The Importance of Being Dispersed: A Ranking of Diffusion MRI Models for Fibre Dispersion Using In Vivo Human Brain Data

Uran Ferizi^{1, 2}, Torben Schneider², Maira Tariq¹, Claudia A.M. Wheeler-Kingshott², Hui Zhang¹, and Daniel C. Alexander¹

¹CMIC, Dept. Computer Science and Dept. Medical Physics and Bioengineering, University College London, United Kingdom
uran.ferizi.10@ucl.ac.uk

²NMR Research Unit, Department of Neuroinflammation, Institute of Neurology, University College London, United Kingdom

Abstract. 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 fibre dispersion are preferred. The results show the importance of modelling dispersion, even in apparently coherent fibres.

LNCS 8149, p. 74 ff.

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