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Related Experiment Video

Updated: Nov 6, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Decoding the microstructural properties of white matter using realistic models.

Renaud Hédouin¹, Riccardo Metere², Kwok-Shing Chan²

¹Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands; Empenn, INRIA, INSERM, CNRS, Université de Rennes 1, Rennes, France.

|May 8, 2021

Summary

This summary is machine-generated.

Realistic 2D white matter models accurately simulate MR signals, enabling robust in silico prediction of microstructural parameters like fiber volume fraction and g-ratio using deep learning.

Keywords:

Deep learning network Magnetic susceptibility Microstructural properties White matter models

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Last Updated: Nov 6, 2025

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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
Biophysics
Computational Neuroscience

Background:

White matter (WM) magnetic resonance signal (MRS) evolution depends on axon orientation.
Realistic WM models improve signal prediction over simplified analytical solutions.
Multi-echo gradient echo (ME-GRE) sequences are sensitive to microstructural properties.

Purpose of the Study:

Develop a pipeline for generating realistic 2D WM models.
Simulate MR signals from these models.
Validate 2D models for predicting microstructural parameters using deep learning.

Main Methods:

Generate realistic 2D WM models with adjustable fiber volume fraction (FVF) and g-ratio.
Simulate ME-GRE MR signal evolution in static magnetic fields.
Train a deep learning network to recover microstructural parameters from simulated and ex vivo data.

Main Results:

Realistic 2D WM models approximate MR signals from 3D WM models.
In silico prediction of FVF, g-ratio, water relaxation, and susceptibility is robust with multi-orientation ME-GRE data.
Deep learning successfully recovers microstructural parameters from ex vivo data, even with as few as 3 orientations.

Conclusions:

Realistic 2D WM models are valuable tools for simulating MR signals and predicting microstructural properties.
Deep learning combined with multi-orientation ME-GRE data offers a powerful approach for non-invasive WM microstructure quantification.
The problem is overdetermined, allowing accurate parameter recovery with reduced orientation data.