Analytic tractography: A closed-form solution for estimating local white matter connectivity with diffusion MRI

TL;DR

This paper introduces an analytical method for estimating local white matter connectivity from diffusion MRI data, reducing computational costs and improving accuracy over traditional probabilistic and deterministic tractography methods.

Contribution

The authors present a closed-form solution for voxel transition probabilities in tractography, enabling faster and more accurate white matter connectivity analysis.

Findings

Analytical probabilities converge with probabilistic simulations as seed count increases.

Method accurately estimates ground-truth transition probabilities in phantom data.

Integrating the method into the Voxel Graph framework enables efficient white matter pathway analysis.

Abstract

White matter structures composed of myelinated axons in the living human brain are primarily studied by diffusion-weighted MRI (dMRI). These long-range projections are typically characterized in a two-step process: dMRI is used to estimate the orientation of axons within each voxel, then these local orientations are linked together to estimate the spatial extent of putative white matter bundles. Tractography, the process of tracing bundles across voxels, either requires computationally expensive (probabilistic) simulations to model uncertainty in fiber orientation or ignores it completely (deterministic). Probabilistic simulation necessarily generates a finite number of trajectories, introducing "simulation error" to trajectory estimates. Here we introduce a method to analytically (via a closed-form solution) take an orientation distribution function (ODF) from each voxel and calculate the probabilities that a trajectory projects from a voxel into each directly adjacent voxel. We validate our method by demonstrating that probabilistic simulations converge to our analytically computed probabilities at the voxel level as the number of simulated seeds increases. We show that our method accurately calculates the ground-truth transition probabilities from a phantom dataset. As a demonstration, we incoroporate our analytic method for voxel transition probabilities into the Voxel Graph framework, creating a quantitative framework for assessing white matter structure that we call "analytic tractography". The long-range connectivity problem is reduced to finding paths in a graph whose adjacency structure reflects voxel-to-voxel analytic transition probabilities. We demonstrate this approach performs comparably to many current probabilistic and deterministic approaches at a fraction of the computational cost. Open source software software is provided.