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Bayesian Diffusion Tensor Estimation with Spatial Priors

  • Xuan GuEmail author
  • Per Sidén
  • Bertil Wegmann
  • Anders Eklund
  • Mattias Villani
  • Hans Knutsson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10424)

Abstract

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.

Keywords

Spatial regularization Diffusion tensor Spatial priors Markov chain Monte Carlo Fractional anisotropy 

Notes

Acknowledgements

This research was supported by the Information Technology for European Advancement (ITEA) 3 Project BENEFIT (better effectiveness and efficiency by measuring and modelling of interventional therapy) and the Swedish Research Council (grant 2015-05356, “Learning of sets of diffusion MRI sequences for optimal imaging of micro structures” and grant 2013-5229 “Statistical analysis of fMRI data”).

Data collection and sharing for this project was provided by the Human Connectome Project (HCP; Principal Investigators: Bruce Rosen, M.D., Ph.D., Arthur W. Toga, Ph.D., Van J. Weeden, MD). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neuro-logical Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Xuan Gu
    • 1
    • 3
    Email author
  • Per Sidén
    • 2
  • Bertil Wegmann
    • 2
  • Anders Eklund
    • 1
    • 2
    • 3
  • Mattias Villani
    • 2
  • Hans Knutsson
    • 1
    • 3
  1. 1.Division of Medical Informatics, Department of Biomedical EngineeringLinköping UniversityLinköpingSweden
  2. 2.Division of Statistics and Machine Learning, Department of Computer and Information ScienceLinköping UniversityLinköpingSweden
  3. 3.Center for Medical Image Science and VisualizationLinköping UniversityLinköpingSweden

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