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Abstract #0715

Characterisation of cerebellar microstructure with two-compartment Spherical Mean Technique

Giovanni Savini1,2, Fulvia Palesi2,3, Gloria Castellazzi2,4, Letizia Casiraghi2,5, Francesco Grussu6, Alessandro Lascialfari1,3, Egidio D'Angelo2,5, and Claudia AM Gandini Wheeler-Kingshott5,6,7

1Department of Physics, University of Milan, Milan, Italy, 2Brain Connectivity Center, C. Mondino National Neurological Institute, Pavia, Italy, 3Department of Physics, University of Pavia, Pavia, Italy, 4Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy, 5Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy, 6Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, United Kingdom, 7Brain MRI 3T Mondino Research Center, C. Mondino National Neurological Institute, Pavia, Italy

Cerebrum microstructure has been extensively investigated with diffusion-weighted MRI, but little attention has been dedicated to the microstructural characterisation of the cerebellum.

We considered an anatomical parcellation of the cerebellum and fitted a multi-compartment model to diffusion data exploiting the spherical mean technique, which provides parametric maps unconfounded by the underlying fibre orientation distribution. For each region we report average values for multi-compartment parameters (e.g. intra-neurite volume fraction and intrinsic diffusivity) and diffusion tensor metrics.

Multi-compartment metrics more specific to microstructure provide information complementary to diffusion tensor metrics in the cerebellum, thus giving new insights about microstructural correlations between regions.

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