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Related Experiment Video

Updated: Jan 24, 2026

DTI of the Visual Pathway - White Matter Tracts and Cerebral Lesions

DTI of the Visual Pathway - White Matter Tracts and Cerebral Lesions

Published on: August 26, 2014

Combining white matter diffusion and geometry for tract-specific alignment and variability analysis.

Itay Benou¹, Ronel Veksler², Alon Friedman³

¹Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel; The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel.

|May 17, 2019

Summary

This summary is machine-generated.

A new framework using Fiber-Flux Diffusion Density (FFDD) analyzes white matter tracts for subtle abnormalities. This method improves sensitivity by combining diffusion and geometry, outperforming existing DTI registration techniques.

Keywords:

Diffusion MRI Fiber bundle Mild traumatic brain injury Tract-specific analysis Tractography registration White matter

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Area of Science:

Neuroimaging
Biomedical Engineering
Computational Neuroscience

Background:

Diffusion Tensor Imaging (DTI) and tractography are crucial for analyzing white matter (WM) fiber bundles.
Current methods may miss subtle structural abnormalities by analyzing diffusion or geometry independently.
A need exists for integrated approaches capturing both microscopic and macroscopic WM tract features.

Purpose of the Study:

To introduce a novel framework, Fiber-Flux Diffusion Density (FFDD), for along-tract analysis of WM fiber bundles.
To develop a robust method for sub-voxel alignment of fiber tracts, enabling meaningful inter-subject comparisons.
To demonstrate the framework's superior sensitivity and performance compared to existing DTI registration algorithms.

Main Methods:

Developed Fiber-Flux Diffusion Density (FFDD) by combining diffusion-based measures with fiber-flux density for WM tract modeling.
Utilized the Fast Marching Method (FMM) to construct an FFDD dissimilarity measure for sub-voxel alignment of fiber bundles.
Applied the framework to Human Connectome Project (HCP) and contact sports datasets, including longitudinal and group studies.

Main Results:

The FFDD framework successfully captured both microscopic and macroscopic WM tract features.
FMM-based alignment outperformed a commonly used DTI registration algorithm.
Statistically significant FFDD differences were found between football players and controls, with longitudinal changes observed in players exposed to head trauma.

Conclusions:

The proposed FFDD framework offers improved sensitivity for detecting subtle WM abnormalities by integrating diffusion and geometric information.
The FMM-based alignment technique provides accurate and efficient co-alignment of multiple fiber bundles.
FFDD analysis is a promising tool for investigating the effects of repetitive head trauma and traumatic brain injury on white matter structure.