Linking spherical mean diffusion weighted signal with intra-axonal volume fraction

Area of Science:

• Neuroimaging
• Biophysics
• Medical Physics

Background:

• Diffusion MRI assesses brain microstructure, but conventional DTI struggles with complex white matter like crossing fibers.
• Advanced techniques like CSD and SMT aim to quantify intra-axonal volume fraction (Vin) for better microstructural insights.

Purpose of the Study:

• To compare CSD and SMT techniques for measuring Vin.
• To investigate the relationship between the spherical mean diffusion weighted signal (S¯) and Vin across different b-values.

Main Methods:

• Utilized high-quality Human Connectome Project diffusion MRI data.
• Compared CSD and SMT methods for Vin estimation.
• Analyzed the correlation between S¯ and Vin at high (≥3000 s/mm²) and low (≈1000 s/mm²) b-values.

Main Results:

• At high b-values (≥3000 s/mm²), S¯ showed a linear relationship with Vin, providing similar white matter contrast.
• At low b-values (≈1000 s/mm²), the S¯-Vin relationship was sensitive to intrinsic diffusivity variations.
• S¯ at 1000 s/mm² is not a reliable indicator of Vin.

Conclusions:

• High b-value diffusion MRI (≥3000 s/mm²) enables accurate Vin estimation using S¯.
• Low b-value data (≈1000 s/mm²) from conventional DTI cannot be re-analyzed to yield Vin-weighted contrast.
• CSD and SMT show promise for detailed brain microstructure analysis, particularly at higher b-values.