Tractography with T1-weighted MRI and associated anatomical constraints on clinical quality diffusion MRI

TL;DR

This paper introduces a neural network-based method for tractography using T1-weighted MRI data, enabling reliable white matter pathway estimation at clinical resolutions, which broadens applicability to diverse populations.

Contribution

It generalizes a neural tractography approach to clinical resolution data, making it applicable to standard MRI scans and diverse study populations.

Findings

T1-based tractography reproduces diffusion tractography with about 2mm error.

The method works on clinical resolution data from aging populations.

The epsilon ball seeding metric effectively compares tractography methods.

Abstract

Diffusion MRI (dMRI) streamline tractography, the gold standard for in vivo estimation of brain white matter (WM) pathways, has long been considered indicative of macroscopic relationships with WM microstructure. However, recent advances in tractography demonstrated that convolutional recurrent neural networks (CoRNN) trained with a teacher-student framework have the ability to learn and propagate streamlines directly from T1 and anatomical contexts. Training for this network has previously relied on high-resolution dMRI. In this paper, we generalize the training mechanism to traditional clinical resolution data, which allows generalizability across sensitive and susceptible study populations. We train CoRNN on a small subset of the Baltimore Longitudinal Study of Aging (BLSA), which better resembles clinical protocols. Then, we define a metric, termed the epsilon ball seeding method, to compare T1 tractography and traditional diffusion tractography at the streamline level. Under this metric, T1 tractography generated by CoRNN reproduces diffusion tractography with approximately two millimeters of error.