Multi-Spherical Diffusion MRI: Exploring Diffusion Time Using Signal Sparsity

Rutger H.J. Fick (1) , Alexandra Petiet (2) , Mathieu Santin (2) , Anne-Charlotte Philippe (2) , Stéphane Lehéricy (2) , Rachid Deriche (1) , Demian Wassermann (1)

1 ATHENA - Computational Imaging of the Central Nervous System
2 CENIR - Plateforme de neuroimagerie CENIR = Center for NeuroImaging Research-Human MRI Neuroimaging core facility for clinical research [ICM Paris]

Effective representation of the diffusion signal's dependence on diffusion time is a sought-after, yet still unsolved, challenge in diffusion MRI (dMRI). We propose a functional basis approach that is specifically designed to represent the dMRI signal in this four-dimensional space – varying over gradient strength, direction and diffusion time. In particular , we provide regularization tools imposing signal sparsity and signal smoothness to drastically reduce the number of measurements we need to probe the properties of this multi-spherical space. We illustrate a novel application of our approach, which is the estimation of time-dependent q-space indices, on both synthetic data generated using Monte-Carlo simulations and in vivo data acquired from a C57Bl6 wild-type mouse. In both cases, we find that our regularization approach stabilizes the signal fit and index estimation as we remove samples, which may bring multi-spherical diffusion MRI within the reach of clinical application.

Mots clés: Multi-Spherical MRI; Diffusion MRI; Diffusion Time; Ensemble Average Propagator

Domaines: Imagerie; Imagerie médicale

Rutger H.J. Fick, Alexandra Petiet, Mathieu Santin, Anne-Charlotte Philippe, Stéphane Lehéricy, et al.. Multi-Spherical Diffusion MRI: Exploring Diffusion Time Using Signal Sparsity. MICCAI 2016 Workshop on Computational Diffusion MRI (CDMRI'16), Oct 2016, Athènes, Greece. ⟨hal-01354985⟩