Clinical Value and Diagnostic Accuracy of 3.0T Multi-Parameter Magnetic Resonance Imaging in Traumatic Brachial Plexus Injury
Lihong Zhang, Taixing Xiao, Qiufeng Yu, Yong Li, Feng Shen, Wenming Li
(The Medical Imaging Center, The Third People’s Hospital of Liaocheng, Liaocheng, Shangdong, China (mainland))
Med Sci Monit 2018; 24:7199-7205
The aim of this study was to evaluate the clinical value and diagnostic accuracy of 3.0T multi-parameter magnetic resonance imaging (MRI) in traumatic brachial plexus injury.
MATERIAL AND METHODS: Twenty-five healthy volunteers and 28 patients with clinically confirmed traumatic brachial plexus injury were enrolled in this study. Bilateral brachial plexus imaging was performed using conventional sequences (T1WI, T2WI), short time inversion recovery (STIR), balanced fast field echo (balance-FFE), and diffusion weighted imaging with background suppression (DWIBS). The MRI diagnosis was compared with intraoperative electromyography and surgery.
RESULTS: Brachial plexus injuries were classified based on the anatomic locations. There were 16 patients with pre-ganglionic injury and 12 patients with post-ganglionic injury. The pre-ganglionic injury included ruptured nerve roots, stiff nerve roots, traumatic meningeal cysts, black line sign, spinal cord edema, and thickened nerve root sleeve. The post-ganglionic injury included thickened nerve roots, disappearance of normal nerve root structure or disrupted continuity of the nerve, stiff nerve roots, pseudo-neuroma, and abnormalities in the adjacent soft tissues. Comparing the results from MRI and surgery, the sensitivity, specificity, and accuracy of MRI examination were 93.55%, 71.43%, and 89.47% respectively for preganglionic injury, and 91.30%, 60.00%, and 85.71% respectively for postganglionic injury.
CONCLUSIONS: The combination of STIR, balance-FFE, and DWIBS sequences can display brachial plexus pre-ganglionic and post-ganglionic injury clearly, effectively, and accurately.
Keywords: Brachial Plexus, Magnetic Resonance Imaging, Models, Statistical