Intelligent Algorithm-Based Multislice Spiral Computed Tomography to Diagnose Coronary Heart Disease

Abstract

In this study, dictionary learning and expectation maximization reconstruction (DLEM) was combined to denoise 64-slice spiral CT images, and results of coronary angiography (CAG) were used as standard to evaluate its clinical value in diagnosing coronary artery diseases. 120 patients with coronary heart disease (CHD) confirmed by CAG examination were retrospectively selected as the research subjects. According to the random number table method, the patients were divided into two groups: the control group was diagnosed by conventional 64-slice spiral CT images, and the observation group was diagnosed by 64-slice spiral CT images based on the DLEM algorithm, with 60 cases in both groups. With CAG examination results as the standard, the diagnostic effects of the two CT examination methods were compared. The results showed that when the number of iterations of maximum likelihood expectation maximization (MLEM) algorithm reached 50, the root mean square error (RMSE) and peak signal to noise ratio (PSNR) values were similar to the results obtained by the DLEM algorithm under a number of iterations of 10 when the RMSE and PSNR values were 18.9121 dB and 74.9911 dB, respectively. In the observation group, 28.33% (17/60) images were of grade 4 or above before processing; after processing, it was 70% (42/60), significantly higher than the proportion of high image quality before processing. The overall diagnostic consistency, sensitivity, specificity, and accuracy (88.33%, 86.67%, 80%, and 85%) of the observation group were better than those in the control group (60.46%, 62.5%, 58.33%, and 61.66%). In conclusion, the DLEM algorithm has good denoising effect on 64-slice spiral CT images, which significantly improves the accuracy in the diagnosis of coronary artery stenosis and has good clinical diagnostic value and is worth promoting.