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September 17, 2025

Enhancing Diffusion MR Tractography Using a Deep Learning Model that Incorporates Anatomical Knowledge

Key Points

Tractography using the deep learning model estimated fiber orientation distributions with fewer false positives compared to standard methods.
The network’s performance remained consistent even with input images of lower angular resolution and added noise.
An augmented dataset based on known white matter pathways was utilized to train the deep neural network effectively.
This approach may enable more reliable brain connectivity studies, especially in vulnerable populations such as children and seniors.

Abstract

Motivation: To develop reliable diffusion MRI tractography to study brain connectivity. Goal(s): The study aims to improve the estimation of fiber orientation distribution (FOD), which is key to improve the specificity of tractography. Approach: We created an augmented streamline dataset based on known white matter pathways to train a deep neural network to estimate FOD from diffusion MRI signals. Results: Tractography based on the network estimated FODs showed reduced false-positives compared to conventional methods. The improvement remained for input data with reduced angular resolutions and added noise. Impact: The proposed method can improve tractography by reducing false-positives and benefit studies on structural connectivity of the brain. Furthermore, it may shorten the acquisition time required for robust tractography, which is important for studies on children and seniors.

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Enhancing Diffusion MR Tractography Using a Deep Learning Model that Incorporates Anatomical Knowledge

Key Points

Abstract

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