Effect of 320-row CT reconstruction technology on fractional flow reserve derived from coronary CT angiography based on machine learning: single- versus multiple-cardiac periodic images

Abstract

Background: Fractional flow reserve derived from computed tomography (CT-FFR) can be used to noninvasively evaluate the functions of coronary arteries and has been widely welcomed in the field of cardiovascular research. However, whether different image reconstruction schemes have an effect on CT-FFR analysis through single-and multiple-cardiac periodic images in the same patient has not been investigated.Methods: This study retrospectively enrolled 122 patients who underwent 320-row computed tomography (CT) examination with both single-and multiple-cardiac periodic reconstruction schemes; a total of 366 coronary arteries were analyzed. The lowest CT-FFR values of each vessel and the poststenosis CT-FFR values of the lesion-specific coronary artery were measured using the two reconstruction techniques.The Wilcoxon signed-rank test was used to compare differences in CT-FFR values between the two reconstruction techniques. Spearman correlation analysis was performed to determine the relationship between CT-FFR values derived using the two methods. Bland-Altman and intraclass correlation coefficient (ICC) analyses were performed to evaluate the consistency of CT-FFR values.Results: In all blood vessels, the lowest CT-FFR values showed no significant differences between the two reconstruction techniques in the left anterior descending artery (LAD; P=0.65), left circumflex artery (LCx; P=0.46), or right coronary artery (RCA; P=0.22). In blood vessels with atherosclerotic plaques, the poststenosis CT-FFR values (2 cm distal to the maximum stenosis) exhibited no significant differences between the two reconstruction techniques in the LAD (P=0.78), LCx (P=1.00), or RCA (P=1.00). The mean CT-FFR values of single-and multiple-cardiac periodic images showed excellent correlation and minimal bias in all groups.Conclusions: CT-FFR analysis based on an artificial intelligence deep learning neural network is stable and not affected by the type of 320-row CT reconstruction technology.

Diagnosis of coronary artery disease in patients with type 2 diabetes mellitus based on computed tomography and pericoronary adipose tissue radiomics: a retrospective cross-sectional study

“…Previous studies have shown that the CACS can be used as a proxy for coronary atherosclerotic plaque burden to predict cardiovascular risk [ 45 ], and the CACS increases with cardiovascular risk in the T2DM patient population [ 46 ] to serve as a composite measure of atherosclerotic disease [ 16 ]. CT-FFR, as an indicator of coronary vascular haemodynamic stenosis, provides both physiological and corresponding anatomical information about the coronary vessels [ 47 ]; DS directly reflects the anatomical stenosis of the coronary vessels. Whereas PAT is located near the myocardium, and its excessive activity accelerates coronary atherosclerosis and, thus, cardiovascular disease [ 48 ].…”

Blood Vessels Segmentation of Coronary X-Rays Angiography Images Including Edge based Features and Artificial Intelligence Approaches

“…Through this process, the author found that the highest accuracy was achieved with a learning rate of 0.00001, a dropout rate of 0.3, and the Adam optimizer. Furthermore, the DenseNet model exhibited the highest accuracy, precision, recall, and F1-Score (93%) compared to the other pretrained models, ResNet-50 and VGG-16 [22].…”

Identification of patients with acute coronary syndrome and representation of their degree of inflammation: application of pericoronary adipose tissue within different radial distances of the proximal coronary arteries

“…Coronary artery disease (CAD) is one of the diseases with the highest morbidity in the world ( 1 ), and despite current improvements in interventional and pharmacological treatments, acute coronary syndrome (ACS) remains a leading cause of death in developed countries ( 2 ). Inflammation of coronary vessels will ultimately promote atherosclerosis and atherothrombosis in patients with ACS ( 3 , 4 ).…”