Deep Learning-Based Denoised MRI Images for Correlation Analysis between Lumbar Facet Joint and Lumbar Disc Herniation in Spine Surgery

Abstract

This work aimed to explore the relationship between spine surgery lumbar facet joint (LFJ) and lumbar disc herniation (LDH) via compressed sensing algorithm-based MRI images to analyze the clinical symptoms of patients with residual neurological symptoms after LDH. Under weighted BM3D denoising, Epigraph method was introduced to establish the novel CSMRI reconstruction algorithm (BEMRI). 127 patients with LDH were taken as the research objects. The BEMRI algorithm was compared with others regarding peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Patients’ bilateral LFJ angles were compared. The relationships between LFJ angles, lumbar disc degeneration, and LFJ degeneration were analyzed. It turned out that the PSNR and SSIM of BEMRI algorithm were evidently superior to those of other algorithms. The proportion of patients with grade IV degeneration was at most 31.76%. Lumbar disc grading was positively correlated with change grading of LFJ degeneration ( P < 0.001 ). LFJ asymmetry was positively correlated with LFJ degeneration grade and LDH ( P < 0.001 ). Incidence of residual neurological symptoms in patients aged 61–70 years was as high as 63.77%. The proportion of patients with severe urinary excretion disorders was 71.96%. Therefore, the BEMRI algorithm improved the quality of MRI images. Degeneration of LDH was positively correlated with degeneration of LFJ. Asymmetry of LFJ was notably positively correlated with the degeneration of LFJ and LDH. Patients aged 61–70 years had a high incidence of residual neurological symptoms after surgery, most of which were manifested as urinary excretion disorders.

A Multi-Element Identification System Based on Deep Learning for the Visual Field of Percutaneous Endoscopic Spine Surgery

“…In recent years, arti cial intelligence and machine learning have been widely used and developed in the eld of medical images, such as radiology [8,16] .Image recognition and classi cation are two of its most frequent tasks. It is able to learn the feature distribution and patterns from the provided data, reduces the require to prior rules, and matches required process through nonlinear deep neural network [17] .CT and MRI medical imaging technology are applied in diagnosing of lumbar disc herniation and lumbar spinal stenosis because they are safe and fast and have several imaging angles.Haixing [18] et al proposed a new spinal MRI image segmentation method, which was accurately segmented the vertebral body, lamina, and dural sac in MRI images,and developed a multiscale attention network MANet to diagnose lumbar spinal stenosis.Feng Gao [19] et al used the deep learning-based BEMRI algorithm to remove the Rician noise in the MRI images and explore the relationship between lumbar facet joints and lumbar disc herniation to analyze the postoperative clinical symptoms of patients with LDH.During spinal surgery, a deep learning computer-assisted navigation system can simultaneously build an interactive 3D image in real time to synchronize patient anatomy and surgical instruments.Guoxin Fan [10] et al performed U-netbased deep learning on preoperative lumbar CT plain images of patients diagnosed with L5/S1 singlesegment disc herniation to rapidly and automatically segment and reconstruct the lumbosacral structures and calculate the Kambin triangle area to assess the surgical di culty of patients undergoing percutaneous endoscopic lumbar discectomy though the foraminal approach .Jens Fichtner [11] et al in a retrospective study of 2232 patients undergoing thoracolumbar internal xation surgery found that the secondary revision rate (0.40%) in the 3D uoroscopic navigation group was signi cantly lower (P < 0.01) compared to the freehand placement group (1.14%), which reduced the radiation dose exposure to the operator and the patient and shortened the operative time during internal xation surgery.…”