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The Standard Model (SM) of diffusion in white matter (WM) provides a unifying framework for various modelling approaches, representing axons as impermeable sticks embedded within a locally anisotropic extra-axonal space. However, the SM relies on simplifying assumptions that may not hold in realistic WM tissue, as they do not take into account axonal undulations, beading, the presence of glial cells, or membrane permeability. In this work, we investigate how SM parameter estimates, derived using the Standard Model Imaging (SMI) framework, behave when the model is applied to realistic numerical WM substrates generated by the CATERPillar tool. Specifically, we vary (i) axonal morphological features such as beading and undulations, (ii) axonal packing density, (iii) orientation dispersion, (iv) membrane permeability of axons and astrocytes separately, (v) myelin volume fraction, and (vi) diffusion time. In each part of the analysis, different noise levels are introduced. Our findings enable users to identify potential biases introduced by varying conditions and to adjust their interpretations accordingly.<\/jats:p>","DOI":"10.1162\/imag.a.1385","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T20:01:41Z","timestamp":1789156901000},"update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":1,"title":["Validating the standard model of diffusion MRI in white matter with numerical substrates"],"prefix":"10.1162","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2535-3319","authenticated-orcid":false,"given":"Jasmine","family":"Nguyen-Duc","sequence":"first","affiliation":[{"name":"Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland"},{"name":"Faculty of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9160-4605","authenticated-orcid":false,"given":"Quentin","family":"Uhl","sequence":"additional","affiliation":[{"name":"Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland"},{"name":"Faculty of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7597-6919","authenticated-orcid":false,"given":"Rita","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland"},{"name":"Faculty of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8960-5465","authenticated-orcid":false,"given":"Jonathan","family":"Rafael-Pati\u00f1o","sequence":"additional","affiliation":[{"name":"Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3664-0195","authenticated-orcid":false,"given":"Ileana O.","family":"Jelescu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland"},{"name":"Faculty of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","published-online":{"date-parts":[[2026,10,15]]},"reference":[{"key":"2026101515560493800_IMAG.a.1385-b1","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1038\/s42003-021-01699-w","article-title":"Deepacson automated segmentation of white matter in 3D electron microscopy","volume":"4","author":"Abdollahzadeh","year":"2021","journal-title":"Communications Biology"},{"key":"2026101515560493800_IMAG.a.1385-b2","doi-asserted-by":"crossref","first-page":"9808","DOI":"10.1038\/s41467-025-64793-1","article-title":"Scattering approach to diffusion quantifies axonal damage in brain injury","volume":"16","author":"Abdollahzadeh","year":"2025","journal-title":"Nature Communications"},{"key":"2026101515560493800_IMAG.a.1385-b3","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1016\/j.neuroimage.2018.07.066","article-title":"Evaluation of the accuracy and precision of the diffusion parameter estimation with Gibbs and noise removal pipeline","volume":"183","author":"Ades-Aron","year":"2018","journal-title":"NeuroImage"},{"key":"2026101515560493800_IMAG.a.1385-b4","doi-asserted-by":"crossref","first-page":"108951","DOI":"10.1016\/j.jneumeth.2020.108951","article-title":"The sensitivity of diffusion MRI to microstructural properties and experimental factors","volume":"347","author":"Afzali","year":"2021","journal-title":"Journal of Neuroscience Methods"},{"key":"2026101515560493800_IMAG.a.1385-b5","doi-asserted-by":"crossref","unstructured":"Aird-Rossiter, C., Zhang, H., Alexander, D. C., Jones, D. K., & Palombo, M. (2025). Decoding gray matter: Large-scale analysis of brain cell morphometry to inform microstructural modeling of diffusion MR signals. 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