Abstract #2628
Ranking Diffusion MRI Models for Fibre Dispersion using In Vivo Human Brain Data
Uran Ferizi 1,2 , Torben Schneider 2 , Eleftheria Panagiotaki 1 , Maira Tariq 1 , Hui Zhang 1 , Claudia A. M. Wheeler-Kingshott 2 , and Daniel C. Alexander 1
1
Department of Computer Science and Centre
for Medical Image Computing, University College London,
London, United Kingdom,
2
NMR
Research Unit, Department of Neuroinflammation, Queen
Square MS Centre, UCL Institute of Neurology, London,
United Kingdom
In this work we compare parametric diffusion MRI models
which explicitly seek to explain fibre dispersion in
nervous tissue. These models aim at providing more
specific biomarkers of disease by disentangling these
structural contributions to the signal. Some models are
drawn from recent work in the field; others have been
constructed from combinations of existing compartments
that aim to capture both intracellular and extracellular
diffusion. To test these models we use a rich dataset
acquired in vivo on the corpus callosum of a human
brain, and then compare the models via the Bayesian
Information Criteria. We test this ranking via
bootstrapping on the data sets, and cross-validate
across unseen parts of the protocol. We find that models
that capture fiber dispersion are preferred. The results
show the importance of modelling dispersion, even in
apparently coherent fibers.
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