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Bayesian Diffusion Tensor Estimation with Spatial Priors
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-2193-6003
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
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2017 (English)In: CAIP 2017: Computer Analysis of Images and Patterns, 2017Conference paper, Published paper (Refereed)
Abstract [en]

Spatial regularization is a technique that exploits the dependence between nearby regions to locally pool data, with the effect of reducing noise and implicitly smoothing the data. Most of the currently proposed methods are focused on minimizing a cost function, during which the regularization parameter must be tuned in order to find the optimal solution. We propose a fast Markov chain Monte Carlo (MCMC) method for diffusion tensor estimation, for both 2D and 3D priors data. The regularization parameter is jointly with the tensor using MCMC. We compare FA (fractional anisotropy) maps for various b-values using three diffusion tensor estimation methods: least-squares and MCMC with and without spatial priors. Coefficient of variation (CV) is calculated to measure the uncertainty of the FA maps calculated from the MCMC samples, and our results show that the MCMC algorithm with spatial priors provides a denoising effect and reduces the uncertainty of the MCMC samples.

Place, publisher, year, edition, pages
2017.
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10424
Keyword [en]
Spatial regularization, Diffusion tensor, Spatial priors Markov chain, Monte Carlo Fractional anisotropy
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-139844DOI: 10.1007/978-3-319-64689-3_30ISBN: 978-3-319-64689-3 (electronic)ISBN: 978-3-319-64688-6 (print)OAI: oai:DiVA.org:liu-139844DiVA: diva2:1133926
Conference
International Conference on Computer Analysis of Images and Patterns
Available from: 2017-08-17 Created: 2017-08-17 Last updated: 2017-10-12

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The full text will be freely available from 2018-07-29 15:19
Available from 2018-07-29 15:19

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Gu, XuanSidén, PerWegmann, BertilEklund, AndersVillani, MattiasKnutsson, Hans
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Division of Biomedical EngineeringFaculty of Science & EngineeringCenter for Medical Image Science and Visualization (CMIV)The Division of Statistics and Machine Learning
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Citation style
  • apa
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