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

Chamberland, Maxime ORCID: https://orcid.org/0000-0001-7064-0984, St-Jean, Samuel, Tax, Chantal M. W. ORCID: https://orcid.org/0000-0002-7480-8817 and Jones, Derek K. ORCID: https://orcid.org/0000-0003-4409-8049 2019. Obtaining representative core streamlines for white matter tractometry of the human brain. Presented at: International MICCAI Workshop, Granada, Spain, Sep 2018. Published in: Bonet-Carne, E, Grussu, F, Ning, L, Sepehrband, F and Tax, C eds. Computational Diffusion MRI. Mathematics and Visualization. Mathematics and Visualization Cham: Springer, pp. 359-366. 10.1007/978-3-030-05831-9_28

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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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Cardiff University Brain Research Imaging Centre (CUBRIC) Psychology
Publisher:	Springer
ISBN:	978-3-030-05831-9
ISSN:	1612-3786
Last Modified:	06 May 2023 02:29
URI:	https://orca.cardiff.ac.uk/id/eprint/123690

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