Year: 2009
Month: June
Day: 0
Editors: D Koller and D Schuurmans and Y Bengio and L Bottou
Publisher: Curran

Department(s): Empirical Inference
Research Project(s): Medical and Neuroscientific Imaging
Bibtex Type: Conference Paper (inproceedings)

Address: Red Hook, NY, USA
Digital: 0
Event Name: 22nd Annual Conference on Neural Information Processing Systems (NIPS 2008)
Event Place: Vancouver, BC, Canada
ISBN: 978-1-605-60949-2
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{5392,
  title = {Bayesian Experimental Design of Magnetic Resonance Imaging Sequences},
  author = {Seeger, MW. and Nickisch, H. and Pohmann, R. and Sch{\"o}lkopf, B.},
  journal = {Advances in neural information processing systems 21 : 22nd Annual Conference on Neural Information Processing Systems 2008},
  booktitle = {Advances in neural information processing systems 21},
  pages = {1441-1448},
  editors = {D Koller and D Schuurmans and Y Bengio and L Bottou},
  publisher = {Curran},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Red Hook, NY, USA},
  month = jun,
  year = {2009},
  month_numeric = {6}
}