Computing and visualising intra‐voxel orientation‐specific relaxation–diffusion features in the human brain. Issue 2 (6th October 2020)

• Record Type: Journal Article
• Authors: de Almeida Martins, João P.; Tax, Chantal M. W.; Reymbaut, Alexis; Szczepankiewicz, Filip; Chamberland, Maxime; Jones, Derek K.; Topgaard, Daniel
• Is Part Of: Human brain mapping. Volume 42:Issue 2(2021)
• Page Start: 310
• Page End: 328
• Publication Date: 2020-10-06
• Subjects: diffusion MRI -- fibre ODF -- fibre‐specific metrics -- partial volume effects -- tensor‐valued diffusion encoding -- white matter; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25224
• Languages: English
• ISSNs: 1065-9471

Abstract

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. Abstract : Diffusion MRI techniques designed to resolve fibre crossings within a given white matter (WM) voxel typically assume that the voxel‐level microstructural features can be represented by a single signal response function; this precludes the investigation of microscopic differences between the sub‐voxel fibre populations. In this work, we build on a recently introduced 5D relaxation–diffusion correlation MRI framework and present an analysis protocol for deriving and visualising metrics informing on the relaxation rates and diffusivities of distinct fibres. Experiments on a healthy volunteer demonstrate that the presented approach can capture crossings between distinct WM tracts of the human brain and inform on their individual relaxation–diffusion properties.