Computing and visualising intra-voxel orientation-specific relaxation-diffusion features in the human brain

de Almeida Martins, João P., Tax, Chantal M. W. ORCID: https://orcid.org/0000-0002-7480-8817, Reymbaut, Alexis, Szczepankiewicz, Filip, Chamerland, Maxime, Jones, Derek K. ORCID: https://orcid.org/0000-0003-4409-8049 and Topgaard, Daniel 2021. Computing and visualising intra-voxel orientation-specific relaxation-diffusion features in the human brain. Human Brain Mapping 42 (2) , pp. 310-328. 10.1002/hbm.25224

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Abstract

Diffusion MRI techniques are used widely to study the characteristics of the human brain connectome in vivo. However, to resolve and characterise white matter (WM) fibres in heterogeneous MRI voxels remains a challenging problem typically approached with signal models that rely on prior information and constraints. We have recently introduced a 5D relaxation–diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo‐times to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation–diffusion distributions where contributions from different sub‐voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre‐specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation‐specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along with distinct fibre bundles. If combined with fibre‐tracking algorithms, the methodology presented in this work has potential for increasing the depth of characterisation of microstructural properties along individual WM pathways.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology Cardiff University Brain Research Imaging Centre (CUBRIC)
Publisher:	Wiley
ISSN:	1065-9471
Date of First Compliant Deposit:	16 October 2020
Date of Acceptance:	22 September 2020
Last Modified:	06 May 2023 01:01
URI:	https://orca.cardiff.ac.uk/id/eprint/135674

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