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

Jun Li,Qin Hu,Jin Li

doi:10.1155/2021/9751009

Abstract

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.

Highlights

Cerebral aneurysm refers to the bulge or globular vascular sac formed on the vascular wall of the intracranial artery
The PSNR scores of magnetic resonance imaging (MRI) images processed based on Gaussian mixed low-rank matrix denoising algorithm were compared with those of MRI images before processing (Figure 5), and the results showed that before MRI image denoising, the PSNR scores of MRI images were 40.01, 30.12, and 26.07 under the conditions of 1%, 3%, and 5% noise levels, respectively, while after the Gaussian mixed low-rank matrix denoising algorithm processing, the PSNR scores of MRI images became 45.02, 38.15, and 35.07 under the conditions of 1%, 3%, and 5% noise levels, respectively
The Gaussian mixture model (GMM)-based low-rank matrix denoising (LRMD) algorithm was adopted to construct a brain image block algorithm and applied to the MRI image analysis and diagnosis of cerebral aneurysm patients, so as to explore the value of MRI processed by the GMM-based LRMD algorithm in the diagnosis of cerebral aneurysm

Summary

Introduction

Cerebral aneurysm refers to the bulge or globular vascular sac formed on the vascular wall of the intracranial artery. The abnormally swollen part of the vascular wall causes vascular rupture under long-term impact pressure, which is more common in the internal carotid artery system. It will show different symptoms if the location of cerebral aneurysm, the size of the tumor, and the occurrence of rupture and hemorrhage of the tube wall are different. It is more sensitive to the identification and diagnosis of intracranial cerebral aneurysm It can display the structure of blood vessels without the use of radiographic contrast agents. It shows a high signal on the fluid-attenuated inversion recovery (FLAIR) sequence, so it has become one of the efficient ways to screen

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