Abstract
This study was to analyze the application value of a reconstruction algorithm in CT images of patients with coronary heart disease and analyze the correlation between epicardial fat volume and coronary heart disease. An optimized reconstruction algorithm was constructed based on compressed sensing theory in this study. Then, the optimized algorithm was applied to the image reconstruction of multislice spiral CT image data after testing its sensitivity, accuracy, and specificity. 60 patients with suspected angina pectoris were divided into lesion group (40 cases) and normal group (20 cases) according to whether there were coronary atherosclerotic plaques in cardiac vessels. The results showed that the sensitivity, specificity, and accuracy of the optimized reconstruction algorithm were 91.78%, 84.27%, and 95.32%, and the running time was (12.18 ± 2.49) s. The CT value of the liver and the CT ratio of the liver and spleen in the lesion group were (53.81 ± 5.91) and (3.88 ± 0.67), respectively. There was no significant difference between the two groups ( P > 0.05 ). The body mass index and epicardial fat volume in the lesion group were (31.93 ± 4.54) kg/m2 and (120.09 ± 22.01) cm3, respectively. The body mass index and fat volume in the lesion group were significantly higher than those in the normal group ( P < 0.05 ). The epicardial fat constitution increased with the increase of the number of coronary arteries involved, and there was a positive correlation between them. Among patients with different coronary atherosclerotic plaques, the epicardial fat volume in patients with mixed plaques was the largest ( P < 0.05 ). In summary, optimizing CT images under compressed a sensing reconstruction algorithm could effectively improve the diagnostic accuracy of doctors. Epicardial fat volume was positively correlated with coronary heart disease. Epicardial fat volume could be used as one of the important indexes to predict coronary heart disease.
Highlights
Coronary atherosclerotic heart disease is known as coronary heart disease
It was noted that the sensitivity was 91.78%, the specificity was 84.27%, the accuracy rate was 95.32%, and the running time was (12.18 ± 2.49) s, suggesting that the compressed sensingbased reconstruction algorithm lifted the diagnosis accuracy
An optimized reconstruction algorithm is constructed based on compressed sensing theory
Summary
Coronary atherosclerotic heart disease is known as coronary heart disease It is the general term for a series of cardiovascular diseases arising from coronary atherosclerotic plaques, causing insufficient local blood supply and insufficient oxygen supply to the heart. It has gradually become a major disease threatening human life [1]. It can buffer the torsion of the coronary arteries caused by arterial pulsation and heart contraction, promote the remodeling of the coronary arteries, and regulate the myocardium and coronary arteries through paracrine It affects the metabolism of the myocardium and coronary artery smooth muscle and influences the heart or vascular functions [2]. Epicardial fat can be used as a marker to reflect the local metabolism because it is closely related to traditional vascular risk factors and can reflect the progress of coronary atherosclerosis from the perspective of local inflammation and system metabolism [4]
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