ArticleJournal of magnetic resonance imaging : JMRI2014
High resolution diffusion tensor imaging of human nerves in forearm.
Article in Journal of magnetic resonance imaging : JMRI, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Meta-analysis of the normal diffusion tensor imaging values of the peripheral nerves in the upper limb.Scientific reports · 2023Pooled it
- Article
- Assessment of peripheral neuropathy in type 2 diabetes by diffusion tensor imaging.Quantitative imaging in medicine and surgery · 2022Article
- Diffusion tensor imaging in cubital tunnel syndrome.Scientific reports · 2021Article
- MRI of the ulnar nerve pre- and post-transposition: imaging features and rater agreement.Skeletal radiology · 2021Article
- Initial findings in traumatic peripheral nerve injury and repair with diffusion tensor imaging.Annals of clinical and translational neurology · 2021Article
- Diffusion Tensor Imaging of the Brachial Plexus: A Comparison between Readout-segmented and Conventional Single-shot Echo-planar Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2019Article
- Peripheral nerve magnetic resonance imaging.F1000Research · 2019Review
- Evaluation of Brachial Plexus Using Combined Stereological Techniques of Diffusion Tensor Imaging and Fiber Tracking.Journal of brachial plexus and peripheral nerve injury · 2019Article
- Segmentation of Peripheral Nerves From Magnetic Resonance Neurography: A Fully-Automatic, Deep Learning-Based Approach.Frontiers in neurology · 2018Article
- MRI biomarkers of proximal nerve injury in CIDP.Annals of clinical and translational neurology · 2018Article
- Diffusion-weighted MR neurography of median and ulnar nerves in the wrist and palm.European radiology · 2017Article
- MRI shows thickening and altered diffusion in the median and ulnar nerves in multifocal motor neuropathy.European radiology · 2017Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo implement high resolution diffusion tensor imaging (DTI) for visualization and quantification of peripheral nerves in human forearm. MATERIALS AND
methodsThis HIPAA-compliant study was approved by our Institutional Review Board and written informed consent was obtained from all the study participants. Images were acquired with T1 -and T2 -weighted turbo spin echo with/without fat saturation, short tau inversion recovery (STIR). In addition, high spatial resolution (1.0 × 1.0 × 3.0 mm(3) ) DTI sequence was optimized for clearly visualizing ulnar, superficial radial and median nerves in the forearm. Maps of the DTI derived indices, fractional anisotropy (FA), mean diffusivity (MD), longitudinal diffusivity (λ// ) and radial diffusivity (λ⊥ ) were generated.
resultsFor the first time, the three peripheral nerves, ulnar, superficial radial, and median, were visualized unequivocally on high resolution DTI-derived maps. DTI delineated the forearm nerves more clearly than other sequences. Significant differences in the DTI-derived measures, FA, MD, λ// and λ⊥ , were observed among the three nerves. A strong correlation between the nerve size derived from FA map and T2 -weighted images was observed.
conclusionHigh spatial resolution DTI is superior in identifying and quantifying the median, ulnar, and superficial radial nerves in human forearm. Consistent visualization of small nerves and nerve branches is possible with high spatial resolution DTI. These normative data could potentially help in identifying pathology in diseased nerves. J. Magn. Reson. Imaging 2014;39:1374-1383. © 2013 Wiley Periodicals, Inc.
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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.