Resolving bundle-specific intra-axonal T<sub>2</sub> values within a voxel using diffusion-relaxation tract-based estimation.

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Version: Final published version
License: CC BY-NC-ND 4.0
Serval ID
serval:BIB_644DE0B4360A
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Resolving bundle-specific intra-axonal T<sub>2</sub> values within a voxel using diffusion-relaxation tract-based estimation.
Journal
NeuroImage
Author(s)
Barakovic M., Tax CMW, Rudrapatna U., Chamberland M., Rafael-Patino J., Granziera C., Thiran J.P., Daducci A., Canales-Rodríguez E.J., Jones D.K.
ISSN
1095-9572 (Electronic)
ISSN-L
1053-8119
Publication state
Published
Issued date
15/02/2021
Peer-reviewed
Oui
Volume
227
Pages
117617
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
At the typical spatial resolution of MRI in the human brain, approximately 60-90% of voxels contain multiple fiber populations. Quantifying microstructural properties of distinct fiber populations within a voxel is therefore challenging but necessary. While progress has been made for diffusion and T <sub>1</sub> -relaxation properties, how to resolve intra-voxel T <sub>2</sub> heterogeneity remains an open question. Here a novel framework, named COMMIT-T <sub>2</sub> , is proposed that uses tractography-based spatial regularization with diffusion-relaxometry data to estimate multiple intra-axonal T <sub>2</sub> values within a voxel. Unlike previously-proposed voxel-based T <sub>2</sub> estimation methods, which (when applied in white matter) implicitly assume just one fiber bundle in the voxel or the same T <sub>2</sub> for all bundles in the voxel, COMMIT-T <sub>2</sub> can recover specific T <sub>2</sub> values for each unique fiber population passing through the voxel. In this approach, the number of recovered unique T <sub>2</sub> values is not determined by a number of model parameters set a priori, but rather by the number of tractography-reconstructed streamlines passing through the voxel. Proof-of-concept is provided in silico and in vivo, including a demonstration that distinct tract-specific T <sub>2</sub> profiles can be recovered even in the three-way crossing of the corpus callosum, arcuate fasciculus, and corticospinal tract. We demonstrate the favourable performance of COMMIT-T <sub>2</sub> compared to that of voxelwise approaches for mapping intra-axonal T <sub>2</sub> exploiting diffusion, including a direction-averaged method and AMICO-T <sub>2</sub> , a new extension to the previously-proposed Accelerated Microstructure Imaging via Convex Optimization (AMICO) framework.
Keywords
Algorithms, Axons, Brain/diagnostic imaging, Brain Mapping/methods, Computer Simulation, Diffusion Magnetic Resonance Imaging/methods, Humans, Image Processing, Computer-Assisted/methods, White Matter/diagnostic imaging, COMMIT, Diffusion MRI, Human brain, T(2) relaxometry, Tractography, White matter
Pubmed
Web of science
Open Access
Yes
Create date
22/12/2020 12:06
Last modification date
25/06/2024 7:30
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