Purpose To implement diffusion tensor imaging (DTI) protocol for visualization of peripheral nerves in human forearm. Materials and Methods This HIPAA-compliant study was approved by our institutional review board and written informed consent was obtained from 10 healthy participants. T1-and T2-weighted turbo spin echo with fat saturation, short tau inversion recovery (STIR), and DTI sequences with 21 diffusion encoding directions were implemented to acquire images of the forearm nerves with an 8 channel knee coil on a 3T MRI scanner. Identification of the nerves was based on T1-weighted, T2-weighted, STIR and DTI-derived fractional anisotropy (FA) images. Maps of the DTI derived indices, FA, mean diffusivity (MD), longitudinal diffusivity (λ//) and radial diffusivity (λ⊥) along the length of the nerves were generated. Results DTI-derived maps delineated the forearm nerves more clearly than images acquired with other sequences. Only ulnar and median nerves were clearly visualized on the DTI-derived FA maps. No significant differences were observed between the left and right forearms in any of the DTI-derived measures. Significant variation in the DTI measures was observed along the length of the nerve. Significant differences in the DTI measures were also observed between the median and ulnar nerves. Conclusion DTI is superior in visualizing the median and ulnar nerves in the human forearm. The normative data could potentially help distinguish normal from diseased nerves.
Purpose To implement high resolution diffusion tensor imaging (DTI) for visualization and quantification of peripheral nerves in human forearm. Materials and Methods This 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 mm3) DTI sequence was optimized for clearly visualizing ulnar, superficial radial and median nerves in the forearm. Maps of the DTI derived indices, FA, mean diffusivity (MD), longitudinal diffusivity (λ//) and radial diffusivity (λ⊥) were generated. Results For 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. Conclusions High 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.
The increased postflight CSF production rate in astronauts with positive flattening is compatible with the hypothesis of microgravity-induced intracranial hypertension inferring downregulation in CSF production in microgravity that is upregulated upon return to normal gravity. Increased postflight CSF maximum systolic velocity in astronauts with negative flattening suggests increased craniospinal compliance and a potential negative risk factor to microgravity-induced intracranial hypertension.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.