@inproceedings{4fac5c127ac3414a90bff0b9c5236219,
title = "Obtaining representative core streamlines for white matter tractometry of the human brain",
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",
author = "Maxime Chamberland and Samuel St-jean and Tax, {Chantal M W} and Jones, {Derek K.}",
year = "2019",
month = may,
day = "3",
doi = "10.1007/978-3-030-05831-9_28",
language = "English",
isbn = "9783030058302",
series = "Mathematics and Visualization",
publisher = "Springer International Publishing AG",
pages = "359--366",
editor = "Elisenda Bonet-Carne and Farshid Sepehrband and Lipeng Ning and Francesco Grussu and Tax, {Chantal M.W.}",
booktitle = "Computational Diffusion MRI",
address = "Switzerland",
}