To compare the image quality, evaluability, diagnostic accuracy, and radiation exposure of high-spatial-resolution (HR, 0.23-mm) computed tomographic (CT) coronary angiography with standard spatial resolution (SR, 0.625-mm) 64-section imaging in patients at high risk for coronary artery disease (CAD) by using invasive coronary angiography (ICA) as the reference method. Written informed consent was obtained from all patients, and the study protocol was approved by the institutional ethical committee. Patients at high risk for CAD (n = 184) who were scheduled for ICA were randomly assigned for study with SR (n = 91) or HR (n = 93) coronary CT angiography before they underwent ICA. To compare the two groups, the Student t test or Wilcoxon test were used to evaluate differences in continuous variables. The χ(2) test or Fisher exact test were used, as appropriate, for categorical data. The McNemar test was used to compare the diagnostic performance of coronary CT angiography versus that of ICA in each group. HR coronary CT angiography showed a higher image quality score (3.7 vs 3.4, P < .001) and evaluability (97% vs 92%, P < .002). In a segment-based analysis, HR coronary CT angiography showed a higher specificity, positive predictive value, and accuracy in comparison with SR coronary CT angiography (98%, 91%, and 99% vs 95%, 80%, and 95%, respectively; P < .001). Moreover, HR coronary CT angiography showed a better agreement with ICA for calcified plaques compared with SR coronary CT angiography and ICA (83% vs 53%, P < .001). In a patient-based analysis, HR coronary CT angiography showed higher specificity and accuracy compared with SR coronary CT angiography (91% and 98% vs 46% and 92%, respectively; P < .01). No differences in radiation exposure were found between the two groups. Improved evaluability and accuracy were seen with HR compared with SR coronary CT angiography of calcified coronary artery lesions, suggesting a potential use for this technology in patients at high risk for CAD.

Vascular inflammation is recognized as the primary trigger of acute coronary syndrome (ACS). However, current noninvasive methods are not capable of accurately detecting coronary inflammation. Epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT), in addition to their role as an energy reserve system, have been found to contribute to the development and progression of coronary artery calcification, inflammation, and plaque vulnerability. They also participate in the vascular response during ischemia, sympathetic stimuli, and arrhythmia. As a result, the evaluation of EAT and PCAT using imaging techniques such as computed tomography (CT), cardiac magnetic resonance (CMR), and nuclear imaging has gained significant attention. PCAT-CT attenuation, which measures the average CT attenuation in Hounsfield units (HU) of the adipose tissue, reflects adipocyte differentiation/size and leukocyte infiltration. It is emerging as a marker of tissue inflammation and has shown prognostic value in coronary artery disease (CAD), being associated with plaque development, vulnerability, and rupture. In patients with acute myocardial infarction (AMI), an inflammatory pericoronary microenvironment promoted by dysfunctional EAT/PCAT has been demonstrated, and more recently, it has been associated with plaque rupture in non-ST-segment elevation myocardial infarction (NSTEMI). Endothelial dysfunction, known for its detrimental effects on coronary vessels and its association with plaque progression, is bidirectionally linked to PCAT. PCAT modulates the secretory profile of endothelial cells in response to inflammation and also plays a crucial role in regulating vascular tone in the coronary district. Consequently, dysregulated PCAT has been hypothesized to contribute to type 2 myocardial infarction with non-obstructive coronary arteries (MINOCA) and coronary vasculitis. Recently, quantitative measures of EAT derived from coronary CT angiography (CCTA) have been included in artificial intelligence (AI) models for cardiovascular risk stratification. These models have shown incremental utility in predicting major adverse cardiovascular events (MACEs) compared to plaque characteristics alone. Therefore, the analysis of PCAT and EAT, particularly through PCAT-CT attenuation, appears to be a safe, valuable, and sufficiently specific noninvasive method for accurately identifying coronary inflammation and subsequent high-risk plaque. These findings are supported by biopsy and in vivo evidence. Although speculative, these pieces of evidence open the door for a fascinating new strategy in cardiovascular risk stratification. The incorporation of PCAT and EAT analysis, mainly through PCAT-CT attenuation, could potentially lead to improved risk stratification and guide early targeted primary prevention and intensive secondary prevention in patients at higher risk of cardiac events.