Visualization of the coherence of the principal diffusion orientation: an eigenvector-based approach

Area of Science:

• Neuroimaging
• Diffusion MRI

Background:

• Diffusion tensor imaging (DTI) is crucial for mapping white matter architecture.
• Existing metrics like fractional anisotropy (FA) have limitations in precisely characterizing complex fiber orientations.

Purpose of the Study:

• To introduce and validate a novel intervoxel diffusion coherence (IVDC) index for enhanced spatial mapping of diffusion anisotropy and orientation coherence.
• To assess the sensitivity and diagnostic potential of IVDC compared to FA.

Main Methods:

• Developed an eigenvector-based approach using a scatter matrix method to calculate the IVDC index voxel-wise.
• Performed simulations with crossing fiber models at varying angles.
• Acquired diffusion tensor images from six healthy volunteers at 3.0T MR.

Main Results:

• The IVDC index demonstrated higher sensitivity than FA to fiber separation changes in noise-free simulations.
• IVDC improved image contrast in key brain regions, including the thalamus, middle cerebral peduncle, and pons.
• Calculated IVDC alongside FA and coherence index in human brain data.

Conclusions:

• The IVDC index provides reliable and complementary information to FA for analyzing water diffusion in the brain.
• IVDC shows promise for improving white matter tractography, particularly in identifying trajectory termination points.
• This novel method offers potential for advanced diagnostic applications in neuroimaging.