Deep Learning-Based Magnetic Resonance-Ultrashort Time of Echo Imaging for Analyzing Degeneration of Intervertebral Disc Cartilage Endplate and Rehabilitation Nursing

Abstract

This study was focused on the magnetic resonance-ultrashort time of echo (MR-UTE) imaging technology based on the convolution residual network (CRN) algorithm to evaluate the degeneration of intervertebral disc endplate (DIDCE) and the efficacy of rehabilitation nursing intervention. In this study, 90 patients with intervertebral disc degeneration in the hospital were randomly divided into an intervention group (45 cases) and a control group (45 cases). All patients were scanned by a magnetic resonance imaging system, and the original UTE images were postprocessed. The control group received routine nursing. The intervention group used massage and rehabilitation nursing intervention measures. The CRN algorithm is used to reconstruct the undersampled MR image and compared with ESPIRiT algorithm and the Regridding algorithm. The result found that CRN has more advantages than ESPIRiT and Regridding reconstruction algorithms. The proportion of partial disappearance and complete disappearance of fibrous ring structure in the low back pain group was higher than that in the non-low back pain group, with a statistical difference (P < 0.05). 90 patients with intervertebral disc cartilage endplate degeneration were divided into lumbago group (62 cases) and nonlumbago group (28 cases) according to whether they had lumbago. The nursing satisfaction of patients in the intervention group (97%) is significantly higher than that of patients in the control group (69%) (P < 0.05). In conclusion, the CRN algorithm successfully removes artifacts and noise in the undersampled image. Cartilage endplate, annulus fibrosus, and bony endplate partially disappeared by the MR-UTE imaging technique. Rehabilitation intervention proved to have a positive effect on the treatment of patients with intervertebral disc degeneration and can improve patients’ satisfaction.