title: Robust and efficient linear registration of white-matter fascicles in the space of streamlines.
abstract: The neuroscientific community today 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 paper, 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.
authors.affiliation: Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science department, Université de Sherbrooke, 2500 Boulevard Université, Sherbrooke, QC J1K 2R1, Canada. Electronic address: garyfallidis@gmail.com.
authors.forename: Eleftherios
authors.initials: E
authors.lastname: Garyfallidis
authors.affiliation: Centro de Investigacion en Matematicas, Guanajuato, Gto, Mexico.
authors.forename: Omar
authors.initials: O
authors.lastname: Ocegueda
authors.affiliation: Equipe Projet Athena, INRIA Sophia-Antipolis Méditerranée, France.
authors.forename: Demian
authors.initials: D
authors.lastname: Wassermann
authors.affiliation: Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science department, Université de Sherbrooke, 2500 Boulevard Université, Sherbrooke, QC J1K 2R1, Canada.
authors.forename: Maxime
authors.initials: M
authors.lastname: Descoteaux
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009412:Nerve Fibers; D009434:Neural Pathways / Q000033:anatomy & histology; D066127:White Matter / Q000033:anatomy & histology*
keywords: Bundles; Diffusion MRI; Fascicles; Fiber tracking; Registration; Streamlines; Tracts