Meeting Banner
Abstract #3541

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

David Romascano1,2, Erick J. Canales-Rodriguez3, Jonathan Rafael-Patino1, Marco Pizzolato1, Gaëtan Rensonnet1,4, Muhamed Barakovic1, Gabriel Girard1,5, Alessandro Daducci6, Tim B. Dyrby2,7, and Jean-Philippe Thiran1,5

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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here