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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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Voxel-Specific Eigenvalue Approach for Improved Fiber Orientation Estimation in Diffusion MRI.

Ashishi Puri¹, Sanjeev Kumar^2,3

¹Department of Mathematics, Applied Science Cluster, UPES, Dehradun, Uttarakhand, India.

NMR in Biomedicine

|December 21, 2025

Summary

This summary is machine-generated.

This study introduces a new diffusion tensor imaging (DTI) method that dynamically calculates diffusion properties for each voxel. This improves white matter fiber reconstruction and accuracy in neuroimaging analysis.

Keywords:

brain diffusion tensor matrix eigenvalues mixture model white matter fibers

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Area of Science:

Neuroimaging
Biophysics
Medical Physics

Background:

Diffusion Tensor Imaging (DTI) maps white matter fibers (WMFs) by analyzing water diffusion.
Traditional DTI uses linear regression, limited by crossing fibers.
Existing multicompartment models assume fixed eigenvalues, causing inaccuracies in complex microstructures.

Purpose of the Study:

To develop a novel DTI approach for improved fiber orientation estimation.
To overcome limitations of fixed eigenvalue assumptions in diffusion MRI.
To enhance the accuracy of white matter reconstruction and neuroimaging analysis.

Main Methods:

Proposed a novel method for dynamic eigenvalue computation of the diffusion tensor matrix (DT-matrix) per voxel.
Implemented voxel-specific characterization of diffusion properties.
Validated the approach using simulations and experiments on human and rat brain datasets.

Main Results:

The novel method demonstrated improved white matter reconstruction compared to traditional DTI and fixed-eigenvalue models.
Achieved reduced angular error in fiber orientation detection.
Showcased enhanced accuracy in regions with complex white matter microstructures.

Conclusions:

Dynamically computing eigenvalues enhances DTI accuracy by accounting for voxel-specific diffusion properties.
The proposed method refines tissue microstructure characterization in diffusion MRI.
This approach advances the precision of neuroimaging analysis for WMF mapping.