ABSTRACT
This study aimed to implement a deep learning-based super-resolution (SR) technique that can assist in the diagnosis and surgery of trigeminal neuralgia (TN) using magnetic resonance imaging (MRI). Experimental methods applied SR to MRI data examined using five techniques, including T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), contrast-enhancement T1WI (CE-T1WI), T2WI turbo spin-echo series volume isotropic turbo spin-echo acquisition (VISTA), and proton density (PD), in patients diagnosed with TN. The image quality was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). High-quality reconstructed MRI images were assessed using the Leksell coordinate system in gamma knife radiosurgery (GKRS). The results showed that the PSNR and SSIM values achieved by SR were higher than those obtained by image postprocessing techniques, and the coordinates of the images reconstructed in the gamma plan showed no differences from those of the original images. Consequently, SR demonstrated remarkable effects in improving the image quality without discrepancies in the coordinate system, confirming its potential as a useful tool for the diagnosis and surgery of TN.
ABSTRACT
The surgical treatment of Jefferson fractures generally involves solid fusion, which limits the range of motion of the upper cervical spine. Herein, we present a case of a Jefferson fracture that was surgically treated using direct C1 posterior arch screw reduction and osteosynthesis instead of fusion surgery. Postoperative computed tomography (CT) and plain radiography revealed that both C1 posterior arch screws were well positioned, and placing lag screws at the C1 level played a key role in fracture reduction. The present case highlights a surgical technique, which can preserve neck motion by using lag screws for reduction and osteosynthesis.