scholarly journals Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis and Evaluation of Tibial Plateau Fracture Combined with Meniscus Injury

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qimao Fu ◽  
Chuizhi Huang ◽  
Yan Chen ◽  
Nailong Jia ◽  
Jinghui Huang ◽  
...  
Keyword(s):  
Tibial Plateau ◽  
Tibial Plateau Fracture ◽  
Low Rank ◽  
Resonance Imaging ◽  
Meniscus Injury ◽  
Rank Matrix ◽  
Plateau Fracture ◽  
Sparse Prior ◽  
Low Rank Matrix ◽  
Mri Image

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.

scholarly journals Late-onset isolated cerebral fat embolism syndrome after a simple tibial plateau fracture: a rare case report

2021 ◽  
Vol 49 (7) ◽  
pp. 030006052110284
Author(s):  
Ta-Li Hsu ◽  
Tien-Chi Li ◽  
Fei-Pi Lai ◽  
Ming Ouhyoung ◽  
Chih-Hung Chang ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Rare Case ◽  
Tibial Plateau ◽  
Tibial Plateau Fracture ◽  
Fat Embolism ◽  
Resonance Imaging ◽  
Fat Embolism Syndrome ◽  
Embolism Syndrome ◽  
Plateau Fracture

Fat embolism syndrome (FES) is a complication of long bone fractures that often occurs within 72 hours of injury. Early-onset isolated cerebral fat embolism is catastrophic and rarely reported. We herein present a rare case of delayed-onset isolated cerebral FES that developed 10 days after definite fixation of a left tibial plateau fracture. A 70-year-old woman was injured in a traffic accident and diagnosed with a left tibial plateau fracture. However, she developed sudden loss of consciousness (E4V1M1) and quadriplegia 10 days after fracture fixation. Her vital signs showed no respiratory distress. Diagnosis of isolated cerebral FES was made based on magnetic resonance imaging of the brain, the findings of which were compatible with the clinical neurological findings. After supportive care and rehabilitation, her consciousness became clear on the second day of admission, and her consciousness changed to E4V5M6. She gradually regained strength in her right limbs but had residual left limb paraplegia. Isolated cerebral FES should always be considered for patients who develop a change in consciousness, even beyond 72 hours after injury. Imaging may not initially show definitive abnormalities. Repeated magnetic resonance imaging should be considered if the initial clinical presentation does not fully meet Gurd’s criteria.

scholarly journals Analysis on Characteristics of Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis of Cerebral Aneurysm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jun Li ◽  
Jin Li ◽  
Qin Hu
Keyword(s):  
Magnetic Resonance Imaging ◽  
Cerebral Aneurysm ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Low Rank ◽  
Resonance Imaging ◽  
Rank Matrix ◽  
Before And After ◽  
Low Rank Matrix

This study was to explore the effect of a low-rank matrix denoising (LRMD) algorithm based on the Gaussian mixture model (GMM) on magnetic resonance imaging (MRI) images of patients with cerebral aneurysm and to evaluate the practical value of the LRMD algorithm in the clinical diagnosis of cerebral aneurysm. In this study, the intracranial MRI data of 40 patients with cerebral aneurysm were selected to study the denoising effect of the low-rank matrix denoising algorithm based on the Gaussian mixture model on MRI images of cerebral aneurysm under the influence of Rice noise, to evaluate the PSNR value, SSIM value, and clarity of MRI images before and after denoising. The diagnostic accuracy of MRI images of cerebral aneurysms before and after denoising was compared. The results showed that after the low-rank matrix denoising algorithm based on the Gaussian mixture model, the PSNR, SSIM, and sharpness values of intracranial MRI images of 10 patients were significantly improved ( P < 0.05 ), and the diagnostic accuracy of MRI images of cerebral aneurysm increased from 76.2 ± 5.6 % to 93.1 ± 7.9 % , which could diagnose cerebral aneurysm more accurately and quickly. In conclusion, the MRI images processed based on the low-rank matrix denoising algorithm under the Gaussian mixture model can effectively remove the interference of noise, improve the quality of MRI images, optimize the accuracy of MRI image diagnosis of patients with cerebral aneurysm, and shorten the average diagnosis time, which is worth promoting in the clinical diagnosis of patients with cerebral aneurysm.

EXACT LOW-RANK MATRIX RECOVERY VIA NONCONVEX SCHATTEN p-MINIMIZATION

2013 ◽  
Vol 30 (03) ◽  
pp. 1340010 ◽  
Author(s):  
LINGCHEN KONG ◽  
NAIHUA XIU
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Low Rank ◽  
Magnetic Resonance Imaging System ◽  
Resonance Imaging ◽  
Matrix Recovery ◽  
Rank Matrix ◽  
Restricted Isometry Constant ◽  
Low Rank Matrix Recovery ◽  
Low Rank Matrix

The low-rank matrix recovery (LMR) arises in many fields such as signal and image processing, quantum state tomography, magnetic resonance imaging, system identification and control, and it is generally NP-hard. Recently, Majumdar and Ward [Majumdar, A and RK Ward (2011). An algorithm for sparse MRI reconstruction by Schatten p-norm minimization. Magnetic Resonance Imaging, 29, 408–417]. had successfully applied nonconvex Schatten p-minimization relaxation of LMR in magnetic resonance imaging. In this paper, our main aim is to establish RIP theoretical result for exact LMR via nonconvex Schatten p-minimization. Carefully speaking, letting [Formula: see text] be a linear transformation from ℝm×n into ℝs and r be the rank of recovered matrix X ∈ ℝm×n, and if [Formula: see text] satisfies the RIP condition [Formula: see text] for a given positive integer k ∈ {1, 2, …, m – r}, then r-rank matrix can be exactly recovered. In particular, we obtain a uniform bound on restricted isometry constant [Formula: see text] for any p ∈ (0, 1] for LMR via Schatten p-minimization.

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