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Observational analysis calculates axonal signal fraction in clinical data, suggesting potential for pathology characterization.
Motivation: To show feasibility of estimating the signal fraction due to axons with clinical in-vivo human diffusion MRI. Goal(s): To optimize the calculations of the axonal signal fraction (ASF) while simultaneously minimizing model assumptions and model degeneracy. Approach: We expand two high b-value PGSE shells in spherical harmonics and calculate the zonal ratios, spectra, spherical means and variances. Then using an elaborate machine learning setup we relate these to the axonal diffusivities. With these we calculate the axonal signal fraction (ASF) using the powder average equation. Results: The ASF calculated on Clinical data has comparable performance to that calculated on Connectome data. Impact: The ASF is related to the volume occupancy of axons and could be used to characterize pathology. We enable its estimation using conventional diffusion MRI data from a clinical scanner, while at the same time minimizing model assumptions and degeneracy.
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Thøgersen et al. (2025) studied this question.
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