ArticleMagnetic resonance in medicine2019
Relevance of time-dependence for clinically viable diffusion imaging of the spinal cord.
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 15 papers.
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Who cites it
15 citing papers in PubMed, 30 citations in OpenAlex.
- Evaluation of magnetic resonance spectroscopy total sodium concentration measures, and associations with microstructure and physical impairment in cervical myelopathy.Scientific reports · 2025Article
- Influence of preprocessing, distortion correction and cardiac triggering on the quality of diffusion MR images of spinal cord.Magnetic resonance imaging · 2024Article
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- Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: Optimisation and pre-clinical demonstration.NeuroImage · 2023Article
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- CACTUS: a computational framework for generating realistic white matter microstructure substrates.Frontiers in neuroinformatics · 2023Article
- Advanced spinal cord MRI in multiple sclerosis: Current techniques and future directions.NeuroImage. Clinical · 2022Review
- Generic acquisition protocol for quantitative MRI of the spinal cord.Nature protocols · 2021Article
- A simple estimate of axon size with diffusion MRI.NeuroImage · 2021Article
- Cerebellar White Matter Structural Correlates of Locomotor Adaptation. Do They Reflect Neural Adaptation?Cerebellum (London, England) · 2020Article
- Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising.NeuroImage · 2020Article
- Advanced Multicompartment Diffusion MRI Models and Their Application in Multiple Sclerosis.AJNR. American journal of neuroradiology · 2020Review
- Diffusion MRI microstructural models in the cervical spinal cord - Application, normative values, and correlations with histological analysis.NeuroImage · 2019Article
- Relevance of time-dependence for clinically viable diffusion imaging of the spinal cord.Magnetic resonance in medicine · 2019Article
- Tensor-valued diffusion encoding for diffusional variance decomposition (DIVIDE): Technical feasibility in clinical MRI systems.PloS one · 2019Article
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Authors and funding
11 authors at 5 institutions in 4 countries.
Funding
Abstract
purposeTime-dependence is a key feature of the diffusion-weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study time-dependence in the human spinal cord, as its axonal structure is specific and different from the brain.
methodsWe run Monte Carlo simulations using a synthetic model of spinal cord white matter (WM) (large axons), and of brain WM (smaller axons). Furthermore, we study clinically feasible multi-shell DW scans of the cervical spinal cord (b = 0; b = 711 s mm
resultsBoth intra-/extra-axonal perpendicular diffusivities and kurtosis excess show time-dependence in our synthetic spinal cord model. This time-dependence is reflected mostly in the intra-axonal perpendicular DW signal, which also exhibits strong decay, unlike our brain model. Time-dependence of the total DW signal appears detectable in the presence of noise in our synthetic spinal cord model, but not in the brain. In WM in vivo, we observe time-dependent macroscopic and microscopic diffusivities and diffusion kurtosis, NODDI and two-compartment SMT metrics. Accounting for large axon calibers improves fitting of multi-compartment models to a minor extent.
conclusionsTime-dependence of clinically viable DW MRI metrics can be detected in vivo in spinal cord WM, thus providing new opportunities for the non-invasive estimation of microstructural properties. The time-dependence of the perpendicular DW signal may feature strong intra-axonal contributions due to large spinal axon caliber. Hence, a popular model known as "stick" (zero-radius cylinder) may be sub-optimal to describe signals from the largest spinal axons.
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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.