dicompare schema: Axon diameter mapping (v1.2)
Abstract
**Axon Diameter Mapping** **Overview:** Multi-shell diffusion-weighted MRI of the human brain that was optimized for axon diameter mapping using the power-law approach of Veraart et al. (2020) **Hardware requirements:** The modeling approach leverages (a) high *b*-values to suppress extra-axonal signal, and (b) strong diffusion-weighted strengths to maximize the sensitivity of diffusion-weighted MRI signal to restricted diffusion within micrometer-thin axons. Therefore, axon diameter mapping is currently limited to MRI scanners that are equipped with ultra-strong diffusion-weighting gradients, i.e. 300mT/m. Examples include Siemens 3T Connectom, Siemens 3T Connectom.X, and GE 3T Magnus. The protocol was optimized and tested on Siemens 3T Connectom. **Code:** Code to analyze the data is provided in https://github.com/NYU-DiffusionMRI/AxonRadiusMapping. **Supporting data:** Rician signal biases impact the accuracy of Axon Diameter Mapping. Therefore it is important to collect supporting data from which a noise map can be derived. **References:** *Model:* Veraart J, Nunes D, Rudrapatna U, Fieremans E, Jones DK, Novikov DS, Shemesh N. Noninvasive quantification of axon radii using diffusion MRI. Elife. 2020 Feb 12;9:e49855. doi: 10.7554/eLife.49855. *Reproducibility and protocol:* Veraart J, Raven EP, Edwards LJ, Weiskopf N, Jones DK. The variability of MR axon radii estimates in the human white matter. Hum Brain Mapp. 2021 May;42(7):2201-2213. doi: 10.1002/hbm.25359. *Interpretation:* Karat BG, Wren-Jarvis J, Raven EP, Khan AR, Jones DK, Palombo M, Veraart J. Revisiting the interpretation of axon diameter mapping using higher-order signal representations. Imaging Neurosci (Camb). 2026 Jan 9;4:IMAG.a.1080. doi: 10.1162/IMAG.a.1080. This is a dicompare validation schema. View, browse, and use it at https://dicompare.neurodesk.org/schema/Axon_diameter_mapping_v1.2.
