Automatic Verification of the Gradient Table in Diffusion-Weighted MRI Based on Fiber Continuity.

Area of Science:

• Neuroimaging
• Medical Physics
• Computational Neuroscience

Background:

• Inconsistent coordinate systems in diffusion-weighted MRI (dMRI) can compromise fiber tracking and connectivity analysis.
• Manual verification and correction of diffusion gradient tables are time-consuming and labor-intensive.
• Accurate gradient table orientation is crucial for reliable dMRI data interpretation.

Purpose of the Study:

• To develop an automated method for verifying and correcting diffusion gradient tables in dMRI.
• To reduce manual labor and improve efficiency in dMRI processing pipelines.
• To enhance the accuracy of fiber orientation reconstruction and subsequent analyses.

Main Methods:

• Exploited the principle of fiber continuity, assuming smooth variations in fibrous tissues like white matter.
• Developed a tractography-free algorithm that tests all permutation and flip configurations of the gradient table.
• Assessed the consistency of reconstructed fiber orientations with fiber continuity for each configuration.

Main Results:

• The proposed algorithm successfully identified the correct permutation and flip configuration for the gradient table.
• Validation across 185 experiments using human brain dMRI data from three public sources confirmed the method's efficacy.
• The algorithm consistently selected the gradient table configuration exhibiting the highest fiber orientation consistency.

Conclusions:

• The automated gradient table verification method significantly improves dMRI processing efficiency and accuracy.
• This tractography-free approach offers a reliable solution for ensuring correct diffusion gradient orientations.
• The proposed method is a valuable addition to dMRI processing pipelines, enhancing downstream analyses like connectivity studies.