Recognition of white matter bundles using local and global streamline-based registration and clustering

Eleftherios Garyfallidis, Marc-Alexandre Côté, Francois Rheault, Jasmeen Sidhu, Janice Hau, Laurent Petit, David Fortin, Stephen Cunanne, Maxime Descoteaux
NeuroImage. 2018-04-01; 170: 283-295
DOI: 10.1016/j.neuroimage.2017.07.015

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1. Neuroimage. 2018 Apr 15;170:283-295. doi: 10.1016/j.neuroimage.2017.07.015. Epub
2017 Jul 13.

Recognition of white matter bundles using local and global streamline-based
registration and clustering.

Garyfallidis E(1), Côté MA(2), Rheault F(2), Sidhu J(2), Hau J(3), Petit L(4),
Fortin D(5), Cunanne S(6), Descoteaux M(2).

Author information:
(1)Department of Intelligent Systems Engineering, School of Informatics and
Computing, Indiana University, Bloomington, USA. Electronic address:
.
(2)Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department,
Université de Sherbrooke, Sherbrooke, Canada.
(3)Brain Development Imaging Lab (BDIL), Department of Psychology, San Diego
State University, USA; Groupe d’ Imagerie Neurofonctionnelle, Institut des
Maladies Neurodégénératives, UMR5293, CNRS, CEA Université de Bordeaux, Bordeaux,
France.
(4)Groupe d’ Imagerie Neurofonctionnelle, Institut des Maladies
Neurodégénératives, UMR5293, CNRS, CEA Université de Bordeaux, Bordeaux, France.
(5)Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health
Science, University of Sherbrooke, Sherbrooke, Québec, Canada.
(6)Research Center on Aging and Faculté de Médecine et des Sciences de la Santé,
Université de Sherbrooke, Sherbrooke, Québec, Canada.

Virtual dissection of diffusion MRI tractograms is cumbersome and needs extensive
knowledge of white matter anatomy. This virtual dissection often requires several
inclusion and exclusion regions-of-interest that make it a process that is very
hard to reproduce across experts. Having automated tools that can extract white
matter bundles for tract-based studies of large numbers of people is of great
interest for neuroscience and neurosurgical planning. The purpose of our proposed
method, named RecoBundles, is to segment white matter bundles and make virtual
dissection easier to perform. This can help explore large tractograms from
multiple persons directly in their native space. RecoBundles leverages latest
state-of-the-art streamline-based registration and clustering to recognize and
extract bundles using prior bundle models. RecoBundles uses bundle models as
shape priors for detecting similar streamlines and bundles in tractograms.
RecoBundles is 100% streamline-based, is efficient to work with millions of
streamlines and, most importantly, is robust and adaptive to incomplete data and
bundles with missing components. It is also robust to pathological brains with
tumors and deformations. We evaluated our results using multiple bundles and
showed that RecoBundles is in good agreement with the neuroanatomical experts and
generally produced more dense bundles. Across all the different experiments
reported in this paper, RecoBundles was able to identify the core parts of the
bundles, independently from tractography type (deterministic or probabilistic) or
size. Thus, RecoBundles can be a valuable method for exploring tractograms and
facilitating tractometry studies.

Copyright © 2017 Elsevier Inc. All rights reserved.

DOI: 10.1016/j.neuroimage.2017.07.015
PMID: 28712994

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