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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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Published on: July 28, 2013

Diffusion basis functions decomposition for estimating white matter intravoxel fiber geometry.

Alonso Ramirez-Manzanares¹, Mariano Rivera, Baba C Vemuri

¹Computer Science Department, Centro de Investigacion en Matematicas A.C., Guanajuato 36000, Mexico. alram@cimat.mx

IEEE Transactions on Medical Imaging

|August 19, 2007

Summary

This study introduces a novel method for reconstructing complex brain white matter tract geometry from diffusion MRI data. The approach efficiently captures fiber crossings and bifurcations, improving accuracy and reducing scan time.

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

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Published on: August 26, 2014

Area of Science:

Neuroimaging
Diffusion MRI
Computational Neuroscience

Background:

Diffusion Magnetic Resonance Imaging (dMRI) is crucial for mapping brain white matter architecture.
Accurately reconstructing complex fiber geometries, including crossings and bifurcations, remains a challenge in dMRI analysis.
Existing parametric models often require extensive data or struggle with multi-fiber voxels.

Purpose of the Study:

To develop a new formulation for recovering intravoxel fiber tract geometry from dMRI data.
To address the challenge of reconstructing complex fiber structures, including multiple neuronal fibers within a single voxel.
To improve the efficiency and robustness of parametric models for intravoxel fiber geometry estimation.

Main Methods:

Defined a discrete set of diffusion basis functions for representing intravoxel information.
Utilized a linear combination of basis functions to recover information in voxels with fiber crossings or bifurcations.
Employed a discrete mixture of Gaussians for the parametric representation of intravoxel fiber geometry.
Developed two algorithmic solutions: linear programming and quadratic cost function minimization with non-negativity constraints.

Main Results:

Demonstrated advantages in synthetic experiments, including reduced acquisition time using fewer diffusion-weighted images (23) and lower b-values (1250 s/mm2).
Showcased robustness in handling more than two fibers within a voxel, surpassing current state-of-the-art parametric models.
Successfully applied the algorithms to both synthetic and real dMRI datasets, validating the formulation.

Conclusions:

The proposed discrete formulation enables efficient and accurate recovery of intravoxel fiber geometry, even in complex scenarios.
The method significantly reduces dMRI acquisition time while maintaining or improving reconstruction quality.
This approach offers a robust and improved state-of-the-art solution for analyzing white matter tract architecture.