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Updated: Mar 22, 2026

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

White matter microstructure from nonparametric axon diameter distribution mapping.

Dan Benjamini¹, Michal E Komlosh², Lynne A Holtzclaw³

¹Quantitative Imaging and Tissue Sciences, NICHD, National Institutes of Health, Bethesda, MD 20892, USA; Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel-Aviv University, Tel-Aviv, Israel.

|April 30, 2016

Summary

This summary is machine-generated.

A new double diffusion encoding MRI method non-parametrically estimates axon diameter distribution (ADD). This technique maps microstructural changes in nervous systems, aiding in understanding neurodegenerative diseases and trauma.

Keywords:

Average axon diameter Axon diameter distribution Double diffusion encoding Double pulsed field gradient Empirical MRI Nonparametric Pore size distribution

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Area of Science:

Neuroimaging
Biophysics
Medical Physics

Background:

Changes in axon diameter distribution (ADD) are linked to neurological development, disease, and trauma.
Existing methods often rely on a priori assumptions about ADD, limiting their applicability to abnormal tissues.
A flexible, non-parametric approach is needed for accurate ADD estimation in diverse biological contexts.

Purpose of the Study:

To develop and validate a novel double diffusion encoding (DDE) MRI method for estimating and mapping axon diameter distribution (ADD).
To explore the impact of weighting ADD by axon number versus volume on spatial contrast.
To demonstrate the utility of DDE-derived maps for white matter segmentation and identification of distinct white matter tracks.

Main Methods:

Employed a double diffusion encoding (DDE) MRI sequence.
Developed a general, non-parametric framework for estimating ADD without prior assumptions.
Applied the method to ex vivo ferret spinal cord, analyzing DDE data to generate maps of average axon diameter, ADD variance, and extra-axonal volume fraction.
Utilized a k-means clustering algorithm with spatial constraints for white matter segmentation based on derived morphological information.

Main Results:

Generated spatially resolved maps of average axon diameter, ADD variance, and extra-axonal volume fraction.
Introduced a novel map for sub-micron restricted structures.
Successfully segmented white matter into distinct domains, identifying white matter tracks.
Validated the DDE method against histological measures, showing good agreement.

Conclusions:

The developed DDE MRI method provides a robust, non-parametric approach for estimating and mapping ADD.
The method enables detailed microstructural analysis of nervous system tissue, including abnormal conditions.
This framework holds potential for preclinical and clinical neuroimaging applications, enhancing the understanding of neurodegenerative pathologies and trauma-induced microstructural changes.