Abstract
This study aimed to compare the role of magnetic resonance imaging (MRI) and computed tomography (CT) images based on the low-rank matrix (LRM) denoising (LRMD) algorithm in the diagnosis of cerebral aneurysms (CAs). By comparing the role of MRI and CT in the diagnosis of CA, it would be helpful to formulate more reasonable diagnosis strategies and provide a solid foundation for clinical treatment of patients. 80 patients with cerebral aneurysm admitted to hospital were selected as the research objects. First, the LRMD algorithm was established and applied to the image denoising process of MRI and CT. Then, the diagnosis rate of CA by MRI and CT before and after denoising was compared, and the diagnostic rates of the two methods for aneurysms of different sizes were compared. Finally, the location, foci, and patient satisfaction of the aneurysm were compared. The results showed that the MRI and CT images after denoising with LRM were clearer, and the secondary structures in the brain were more obvious. It meant that LRMD had good image denoising effect. The diagnostic rate of denoised MRI and CT was improved. Although the difference was not statistically notable, the diagnostic rate of CT was obviously higher in contrast to MRI ( P < 0.05 ). The diagnostic rate of CT for smaller aneurysms (<3 mm and 3–5 mm) was also notably higher in contrast to MRI ( P < 0.05 ). However, there was no difference in the diagnosis of tumor location between the two. The clarity of CT diagnostic images was better than MRI ( P < 0.05 ). Accordingly, patients were more satisfied with CT in contrast to MRI ( P < 0.05 ). In summary, CT images based on the LRMD algorithm were superior to MRI in the diagnosis of CA, and it could provide more accurate diagnosis results.
Highlights
CA is an aneurysm-like protrusion caused by the destruction of the elastic layer in the wall of the intracranial artery, which results in the abnormal expansion of the lumen [1]. e cause of the disease is mostly congenital dysplasia, and others are disease interference factors, including hypertension and arteriosclerosis
MRI and CT detection have the characteristics of noninvasive, fast, and accurate. erefore, they have gradually replaced the digital subtraction angiography technique, which is the standard procedure for the diagnosis and detection of CA [4]
It was found that the sharpness of the lesion in CT examination was better in contrast to MRI (P < 0.05), and the patients’ satisfaction with CT examination was higher in contrast to MRI (P < 0.05). ese results indicated that the diagnostic effect of CT was obviously better in contrast to MRI. It analyzed and compared the role of MRI and CT images based on the LRMD algorithm in the diagnosis of CA. e results showed that CT imaging was superior to MRI imaging in the diagnosis of CA in all aspects, especially in the diagnosis rate, the detection rate of tumor size, the definition of the lesion, and the degree of patient satisfaction. e influence of low-rank denoising on the diagnosis result was not very notable
Summary
CA is an aneurysm-like protrusion caused by the destruction of the elastic layer in the wall of the intracranial artery, which results in the abnormal expansion of the lumen [1]. e cause of the disease is mostly congenital dysplasia, and others are disease interference factors, including hypertension and arteriosclerosis. E cause of the disease is mostly congenital dysplasia, and others are disease interference factors, including hypertension and arteriosclerosis. Cerebrovascular diseases often occur in the middle-aged and elderly people. It is a common neurological disease with a high fatality rate and disability rate. MRI and CT technology are two commonly used clinical detection techniques, which include the detection of CA. Erefore, they have gradually replaced the digital subtraction angiography technique, which is the standard procedure for the diagnosis and detection of CA [4]. E major limitation of MRI and CT imaging in diagnosis is the image quality. E various interferences encountered during image acquisition will cause noise in the image, resulting in deviation or even error in the final obtained information. The performance of equipment is constantly improving, software-based image
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