Magnetic resonance neurography of the brachial plexus using 3D SHINKEI: Comparative evaluation with conventional magnetic resonance sequences for the visualization of anatomy and detection of nerve injury at 1.5t
2021
Background and Purpose: This work aims at optimizing and studying the feasibility of imaging the brachial plexus at 1.5T using 3D nerve-SHeath signal increased with INKed rest-tissue RARE imaging (3D SHINKEI) neurography sequence by comparing with routine sequences. Materials and Methods: The study was performed on a 1.5T Achieva scanner. It was designed in two parts: (a) Optimization of SHINKEI sequence at 1.5T; and (b) Feasibility study of the optimized SHINKEI sequence for generating clinical quality magnetic resonance neurography images at 1.5T. Simulations and volunteer experiments were conducted to optimize the T2 preparation duration for optimum nerve-muscle contrast at 1.5T. Images from the sequence under study and other routine sequences from 24 patients clinically referred for brachial plexus imaging were scored by a panel of radiologists for diagnostic quality. Injury detection efficacy of these sequences were evaluated against the surgical information available from seven patients. Results: T2 preparation duration of 50 ms gives the best contrast to noise between nerve and muscle. The images of 3D SHINKEI and short-term inversion recovery turbo spin-echo sequences are of similar diagnostic quality but significantly better than diffusion weighted imaging with background signal suppression. In comparison with the surgical findings, 3D SHINKEI has the lowest specificity; however, it had the highest sensitivity and predictive efficacy compared to other routine sequences. Conclusion: 3D SHINKEI sequence provides a good nerve–muscle contrast and has high predictive efficacy of nerve injury, indicating that it is a potential screening sequence candidate for brachial plexus scans at 1.5T also.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
0
References
0
Citations
NaN
KQI