Abstract
This study was carried out to explore the diagnostic effect of magnetic resonance imaging (MRI) based on the low-rank matrix (LRM) denoising algorithm under gradient sparse prior for the tibial plateau fracture (TPF) combined with meniscus injury (TPF + MI). In this study, the prior information of the noise-free MRI image block was combined with the self-phase prior, the gradient prior of MRI was introduced to eliminate the ringing effect, and a new MRI image denoising algorithm was constructed, which was compared with the anisotropic diffusion fusion (ADF) algorithm. After that, the LRM denoising algorithm based on gradient sparse prior was applied to the diagnosis of 112 patients with TPF + MI admitted to hospital, and the results were compared with those of the undenoised MRI image. Then, the incidence of patients with all kinds of different meniscus injury parting was observed. A total of 66 cases (58.93%) of meniscus tears (MT) were found, including 56 cases (50.00%) of lateral meniscus (LM), 10 cases (8.93%) of medial meniscus (MM), 16 cases (14.29%) of meniscus contusion (MC), and 18 cases (16.07%) of meniscus degenerative injury (MDI). The incidences of MI in Schatzker subtypes were 0%, 53.33% (24/45), 41.67% (5/12), 76.47% (13/17), 78.94% (15/19), and 23.53% (4/17), showing no statistically significant difference ( P > 0.05 ), but the incidence of MT was 54.46% (61/112), which was greatly higher than that of MC (15.18% (17/112)), and the difference was statistically obvious ( P < 0.05 ). The diagnostic specificity (93.83%) and accuracy (95.33%) of denoised MRI images were dramatically higher than those of undenoised MRI images, which were 78.34% and 71.23%, respectively, showing statistically observable differences ( P < 0.05 ). In short, the algorithm in this study showed better denoising performance with the most retained image information. In addition, denoising MRI images based on the algorithm constructed in this study can improve the diagnostic accuracy of MI.
Highlights
Tibial plateau fracture (TPF) is an important and critical joint fracture or complex fracture of tissue destruction
The prior information of the noise-free Magnetic resonance imaging (MRI) image block was combined with the self-phase prior, the gradient prior of MRI was introduced to eliminate the ringing effect, and a new MRI image denoising algorithm was constructed, which was compared with the anisotropic diffusion fusion (ADF) algorithm
The low-rank matrix (LRM) denoising algorithm based on gradient sparse prior was applied to the diagnosis of 112 patients with TPF + meniscus injury (MI) admitted to hospital, and the results were compared with those of the undenoised MRI image, hoping to provide some theoretical references for the study of TPF + MI diagnosis based on MRI images under artificial intelligence (AI) algorithms
Summary
Tibial plateau fracture (TPF) is an important and critical joint fracture or complex fracture of tissue destruction. Research results show that meniscus injury (MI) is one of the important factors that cause arthritis. E incidence rate is as high as 49%, which can cause physical damage and psychological double torture to patients [2, 3]. Erefore, improving the diagnostic accuracy of tibial plateau fracture combined with meniscus injury (TPF + MI) is the top priority of joint medical imaging. Magnetic resonance imaging (MRI) examination has become a common and important means of medical diagnosis. MRI examination is affected by noise in imaging, so that most of the MRI images obtained have different intensities of noise, which causes abnormally blurred medical image tissue edges, greatly affecting the accurate diagnosis of the disease [4, 5]. Farkas et al [6] proved that the combination of the Gaussian mixture model (GMM) and maximum A posteriori algorithm based on expectation maximization (MAP-EM) can solve the inversion in image processing
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