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

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 echotimes 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.

Toward nonparametric diffusion‐ characterization of crossing fibers in the human brain

“…Orientation distribution functions were defined from the thin‐bin components output by the Monte Carlo inversion of Section 2.2.1 using the procedure detailed in Refs. [75,76,95]. This procedure consists in mapping the set of thin‐bin components onto the nodes of a dense spherical mesh , building up the ODF radii from the thin‐bin component weights and orientations .…”

“…[41,45,72‐74], respectively. Enhanced by methods aiming to visualize and quantify fiber‐specific properties, Monte Carlo signal inversions have recently provided critical sensitivity and specificity to fiber‐specific values 75‐77 . However, this work has yet to be extended to fiber‐specific ‐values, which are of particular interest to evaluate changes in bundle‐specific myelin contents 78 relevant to the study of neurodevelopment, plasticity, aging, and neurological disorders 79‐81 …”

“…We first estimate parameter maps of ’s statistical descriptors and extract orientation‐resolved values within the pool of highly anisotropic components output by the Monte Carlo inversion algorithm. These values are then color‐mapped onto nonparametric orientation distribution functions (ODFs) 75,76 and quantified in terms of median value and precision using Monte Carlo density‐peak clustering (MC‐DPC) 77 . In particular, we identify significant differences with respect to relaxation between major WM bundles without relying on limiting assumptions, albeit in a single healthy volunteer.…”

“…Enhanced by methods aiming to visualize and quantify fiber-specific properties, Monte Carlo signal inversions have recently provided critical sensitivity and specificity to fiber-specific T 2 values. [75][76][77] However, this work has yet to be extended to fiber-specific T 1 -values, which are of particular interest to evaluate changes in bundle-specific myelin contents 78 relevant to the study of neurodevelopment, plasticity, aging, and neurological disorders. [79][80][81] In this work, we leverage nonparametric distributions (D, R 1 ) of diffusion tensors D and longitudinal relaxation rates R 1 = 1∕T 1 obtained via Monte Carlo inversion of a 5D diffusion-T 1 weighted in vivo human brain dataset to resolve subvoxel diffusion-R 1 components.…”

DIffusion-Prepared Phase Imaging (DIPPI): quantifying myelin in crossing fibres

“…Diffusion-weighting gradients can be used to distinguish between crossing fibres. Diffusion-weighting has previously been combined with all of the myelin-sensitive metrics listed above to obtain tract-specific metrics, namely T 2 33–35 , T 2 * 34,36 , T 1 34,37 , and magnetisation transfer 38 . Unfortunately, diffusion-weighted gradients take such a long time to build up this sensitivity to fibre orientation that there will be very little signal left associated with the myelin water due to its short T 2 39 .…”