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

IF 0.4 4区 化学 Q4 CHEMISTRY, PHYSICAL Concepts in Magnetic Resonance Part A Pub Date : 2022-07-13 DOI:10.1155/2022/8709075
Sainan Jiang, Xiaoyun Song, Chengming Jiang
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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.
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基于深度学习的磁共振-超短时间回波成像分析椎间盘软骨终板退变及康复护理
本研究主要采用基于卷积残差网络(CRN)算法的磁共振-超短时间回波(MR-UTE)成像技术评价椎间盘终板退变(DIDCE)及康复护理干预的效果。本研究将90例住院的椎间盘退变患者随机分为干预组(45例)和对照组(45例)。所有患者均接受磁共振成像系统扫描,并对原始UTE图像进行后处理。对照组患者给予常规护理。干预组采用按摩、康复护理等干预措施。利用CRN算法对欠采样的MR图像进行重构,并与ESPIRiT算法和Regridding算法进行比较。结果表明,CRN重构算法比spirit和Regridding重构算法更有优势。腰痛组纤维环结构部分消失和完全消失的比例高于非腰痛组,差异有统计学意义(P < 0.05)。将90例椎间盘软骨终板退变患者根据是否有腰痛分为腰痛组(62例)和非腰痛组(28例)。干预组患者的护理满意度(97%)显著高于对照组(69%)(P < 0.05)。综上所述,CRN算法成功地去除了欠采样图像中的伪影和噪声。软骨终板、纤维环、骨终板部分消失。康复干预对椎间盘退变患者的治疗有积极的效果,可以提高患者的满意度。
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来源期刊
CiteScore
0.90
自引率
0.00%
发文量
12
审稿时长
>12 weeks
期刊介绍: Concepts in Magnetic Resonance Part A brings together clinicians, chemists, and physicists involved in the application of magnetic resonance techniques. The journal welcomes contributions predominantly from the fields of magnetic resonance imaging (MRI), nuclear magnetic resonance (NMR), and electron paramagnetic resonance (EPR), but also encourages submissions relating to less common magnetic resonance imaging and analytical methods. Contributors come from academic, governmental, and clinical communities, to disseminate the latest important experimental results from medical, non-medical, and analytical magnetic resonance methods, as well as related computational and theoretical advances. Subject areas include (but are by no means limited to): -Fundamental advances in the understanding of magnetic resonance -Experimental results from magnetic resonance imaging (including MRI and its specialized applications) -Experimental results from magnetic resonance spectroscopy (including NMR, EPR, and their specialized applications) -Computational and theoretical support and prediction for experimental results -Focused reviews providing commentary and discussion on recent results and developments in topical areas of investigation -Reviews of magnetic resonance approaches with a tutorial or educational approach
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