Section: New Results

Computational Diffusion MRI

Robust and efficient linear registration of white-matter fascicles in the space of streamlines

Participants : Eleftherios Garyfallidis [SCIL Lab., Sherbrooke University] , Omar Cepeda [SCIL Lab., Sherbrooke University] , Demian Wassermann, Maxime Descoteaux [SCIL Lab., Sherbrooke University] .

The neuroscientific community is very much interested in analyzing specific white matter bundles like the arcuate fasciculus, the corticospinal tract, or the recently discovered Aslant tract to study sex differences, lateralization and many other connectivity applications. For this reason, experts spend time manually segmenting these fascicles and bundles using streamlines obtained from diffusion MRI tractography. However, to date, there are very few computational tools available to register these fascicles directly so that they can be analyzed and their differences quantified across populations. In this work, we introduce a novel, robust and efficient framework to align bundles of streamlines directly in the space of streamlines. We call this framework Streamline-based Linear Registration. We first show that this method can be used successfully to align individual bundles as well as whole brain streamlines. Additionally, if used as a piecewise linear registration across many bundles, we show that our novel method systematically provides higher overlap (Jaccard indices) than state-of-the-art nonlinear image-based registration in the white matter. We also show how our novel method can be used to create bundle-specific atlases in a straightforward manner and we give an example of a probabilistic atlas construction of the optic radiation. In summary, Streamline-based Linear Registration provides a solid registration framework for creating new methods to study the white matter and perform group-level tractometry analysis.

This work has been published in [14]

Cortical surface parcellation via dMRI using mutual nearest neighbor condition

Participants : Brahim Belaoucha, Maureen Clerc, Théodore Papadopoulo.

In this work, we present a method that aims at parcellating the cortical surface from individual anatomy. The parcellation is obtained using the mutual nearest neighbor criteria to obtain regions that have similar fiber distribution. The later is obtained by applying a probabilistic tractography on the diffusion MRI (dMRI), a non-invasive modality allowing the access to the structural information of the cortical surface. The proposed algorithm is compared to some of the atlases that can be found in the literature. We show that these atlases have lower similarity of fibers distributions than the proposed algorithm.

This work has been accepted at the conference ISBI 2016.