A Bayesian method with reparameterization for diffusion tensor imaging

Diwei Zhou

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBN

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.
Original languageEnglish
Title of host publicationSPIE conference, Medical Imaging 2008: Image Processing
Place of PublicationBellingham, WA, USA
PublisherSPIE
Pages0-0
Number of pages1
Volume6914
ISBN (Print)9780819470980
DOIs
Publication statusPublished - 17 Feb 2008
EventSPIE conference, Medical Imaging 2008: Image Processing - San Diego, California, USA, 17–21 February, 2008
Duration: 17 Feb 2008 → …

Publication series

NameSPIE Proceedings

Conference

ConferenceSPIE conference, Medical Imaging 2008: Image Processing
Period17/02/08 → …

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  • Cite this

    Zhou, D. (2008). A Bayesian method with reparameterization for diffusion tensor imaging. In SPIE conference, Medical Imaging 2008: Image Processing (Vol. 6914, pp. 0-0). (SPIE Proceedings). SPIE. https://doi.org/10.1117/12.771697