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Title: A Bayesian method with reparameterization for diffusion tensor imaging - art. no. 69142J
Authors: Zhou, Diwei
Dryden, Ian L.
Koloydenko, Alexey
Li, Bai
Keywords: Diffusion tensor imaging
Multi-tensor model
Bayesian method
Monte Carlo simulation
Issue Date: 2008
Publisher: © Society of Photo-optical Instrumentation Engineers (SPIE)
Citation: ZHOU, D. ... et al., 2008. A Bayesian method with reparameterization for diffusion tensor imaging [69142J]. Medical Imaging 2008: Image Processing, 69142J (March 11, 2008); doi:10.1117/12.771697
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.
Description: ZHOU, D. ... et al., 2008. A Bayesian method with reparameterization for diffusion tensor imaging [69142J]. Medical Imaging 2008: Image Processing, 69142J (March 11, 2008) http://dx.doi.org/10.1117/12.771697 Copyright 2008 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Sponsor: European Commission FP6 Human Resources and Mobility program
Version: Published
DOI: 10.1117/12.771697
URI: https://dspace.lboro.ac.uk/2134/17102
Publisher Link: http://dx.doi.org/10.1117/12.771697
ISBN: 978-0-8194-7098-0
ISSN: 0277-786X
Appears in Collections:Published Articles (Maths)

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