ArticleMagnetic resonance (Gottingen, Germany)2020
Transferring principles of solid-state and Laplace NMR to the field of in vivo brain MRI.
Article in Magnetic resonance (Gottingen, Germany), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- MultidimensionalbioRxiv : the preprint server for biology · 2026Article
- Informed Dictionary-Guided Monte Carlo Inversion for Robust and Reproducible Multidimensional MRI.Magnetic resonance in medicine · 2026Article
- Data-driven classification of tissue water populations by massively multidimensional diffusion-relaxation correlation MRI.Frontiers in neuroscience · 2026Article
- Multidimensional MRI reveals cortical astrogliosis linked to dementia in Alzheimer's disease.Brain communications · 2025Article
- In vivo rat brain mapping of multiple gray matter water populations using nonparametric D(ω)-RNMR in biomedicine · 2025Article
- Frequency-dependence in multidimensional diffusion-relaxation correlation MRI of the brain: Overfitting or meaningful parameter?Imaging neuroscience (Cambridge, Mass.) · 2025Article
- In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI.Human brain mapping · 2024Article
- Variability of multidimensional diffusion-relaxation MRI estimates in the human brain.Imaging neuroscience (Cambridge, Mass.) · 2024Article
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- Nonparametric 5D D-RJournal of magnetic resonance (San Diego, Calif. : 1997) · 2022Article
- Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact.NeuroImage · 2022Review
- Neural networks for parameter estimation in microstructural MRI: Application to a diffusion-relaxation model of white matter.NeuroImage · 2021Article
- Combined diffusion-relaxometry microstructure imaging: Current status and future prospects.Magnetic resonance in medicine · 2021Review
- Measuring compartmental TNeuroImage · 2021Article
- Toward nonparametric diffusion-Magnetic resonance in medicine · 2021Article
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- Resolving bundle-specific intra-axonal TNeuroImage · 2021Article
- Computing and visualising intra-voxel orientation-specific relaxation-diffusion features in the human brain.Human brain mapping · 2021Article
- The effect of gradient nonlinearities on fiber orientation estimates from spherical deconvolution of diffusion magnetic resonance imaging data.Human brain mapping · 2021Article
- Gradient waveform design for tensor-valued encoding in diffusion MRI.Journal of neuroscience methods · 2021Review
Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Magnetic resonance imaging (MRI) is the primary method for noninvasive investigations of the human brain in health, disease, and development but yields data that are difficult to interpret whenever the millimeter-scale voxels contain multiple microscopic tissue environments with different chemical and structural properties. We propose a novel MRI framework to quantify the microscopic heterogeneity of the living human brain as spatially resolved five-dimensional relaxation-diffusion distributions by augmenting a conventional diffusion-weighted imaging sequence with signal encoding principles from multidimensional solid-state nuclear magnetic resonance (NMR) spectroscopy, relaxation-diffusion correlation methods from Laplace NMR of porous media, and Monte Carlo data inversion. The high dimensionality of the distribution space allows resolution of multiple microscopic environments within each heterogeneous voxel as well as their individual characterization with novel statistical measures that combine the chemical sensitivity of the relaxation rates with the link between microstructure and the anisotropic diffusivity of tissue water. The proposed framework is demonstrated on a healthy volunteer using both exhaustive and clinically viable acquisition protocols.
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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.