In vivo fiber tractography using DT-MRI data

TLDR

The authors compute fiber tract trajectories from in vivo DT‑MRI by constructing a continuous diffusion tensor field from noisy discrete data, solving a Frenet equation, and validating the approach on synthesized noisy datasets. The method produced callosal and pyramidal tract trajectories matching known anatomy, but its reliability drops in regions of nonuniform fiber direction and with background noise, yet it still offers quantitative visualization of neural connectivity in vivo and potential for other fibrous tissues.

Abstract

Fiber tract trajectories in coherently organized brain white matter pathways were computed from in vivo diffusion tensor magnetic resonance imaging (DT-MRI) data. First, a continuous diffusion tensor field is constructed from this discrete, noisy, measured DT-MRI data. Then a Frenet equation, describing the evolution of a fiber tract, was solved. This approach was validated using synthesized, noisy DT-MRI data. Corpus callosum and pyramidal tract trajectories were constructed and found to be consistent with known anatomy. The method's reliability, however, degrades where the distribution of fiber tract directions is nonuniform. Moreover, background noise in diffusion-weighted MRIs can cause a computed trajectory to hop from tract to tract. Still, this method can provide quantitative information with which to visualize and study connectivity and continuity of neural pathways in the central and peripheral nervous systems in vivo, and holds promise for elucidating architectural features in other fibrous tissues and ordered media.