ArticleNeuroImage2020
ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 43 citations in OpenAlex.
- Multi-modal Monte Carlo MRI simulator of tissue microstructure.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Generating brain-wide connectome using synthetic axonal morphologies.Nature communications · 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
- Article
- Exploring white matter dynamics and morphology through interactive numerical phantoms: the White Matter Generator.Frontiers in neuroinformatics · 2024Article
- Investigating exchange, structural disorder, and restriction in gray matter via water and metabolites diffusivity and kurtosis time-dependence.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Multi-tensor fixel-based metrics in tractometry: application to multiple sclerosis.Frontiers in neuroscience · 2024Article
- Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: Optimisation and pre-clinical demonstration.NeuroImage · 2023Article
- Diffusion phase-imaging in anisotropic media using non-linear gradients for diffusion encoding.PloS one · 2023Article
- CACTUS: a computational framework for generating realistic white matter microstructure substrates.Frontiers in neuroinformatics · 2023Article
- Reconstruction of ovine axonal cytoarchitecture enables more accurate models of brain biomechanics.Communications biology · 2022Article
- Neurite Exchange Imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange.NeuroImage · 2022Article
- Recent Advances in Parameter Inference for Diffusion MRI Signal Models.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Review
- Evidence for microscopic kurtosis in neural tissue revealed by correlation tensor MRI.Magnetic resonance in medicine · 2021Article
- Design and characterization of a 3D-printed axon-mimetic phantom for diffusion MRI.Magnetic resonance in medicine · 2021Article
- Mapping complex cell morphology in the grey matter with double diffusion encoding MR: A simulation study.NeuroImage · 2021Article
- Realistic Microstructure Simulator (RMS): Monte Carlo simulations of diffusion in three-dimensional cell segmentations of microscopy images.Journal of neuroscience methods · 2021Article
- Axon morphology is modulated by the local environment and impacts the noninvasive investigation of its structure-function relationship.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
This paper presents Contextual Fibre Growth (ConFiG), an approach to generate white matter numerical phantoms by mimicking natural fibre genesis. ConFiG grows fibres one-by-one, following simple rules motivated by real axonal guidance mechanisms. These simple rules enable ConFiG to generate phantoms with tuneable microstructural features by growing fibres while attempting to meet morphological targets such as user-specified density and orientation distribution. We compare ConFiG to the state-of-the-art approach based on packing fibres together by generating phantoms in a range of fibre configurations including crossing fibre bundles and orientation dispersion. Results demonstrate that ConFiG produces phantoms with up to 20% higher densities than the state-of-the-art, particularly in complex configurations with crossing fibres. We additionally show that the microstructural morphology of ConFiG phantoms is comparable to real tissue, producing diameter and orientation distributions close to electron microscopy estimates from real tissue as well as capturing complex fibre cross sections. Signals simulated from ConFiG phantoms match real diffusion MRI data well, showing that ConFiG phantoms can be used to generate realistic diffusion MRI data. This demonstrates the feasibility of ConFiG to generate realistic synthetic diffusion MRI data for developing and validating microstructure modelling approaches.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.