ArticleFrontiers in neuroinformatics2023
CACTUS: a computational framework for generating realistic white matter microstructure substrates.
Article in Frontiers in neuroinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Field Strength-Dependent White Matter RMagnetic resonance in medicine · 2026Article
- MDCrow: automating molecular dynamics workflows with large language models.Machine learning: science and technology · 2026Article
- Multi-modal Monte Carlo MRI simulator of tissue microstructure.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Validating the neurite exchange imaging (NEXI) model of gray matter using Monte Carlo simulations in numerical substrates.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Validating the standard model of diffusion MRI in white matter with numerical substrates.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Alpha_Mesh_Swc: automatic and robust surface mesh generation from the skeleton description of brain cells.Briefings in bioinformatics · 2025Article
- Predicting Mesoscopic Larmor Frequency Shifts in White Matter With Diffusion MRI-A Monte Carlo Study in Axonal Phantoms.NMR in biomedicine · 2025Article
- SpinWalk: A Monte Carlo simulator for MR-signal formation in inhomogeneous tissue.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Linking microscopy to diffusion MRI with degenerate biophysical models: An application of the Bayesian EstimatioN of CHange (BENCH) framework.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Non-parametric prediction of brain MRI microstructure using transfer learning.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Multi-tensor fixel-based metrics in tractometry: application to multiple sclerosis.Frontiers in neuroscience · 2024Article
- Exploring white matter dynamics and morphology through interactive numerical phantoms: the White Matter Generator.Frontiers in neuroinformatics · 2024Article
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7 authors.
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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, 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 500μm
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