scholarly journals Low-Rank Matrix Denoising Algorithm-Based Magnetic Resonance Imaging Combined with Computed Tomography Images in the Diagnosis of Cerebral Aneurysm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Daigui Zhang ◽  
Lihua Zhou ◽  
Tingdi Zhang ◽  
Shuai Wang ◽  
Yue Li
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Magnetic Resonance ◽  
Low Rank ◽  
Resonance Imaging ◽  
Ct Diagnosis ◽  
Rank Matrix ◽  
Mri Diagnosis ◽  
Low Rank Matrix ◽  
Better Than

This study was to analyze the diagnostic effects of computed tomography (CT) and magnetic resonance imaging (MRI) in patients with cerebrovascular diseases (CVDs) based on low-rank matrix denoising (LRMD) algorithm. The LRMD algorithm was adopted for MRI diagnosis and CT diagnosis for comparative analysis. 129 CVD patients were selected as the research objects, 43 cases were diagnosed by CT, 43 cases were diagnosed by MRI under LRMD, and the other 43 cases were diagnosed by CT + MRI. The results showed that the diagnostic compliance rates (DCRs) of CT group in the cerebral hemorrhage (CH), cerebral infarction (CI), and cerebral aneurysm (CA) were 95.1%, 94.7%, and 70%, respectively, while those in the MRI group were 99.01%, 97.71%, and 100%, respectively. Thus, it was obtained that MRI diagnosis was much better than CT diagnosis, and CT + MRI showed the best diagnosis efficacy, showing statistical differences ( P < 0.05 ). The accuracy, sensitivity, and specificity of MRI diagnosis under the LRMD algorithm were 96.28%, 88.76%, and 90.62%, respectively, which were superior to those of CT diagnosis (92.71%, 84.94%, and 80.71%, respectively). The diagnosis cost per case (DC/C) (799.73 ± 100.02 yuan) and the total diagnosis cost (TDC) (58,521.67 ± 301.62 yuan) in the MRI group were higher than those in the CT group (601.42 ± 83.61 yuan and 39,819.2 ± 198.72, respectively) ( P < 0.05 ). In conclusion, CT + MRI under the LRMD algorithm showed good potential in diagnosis of CVD; MRI based on the LRMD algorithm showed a higher positive rate in the diagnosis of CA and was better than CT diagnosis, and CT + MRI showed the best diagnosis effect and could improve the clinical diagnosis rate.

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.

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.

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