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

Feng Gao,Mingcan Wu,Enas Abdulhay

doi:10.1155/2021/9687591

Feng Gao, Mingcan Wu + Show 1 more

Open Access

https://doi.org/10.1155/2021/9687591

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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.

Highlights

lumbar disc herniation (LDH) is a common chronic spinal degenerative disease [1]
Denoising Algorithms for Magnetic resonance imaging (MRI) Images Based on the Deep Learning. e traditional image denoising algorithms cannot completely remove the Rician noise in MRI images with relatively fuzzy image edge contours, and the deep learning mainly focuses on removing the Gaussian white noise. erefore, the deep ResNet method was proposed based on the deep learning to remove the Rician noise in MRI images
Analysis of Denoising Performance of Different Algorithms. e proposed deep learning denoising algorithm was compared with the weighted stable matching (WSM) algorithm and denoising convolutional neural network (CNN) (DnCNN) algorithm in terms of peak signal-to-noise ratio (PSNR) value (Figure 3(a)). e PSNR value of the Hidden layer Conv

Summary

Introduction

LDH is a common chronic spinal degenerative disease [1]. In recent years, the incidence of LDH has shown a significant upward trend, and a large number of studies have shown that LFJs are related to the occurrence of LDH [2]. Studies have shown that the angle and asymmetry of LFJs are related to the occurrence of LDH [3]. In the current research, there are generally cases where the results of imaging examinations do not match the patient’s signs and symptoms. Due to the limited quality of MRI images, there are certain controversies about the occurrence of LFJ and LDH. E quality of reconstructed images in traditional MRI algorithms is low. Some researchers applied compressed sensing (CS) theory to the reconstruction of MRI images and established the CSMRI algorithm, which can obtain the precise sparsity prior of the image and capture the rich structural information of the image, and it is widely adopted in the field of MR image reconstruction.

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