**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.
Jelle Veraart, Erika Raven· Zenodo (CERN European Organi...· 0 citations
**Overview:** Diffusion-weighted MRI enable the quantification of brain microstructure and structural connectivity in the living human brain using various modeling and analysis approaches. Each of such modeling and analysis approaches have specific requirements in terms of b-values, diffusion-weighting gradients, and others. Here we provide an overview of minimum and/or recommended protocol settings. **Imaging hardware:** The definition of number of b-values, gradient directions, and others generalize across scanners, but settings such as echo time or repetition time might require customization depending on the available hardware. Therefore the ***intended use*** is primarily the evaluation of compatibility of users' data with modeling and analysis approaches. **Modeling approaches:** - Diffusion Tensor Imaging (DTI) - Diffusion Kurtosis Imaging (DKI) - Standard Model Imaging (SMI) - Neurite Orientation Dispersion and Density Imaging (NODDI) - Axon diameter mapping **Supplementary data:** Diffusion-weighted MRI is impacted by imaging artifacts and thermal noise. Subject motion, noise, and numerous imaging artifacts reduce image quality, degrade anatomical reliability, and lower the accuracy, precision, and robustness of modeling. These artifacts can be mitigated through preprocessing if supplementary data is available using [widely adopted pipelines](https://neurodesk.org/edu/examples/diffusion_imaging/qsiprep.html). Supplementary data might include reverse-phase encoded data or structural MRI data. **References:** 1. Basser PJ. Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed. 1995;8:333–44. 2. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: The quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med. 2005;53:1432–40. 3. Novikov DS, Veraart J, Jelescu IO, Fieremans E. Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI. Neuroimage. 2018;174:518–38. 4. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. 2012;61:1000–16. 5. Palombo M, Ianus A, Guerreri M, Nunes D, Alexander DC, Shemesh N, et al. SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI. Neuroimage. 2020;215:116835. 6. 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;42:2201–13. 7. Coelho S, Liao Y, Szczepankiewicz F, Veraart J, Chung S, Lui YW, Novikov DS, Fieremans E. Assessment of precision and accuracy of brain white matter microstructure using combined diffusion MRI and relaxometry. Hum Brain Mapp. 2024 Jun 15;45(9):e26725. **Disclaimer:** This list is not exhaustive. Please contact authors to suggest additional models or modeling approaches and we can update accordingly. This is a dicompare validation schema. View, browse, and use it at https://dicompare.neurodesk.org/schema/Protocols_for_DWI_analysis_v1.1.
Jelle Veraart, Santiago Coelho· Zenodo (CERN European Organi...· 0 citations