ArticleNeuroImage2019
Towards microstructure fingerprinting: Estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations.
Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Placental Blood-Flow Velocity Quantification From Diffusion MRI.Magnetic resonance in medicine · 2026Article
- An Interpretable Deep-Learning Approach for Efficient CEST Parameter Quantification: Importance-Ranked Saturation Transfer MRI Protocol.Magnetic resonance in medicine · 2026Article
- MATI: A GPU-accelerated toolbox for microstructural diffusion MRI simulation and data fitting with a graphical user interface.Magnetic resonance imaging · 2025Article
- Local response function estimation in spherical deconvolution for comprehensive tissue representation using diffusion MRI.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Identifying responders to vagus nerve stimulation based on microstructural features of thalamocortical tracts in drug-resistant epilepsy.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2024Article
- Article
- Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Locus coeruleus features are linked to vagus nerve stimulation response in drug-resistant epilepsy.Frontiers in neuroscience · 2024Article
- Sparse Blind Spherical Deconvolution of diffusion weighted MRI.Frontiers in neuroscience · 2024Article
- Fast multi-compartment Microstructure Fingerprinting in brain white matter.Frontiers in neuroscience · 2024Article
- Molecular Imaging with Aquaporin-Based Reporter Genes: Quantitative Considerations from Monte Carlo Diffusion Simulations.ACS synthetic biology · 2023Article
- Microstructure Fingerprinting for Heterogeneously Oriented Tissue Microenvironments.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Molecular imaging with aquaporin-based reporter genes: quantitative considerations from Monte Carlo diffusion simulations.bioRxiv : the preprint server for biology · 2023Article
- Article
- Unraveling multi-fixel microstructure with tractography and angular weighting.Frontiers in neuroscience · 2023Article
- Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology.Magnetic resonance in medicine · 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
- Combined diffusion-relaxometry microstructure imaging: Current status and future prospects.Magnetic resonance in medicine · 2021Review
- GAMER-MRI in Multiple Sclerosis Identifies the Diffusion-Based Microstructural Measures That Are Most Sensitive to Focal Damage: A Deep-Learning-Based Analysis and Clinico-Biological Validation.Frontiers in neuroscience · 2021Article
- ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation.NeuroImage · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Many closed-form analytical models have been proposed to relate the diffusion-weighted magnetic resonance imaging (DW-MRI) signal to microstructural features of white matter tissues. These models generally make assumptions about the tissue and the diffusion processes which often depart from the biophysical reality, limiting their reliability and interpretability in practice. Monte Carlo simulations of the random walk of water molecules are widely recognized to provide near groundtruth for DW-MRI signals. However, they have mostly been limited to the validation of simpler models rather than used for the estimation of microstructural properties. This work proposes a general framework which leverages Monte Carlo simulations for the estimation of physically interpretable microstructural parameters, both in single and in crossing fascicles of axons. Monte Carlo simulations of DW-MRI signals, or fingerprints, are pre-computed for a large collection of microstructural configurations. At every voxel, the microstructural parameters are estimated by optimizing a sparse combination of these fingerprints. Extensive synthetic experiments showed that our approach achieves accurate and robust estimates in the presence of noise and uncertainties over fixed or input parameters. In an in vivo rat model of spinal cord injury, our approach provided microstructural parameters that showed better correspondence with histology than five closed-form models of the diffusion signal: MMWMD, NODDI, DIAMOND, WMTI and MAPL. On whole-brain in vivo data from the human connectome project (HCP), our method exhibited spatial distributions of apparent axonal radius and axonal density indices in keeping with ex vivo studies. This work paves the way for microstructure fingerprinting with Monte Carlo simulations used directly at the modeling stage and not only as a validation tool.
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