Optimization of acquisition in diffusion MRI towards quantitative connectivity

Publié le jeu 28/04/2022 - 14:00
Date de début de thèse (si connue)
Unité de recherche
IRISA - UMR 6074
Description du sujet de la thèse

Diffusion Magnetic Resonance Imaging (dMRI) is an imaging technique sensible to the self-displacement of water molecules. In brain imaging, it provdes invaluable information on properties of the brain tissue organization at a micrometer scale, several orders of magnitude below the typical resolution of conventional MRI. In white matter, due to the anisotropic nature of diffusion within axonal fiber bundles, it is possible to trace the path of major tracts using tractography, therefore providing an in vivo image of the global brain structural connectivity. The reconstruction of such a high level representation of the brain as a graph paved the way to an unprecedented description of the normal, developing and aging brain and the detection of subtle changes in the global organisation of the brain associated with pathology. However, current tractography algorithms still present inherent challenges, such as a large number of false positives and a relatively poor quantitative estimation of connection strength.

Over the past 5 years, there has been a growing interest of the community in developing advanced acquisition schemes aiming at a more accurate reconstruction of tissue properties locally, thus providing a better sensitivity and specificity to focal changes related to pathology and paving the way for better biomarkers. In particular, the use of generalized diffusion-encoding gradient waveforms were shown to increase the sensitivity to specific microstructure parameters. Intuitively, a better characterization of microstructure locally shall help in the reconstruction of the connectome; yet to date very few studies have focused on the impact of the acquisition scheme on the reconstruction of the connectome. The main objective of this project will be to bridge the gap between acquisition design in the one hand and quantitative connectivity mapping in the other hand.

  1. Denis Le Bihan, E Breton et al. « Imagerie de diffusion in-vivo par resonance magnetique nucleaire ». In : Comptes-Rendus de l’Académie des Sciences 93.5 (1985), p. 27-34.
  2. Daniel C Alexander, Penny L Hubbard, Matt G Hall, Elizabeth A Moore, Maurice Ptito, Geoff JM Parker et Tim B Dyrby. « Orientationally invariant indices of axon diameter and density from diffusion MRI ». In : Neuroimage 52.4 (2010), p. 1374-1389.
  3. Klaus H Maier-Hein, Peter F Neher, Jean-Christophe Houde, Marc-Alexandre Côté, Eleftherios Garyfallidis, Jidan Zhong, Maxime Chamberland, Fang-Cheng Yeh, Ying-Chia Lin, Qing Ji et al. « The challenge of mapping the human connectome based on diffusion tractography ». In : Nature communications 8.1 (2017), p. 1-13.
  4. Alessandro Daducci, Alessandro Dal Palù, Alia Lemkaddem et Jean-Philippe Thiran. « COMMIT : Convex optimization modeling for microstructure informed tractography ». In : IEEE transactions on medical imaging 34.1 (2014), p. 246-257.
  5. Emmanuel J Candès et Michael B Wakin. « An introduction to compressive sampling ». In : IEEE signal processing magazine 25.2 (2008), p. 21-30.
  6. Nicolas Moutal, Ivan I Maximov et Denis S Grebenkov. « Probing surface-to-volume ratio of an anisotropic medium by diffusion NMR with general gradient encoding ». In : IEEE transactions on medical imaging 38.11 (2019), p. 2507-2522.
  7. Xiaowen Dong, Dorina Thanou, Michael Rabbat et Pascal Frossard. « Learning graphs from data : A signal representation perspective ». In : IEEE Signal Processing Magazine 36.3 (2019), p. 44-63.
  8. Gaëtan Rensonnet, Benoit Scherrer, Gabriel Girard, Aleksandar Jankovski, Simon K Warfield, Benoit Macq, Jean-Philippe Thiran et Maxime Taquet. « Towards microstructure fingerprinting : estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations ». In : NeuroImage 184 (2019), p. 964-980.
  9. Emmanuel Caruyer, Alessandro Daducci, Maxime Descoteaux, Jean-Christophe Houde, Jean-Philippe Thiran et Ragini Verma. « Phantomas : a flexible software library to simulate diffusion MR phantoms ». In : Ismrm. 2014.
  10. Jonathan Rafael-Patino, Gabriel Girard, Raphaël Truffet, Marco Pizzolato, Emmanuel Caruyer et Jean-Philippe Thiran. « The diffusion-simulated connectivity (DiSCo) dataset ». In : Data in Brief (2021), p. 107429.
  11. Raphaël Truffet, Jonathan Rafael-Patino, Gabriel Girard, Marco Pizzolato, Christian Barillot, et al.. An evolutionary framework for microstructure-sensitive generalized diffusion gradient waveforms. MICCAI 2020 - 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2020, Lima, Peru. pp.1-11.
Liste des encadrants et encadrantes de thèse

Nom, Prénom
Caruyer, Emmanuel
Type d'encadrement
Directeur.trice de thèse
Unité de recherche

Nom, Prénom
Corouge, Isabelle
Type d'encadrement
Unité de recherche
UMR 6074
Caruyer, Emmanuel
image processing, signal processing, compressed sensing, neuroimaging, diffusion MRI