Abstract #3541

HOTmix: characterizing hindered diffusion using a mixture of generalized higher order tensors

David Romascano^1,2, Erick J. Canales-Rodriguez³, Jonathan Rafael-Patino¹, Marco Pizzolato¹, Gaëtan Rensonnet^1,4, Muhamed Barakovic¹, Gabriel Girard^1,5, Alessandro Daducci⁶, Tim B. Dyrby^2,7, and Jean-Philippe Thiran^1,5

¹Signal Processing Lab (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ²Danish Research Center for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark, ³Department of Radiology, University Hospital Center, Lausanne, Switzerland, ⁴ICTEAM, Université catholique de Louvain, Louvain-la-Neuve, Belgium, ⁵Department of Radiology, University Hospital Center and University of Lausanne, Lausanne, Switzerland, ⁶Department of Computer Science, University of Verona, Verona, Italy, ⁷Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark

We present HOTmix, a new model to describe the diffusion MRI signal for molecules undergoing hindered diffusion. HOTmix is based on a mixture of generalized higher order tensors, explicitly incorporating the diffusion sequence’s time-dependent parameters. The method was evaluated on simulated diffusion MRI signals obtained through Monte Carlo simulations, using intermediate diffusion times, mimicking both ex-vivo and in-vivo conditions. HOTmix provided better reconstructions compared to the standard diffusion tensor, the kurtosis tensor, and a single generalized higher order tensor. In future work, we will explore whether modelling the hindered compartment using HOTmix improves microstructural features estimated using dMRI.

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