Correlation analysis of epicardial adipose tissue volume quantified by computed tomography images and coronary heart disease under optimized reconstruction algorithm

Objectives: This paper was aimed to explore the adoption value of low-dose computed tomography (CT) imaging based on optimized ordered subset expectation maximization (OSEM) reconstruction algorithm in the correlation analysis between epicardial adipose tissue (EAT) volume and coronary heart disease (CHD).

Methods: A total of 110 patients with CHD were selected for CT angiography (CTA) and coronary arteriography (CAG) examinations from October 2017 to October 2019. The predictive value of EAT for CHD was analyzed via receiver operating characteristic (ROC) curve.

Results: The results showed that the iteration time and error of the improved OSEM reconstruction algorithm were better than that of MLEM algorithm under the same number of iterations. Age, smoking, hypertension, diabetes, and EAT in control group were obviously lower in contrast to those in CHD group (P<0.05). EAT in control group was (124.50±26.72) mL, and EAT in the CHD group was (159.41±38.51) mL. EAT (B=0.023, P=0.003) was an independent risk factor for CHD, which was suggested by Multiple linear regression analysis. Moreover, EAT was a risk factor for CHD, and was positively correlated with the degree and NSCV.

Conclusion: The optimized OSEM algorithm was used to improve the reconstruction quality of low-dose CT images and used in quantitative measurement of epicardial fat volume. Results showed EAT was an independent risk factor for CHD, and was positively correlated with the number of coronary lesions and Gensini score. It was of great value for the prediction of CHD.