English

CACTUS: A Computational Framework for Generating Realistic White Matter Microstructure Substrates

Computational Engineering, Finance, and Science 2023-05-26 v1

Abstract

Monte-Carlo diffusion simulations are a powerful tool for validating tissue microstructure models by generating synthetic diffusion-weighted magnetic resonance images (DW-MRI) in controlled environments. This is fundamental for understanding the link between micrometre-scale tissue properties and DW-MRI signals measured at the millimetre-scale, optimising 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 (500um) 3 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.

Keywords

Cite

@article{arxiv.2305.16109,
  title  = {CACTUS: A Computational Framework for Generating Realistic White Matter Microstructure Substrates},
  author = {Juan Luis Villarreal-Haro and Remy Gardier and Erick J Canales-Rodriguez and Elda Fischi Gomez and Gabriel Girard and Jean-Philippe Thiran and Jonathan Rafael-Patino},
  journal= {arXiv preprint arXiv:2305.16109},
  year   = {2023}
}

Comments

21 pages, 7 figures

R2 v1 2026-06-28T10:46:06.063Z