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Publication Detail
White Matter Models of In Vivo Diffusion MRI Human Brain Data: A Statistical Ranking
  • Publication Type:
    Conference
  • Authors:
    Ferizi U, Panagiotaki E, Schneider T, Wheeler-Kingshott CAM, Alexander DC
  • Publication date:
    11/07/2012
  • Published proceedings:
    Proceeding of the 16th Conference on Medical Image Understanding and Analysis
  • Name of conference:
    Medical Image Understanding and Analysis
  • Conference place:
    Swansea, Wales, UK
  • Conference start date:
    09/07/2012
  • Conference finish date:
    11/07/2012
  • Keywords:
    Diffusion MRI, Compartment Models, Brain White Matter, Model Selection
  • Addresses:
    Uran Ferizi
    University College London
    Centre for Medical Image Computing
    Malet Place
    WC1E 6BT
    United Kingdom
Abstract
Diffusion MRI (Magnetic Resonance Imaging) Microstructure Imaging provides a unique non-invasive probe into the microstructure of biological tissue. However, it relies on a mathematical model relating microscopic tissue features to the MR signal. This work aims to determine which models of diffusion MRI are best at describing the signal from in vivo brain white matter. The assumption of Gaussian diffusion in the most commonly used model, the Diffusion Tensor, oversimplifies the diffusive behaviour of water in complex media and is known to break down for relatively large diffusion weights (b-values). Recent work shows that three-compartment models, incorporating restricted intra-axonal compartments, glial compartments and hindered extra-cellular diffusion, are best at explaining multi b-value data sets from fixed brain tissue. Here we perform a similar experiment using in vivo human data to avoid, and evaluate, the effects of the fixation process. We compare one-, two- and three-compartment models, and rank them using two standard model selection criteria. Results show that, as with fixed tissue, threecompartment models explain the data best, although simpler models emerge from the in vivo data. Both changes in diffusion behaviour from fixation and the lower gradient strengths available in vivo are likely to contribute to the difference. The full ranking assists the choice of model and imaging protocol for future brain microstructure imaging.
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