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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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