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Proceedings Paper

A Bayesian method with reparameterization for diffusion tensor imaging
Author(s): Diwei Zhou; Ian L. Dryden; Alexey Koloydenko; Bai Li
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Paper Abstract

A multi-tensor model with identifiable parameters is developed for diffusion weighted MR images. A new parameterization method guarantees the symmetric positive-definiteness of the diffusion tensor. We set up a Bayesian method for parameter estimation. To investigate properties of the method, Monte Carlo simulated data from three distinct DTI direction schemes have been analyzed. The multi-tensor model with automatic model selection has also been applied to a healthy human brain dataset. Standard tensor-derived maps are obtained when the single-tensor model is fitted to a region of interest with a single dominant fiber direction. High anisotropy diffusion flows and main diffusion directions can be shown clearly in the FA map and diffusion ellipsoid map. For another region containing crossing fiber bundles, we estimate and display the ellipsoid map under the single tensor and double-tensor regimes of the multi-tensor model, suitably thresholding the Bayes factor for model selection.

Paper Details

Date Published: 19 March 2008
PDF: 11 pages
Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69142J (19 March 2008); doi: 10.1117/12.771697
Show Author Affiliations
Diwei Zhou, Univ. of Nottingham (United Kingdom)
Ian L. Dryden, Univ. of Nottingham (United Kingdom)
Alexey Koloydenko, Univ. of Nottingham (United Kingdom)
Bai Li, Univ. of Nottingham (United Kingdom)


Published in SPIE Proceedings Vol. 6914:
Medical Imaging 2008: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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