Global inference of the structural connectivity of white matter fiber bundles using deep learning approaches and microstructural prior knowledge

Mapping the structural connectivity of the human brain is a major scientific challenge. Describing the trajectory and connections made by the hundred billion neurons that make up the brain is a titanic and multi-scale task. The major fiber bundles have been described by classical anatomical approaches since the 20. century. These studies also revealed the existence of shorter bundles, called superficial bundles, that ensure the connectivity between neighboring anatomical regions. The small size and complex shape of these bundles set a serious challenge to their visualization, so that their description remains under discussion to this day. The first research axis of this thesis aims at pushing the limits of diffusion MRI and proposing a new ex-vivo dataset of the whole human brain, called Chenonceau, dedicated to the characterization of the fine connectivity of the brain.The dataset consists of two T2-weighted anatomical acquisitions at 100 and 150 micron resolution, as well as 175 dMRI datasets at 200 micron resolution with diffusion weighting reaching 8000 s/mm². More than 4500 hours of acquisition, distributed across two and a half years were necessary to acquire this data. Chenonceau takes advantage of the Bruker 11.7 T preclinical MRI system, equipped with both a high magnetic field and a powerful gradient tunnel (780 mT/m) allowing to reach the mesoscopic resolution and a very high diffusion weighting. To reconcile the large size of the human brain with the preclinical system, a new acquisition protocol is proposed. It is based on the separation of the brain into smaller samples, which are imaged individually, then reassembled in post-processing to reconstitute the full volume.The whole process is presented, including the protocol for the cutting and the storage of the anatomical samples, the details of the MRI sequences and the description of the image processing pipeline. Special attention is dedicated to the definition of the registration step which recomposes the whole volume from the individual acquisitions. The first inferences of anatomical connectivity from this new dataset are also presented. Tractography associated with clustering techniques allow the extraction of the long and superficial bundles of Chenonceau. The second part of the thesis focused on the development of a new method for fiber tracking, based on the use of the spin glass model. The latter expresses the tractography problem as a set of fiber fragments, called spins, distributed in the sample and whose position and orientation, as well as the connections they establish, are associated with an amount of energy. The construction of the tracts results from the displacement and connection of the spins, with the aim of reaching the global minimum of energy. This thesis proposes to replace the Metropolis-Hastings method used for optimization by an agent trained in a reinforcement learning framework.This new formulation aims at improving the choice of actions, which would no longer be randomly drawn, but dictated by a strategy learned by the agent, fruit of its past interactions with similar environments. The anticipation and projection capacities of such an agent appear particularly adequate to propose the most relevant trajectory in regions where the diffusion information is ambiguous. Moreover, the possibility for the algorithm to learn through interactions allows to circumvent the difficulty of establishing datasets of ground-truth bundles. (author)