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Updated: Sep 23, 2025

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

Bridging the gap between constrained spherical deconvolution and diffusional variance decomposition via tensor-valued

Philippe Karan¹, Alexis Reymbaut¹, Guillaume Gilbert²

¹Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada.

Medical Image Analysis

|May 15, 2022

Summary

This summary is machine-generated.

This study optimizes diffusion MRI protocols for better white matter imaging. A new 10-minute protocol combining linear and spherical encodings accurately reconstructs fiber orientations and microstructure measures.

Keywords:

Constrained spherical deconvolution Diffusional variance decomposition Tensor-valued dMRI

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Diffusion Imaging in the Rat Cervical Spinal Cord

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Published on: April 7, 2015

Area of Science:

Neuroimaging
Diffusion MRI
White Matter Imaging

Background:

Diffusion Tensor Imaging (DTI) offers limited specificity for white matter (WM) fiber orientations, especially at crossings.
Constrained Spherical Deconvolution (CSD) improves orientation estimation using High Angular Resolution Diffusion Imaging (HARDI).
Tensor-valued diffusion MRI with Diffusional Variance Decomposition (DIVIDE) provides microstructure-specific measures like microscopic Fractional Anisotropy (μFA).

Purpose of the Study:

To investigate the impact of diffusion encoding combinations on CSD-based fiber orientation distribution function (fODF) reconstruction.
To evaluate the performance of CSD and DIVIDE for accurate μFA computation with tensor-valued diffusion MRI data.
To develop and validate an optimized 10-minute diffusion MRI protocol for simultaneous fODF and μFA reconstruction.

Main Methods:

Simulated diffusion MRI data with varying diffusion encodings were used to assess fODF angular resolution and CSD versatility.
Performance comparison of different encoding combinations for accurate and precise μFA estimation using DIVIDE.
Development of a combined linear and spherical b-tensor encoding protocol optimized for speed and data quality.

Main Results:

The study explored the effects of various diffusion encoding combinations on fODF reconstruction quality.
Performance metrics for μFA accuracy and precision were compared across different protocols.
An optimized 10-minute protocol was proposed, demonstrating successful in vivo reconstruction of fODFs and μFA.

Conclusions:

Combining CSD and DIVIDE with tensor-valued diffusion MRI offers enhanced insights into white matter microstructure and orientation.
The proposed optimized protocol provides a time-efficient solution for simultaneous acquisition of detailed microstructural and orientational information.
This advancement holds promise for more specific and accurate neuroimaging analysis in clinical and research settings.