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This is fundamental for understanding the link between micrometre-scale tissue properties and DW-MRI signals measured at the millimetre-scale, optimizing acquisition protocols to target microstructure properties of interest, and exploring the robustness and accuracy of estimation methods. However, accurate simulations require substrates that reflect the main microstructural features of the studied tissue. To address this challenge, we introduce a novel computational workflow, CACTUS (Computational Axonal Configurator for Tailored and Ultradense Substrates), for generating synthetic white matter substrates. Our approach allows constructing substrates with higher packing density than existing methods, up to 95% intra-axonal volume fraction, and larger voxel sizes of up to 500\u03bcm<jats:sup>3<\/jats:sup> with rich fibre complexity. CACTUS generates bundles with angular dispersion, bundle crossings, and variations along the fibres of their inner and outer radii and g-ratio. We achieve this by introducing a novel global cost function and a fibre radial growth approach that allows substrates to match predefined targeted characteristics and mirror those reported in histological studies. CACTUS improves the development of complex synthetic substrates, paving the way for future applications in microstructure imaging.<\/jats:p>","DOI":"10.3389\/fninf.2023.1208073","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T08:32:10Z","timestamp":1690878730000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["CACTUS: a computational framework for generating realistic white matter microstructure substrates"],"prefix":"10.3389","volume":"17","author":[{"given":"Juan Luis","family":"Villarreal-Haro","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Remy","family":"Gardier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erick J.","family":"Canales-Rodr\u00edguez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elda","family":"Fischi-Gomez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabriel","family":"Girard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Philippe","family":"Thiran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Rafael-Pati\u00f1o","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,8,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e4072","DOI":"10.1002\/nbm.4072","article-title":"Diffusion-weighted MRI with intravoxel incoherent motion modeling for assessment of muscle perfusion in the thigh during post-exercise hyperemia in younger and older adults","volume":"32","author":"Adelnia","year":"2019","journal-title":"NMR Biomed."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.1016\/j.neuroimage.2010.05.043","article-title":"Orientationally invariant indices of axon diameter and density from diffusion MRI","volume":"52","author":"Alexander","year":"2010","journal-title":"NeuroImage"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e041804","DOI":"10.1103\/PhysRevE.83.041804","article-title":"Random-walk-based stochastic modeling of three-dimensional fiber systems","volume":"83","author":"Altendorf","year":"2011","journal-title":"Phys. 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