Obtaining representative core streamlines for white matter tractometry of the human brain

Publication date

2019-05-03

Authors

Chamberland, Maxime
St-Jean, Samuel
Tax, Chantal M W
Jones, Derek K.

Editors

Bonet-Carne, Elisenda
Sepehrband, Farshid
Ning, Lipeng
Grussu, Francesco
Tax, Chantal M.W.

Advisors

Supervisors

Document Type

Part of book

Collections

Open Access logo

License

Abstract

Diffusion MRI infers information about the micro-structural architecture of the brain by probing the diffusion of water molecules. The process of virtually reconstructing brain pathways based on these measurements is called tractography. Various metrics can be mapped onto pathways to study their micro-structural properties. Tractometry is an along-tract profiling technique that often requires the extraction of a representative streamline for a given bundle. This is traditionally computed by local averaging of the spatial coordinates of the vertices, and constructing a single streamline through those averages. However, the resulting streamline can end up being highly non-representative of the shape of the individual streamlines forming the bundle. In particular, this occurs when there is variation in the topology of streamlines within a bundle (e.g., differences in length, shape or branching). We propose an envelope-based method to compute a representative streamline that is robust to these individual differences. We demonstrate that this method produces a more representative core streamline, which in turn should lead to more reliable and interpretable tractometry analyses.

Keywords

Bundle envelope, Core streamline, Diffusion MRI, Tractography, Tractometry, Modelling and Simulation, Geometry and Topology, Computer Graphics and Computer-Aided Design, Applied Mathematics

Citation

Chamberland, M, St-jean, S, Tax, C M W & Jones, D K 2019, Obtaining representative core streamlines for white matter tractometry of the human brain. in E Bonet-Carne, F Sepehrband, L Ning, F Grussu & C M W Tax (eds), Computational Diffusion MRI. Mathematics and Visualization, Springer International Publishing AG, Granada, pp. 359-366. https://doi.org/10.1007/978-3-030-05831-9_28