Human Axon Radii Estimation at MRI Scale

Abstract

Non-invasive assessment of axon radii via MRI is of increasing interest in human brain research. Its validation requires representative reference data that covers the spatial extent of an MRI voxel (e.g., 1mm²). Due to its small field of view, the commonly used manually labeled electron microscopy (mlEM) can not representatively capture sparsely occurring, large axons, which are the main contributors to the effective mean axon radius (r_eff) measured with MRI. To overcome this limitation, we investigated the feasibility of generating representative reference data from large-scale light microscopy (lsLM) using automated segmentation methods including a convolutional neural network (CNN). We determined large, mis-/undetected axons as the main error source for the estimation of r_eff ($\approx$ 10 %). Our results suggest that the proposed pipeline can be used to generate reference data for the MRI-visible r_eff and even bears the potential to map spatial, anatomical variation of r_eff.