ArticleMagnetic resonance in medicine2019
Microscopic anisotropy misestimation in spherical-mean single diffusion encoding MRI.
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Hierarchical Bayesian Modelling Improves Microstructural Parameter Mapping in Diffusion and Exchange MRI Data.NMR in biomedicine · 2026Article
- Geometry of the cumulant series in diffusion MRI.Nature communications · 2026Article
- Reduced NEXI protocol for the quantification of human gray matter microstructure on the Connectome 2.0 scanner.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Diffusion tensor imaging and diffusion kurtosis imaging of the pancreas - feasibility, robustness and protocol comparison in a healthy population.Abdominal radiology (New York) · 2025Article
- Diffusion MRI in the cortex of the brain: Reducing partial volume effects from CSF and white matter in the mean diffusivity using high b-values and spherical b-tensor encoding.Magnetic resonance in medicine · 2025Article
- Complementary MR measures of white matter and their relation to cardiovascular health and cognition.Scientific reports · 2025Article
- Assessment of a diffusion phantom for quality assurance in brain microstructure diffusion MRI studies.Scientific reports · 2025Article
- Linear rotationally invariant kurtosis measures from double diffusion encoding MRI.Magnetic resonance imaging · 2025Article
- Comparison of different neurite density metrics with brain asymmetry evaluation.Zeitschrift fur medizinische Physik · 2025Article
- Tutorial: a guide to diffusion MRI and structural connectomics.Nature protocols · 2025Review
- Article
- Human gray matter microstructure mapped using neurite exchange imaging (NEXI) on a clinical scanner.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Spherical convolutional neural networks can improve brain microstructure estimation from diffusion MRI data.Frontiers in neuroimaging · 2024Article
- Quantifying human gray matter microstructure using neurite exchange imaging (NEXI) and 300 mT/m gradients.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Mapping tissue microstructure across the human brain on a clinical scanner with soma and neurite density image metrics.Human brain mapping · 2023Article
- Extra-axonal contribution to double diffusion encoding-based pore size estimates in the corticospinal tract.Magma (New York, N.Y.) · 2023Article
- Cardiac q-space trajectory imaging by motion-compensated tensor-valued diffusion encoding in human heart in vivo.Magnetic resonance in medicine · 2023Article
- Multi-tensor diffusion abnormalities of gray matter in an animal model of cortical dysplasia.Frontiers in neurology · 2023Article
- Comparison of two types of microscopic diffusion anisotropy in mouse brain.NMR in biomedicine · 2023Article
- Unique information from common diffusion MRI models about white-matter differences across the human adult lifespan.Imaging neuroscience (Cambridge, Mass.) · 2023Article
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Abstract
purposeMicroscopic fractional anisotropy (µFA) can disentangle microstructural information from orientation dispersion. While double diffusion encoding (DDE) MRI methods are widely used to extract accurate µFA, it has only recently been proposed that powder-averaged single diffusion encoding (SDE) signals, when coupled with the diffusion standard model (SM) and a set of constraints, could be used for µFA estimation. This study aims to evaluate µFA as derived from the spherical mean technique (SMT) set of constraints, as well as more generally for powder-averaged SM signals.
methodsSDE experiments were performed at 16.4 T on an ex vivo mouse brain (Δ/δ = 12/1.5 ms). The µFA maps obtained from powder-averaged SDE signals were then compared to maps obtained from DDE-MRI experiments (Δ/τ/δ = 12/12/1.5 ms), which allow a model-free estimation of µFA. Theory and simulations that consider different types of heterogeneity are presented for corroborating the experimental findings.
resultsµFA, as well as other estimates derived from powder-averaged SDE signals produced large deviations from the ground truth in both gray and white matter. Simulations revealed that these misestimations are likely a consequence of factors not considered by the underlying microstructural models (such as intercomponent and intracompartmental kurtosis).
conclusionPowder-averaged SMT and (2-component) SM are unable to accurately report µFA and other microstructural parameters in ex vivo tissues. Improper model assumptions and constraints can significantly compromise parameter specificity. Further developments and validations are required prior to implementation of these models in clinical or preclinical research.
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