Coronary artery disease (CAD) is the leading global cause of mortality, accounting for approximately 30% of all deaths. It is primarily characterized by the accumulation of atherosclerotic plaques within the coronary arteries, leading to reduced blood flow to the heart muscle. Early detection of atherosclerotic plaques is crucial to prevent major adverse cardiac events. Notably, recent studies have shown that 15% of myocardial infarctions occur in patients with non-obstructive CAD, underscoring the importance of comprehensive plaque assessment beyond merely identifying obstructive lesions. Cardiac Computed Tomography Angiography (CCTA) has emerged as a cost-effective and efficient technique for excluding obstructive CAD, particularly in patients with a low-to-intermediate clinical likelihood of the disease. Recent advancements in CCTA technology, such as improved resolution and reduced scan times, have mitigated many technical challenges, allowing for precise quantification and characterization of both calcified and non-calcified atherosclerotic plaques. This review focuses on two critical physiological aspects of atherosclerotic plaques: the burden of calcifications, assessed via the coronary artery calcium score (CACs), and perivascular fat attenuation index (pFAI), an emerging marker of vascular inflammation. The CACs, obtained through non-contrast CT scans, quantifies calcified plaque burden and is widely used to stratify cardiovascular risk, particularly in asymptomatic patients. Despite its prognostic value, the CACs does not provide information on non-calcified plaques or inflammatory status. In contrast, the pFAI, derived from CCTA, serves as an indirect marker of coronary inflammation and has shown potential in predicting adverse cardiac events. Combining both CACs and pFAI assessment could offer a comprehensive risk stratification approach, integrating the established calcification burden with novel inflammatory markers to enhance CAD prevention and management strategies.

Background/Objectives: Echocardiography is the reference standard for grading aortic stenosis (AS); however, it yields discordant severity estimates in up to 40% of patients. Multidetector computed tomography (MDCT)-derived methods for calculating aortic valve area (AVA) may improve diagnostic concordance, but their diagnostic performance, validation against invasive hemodynamics, and the influence of left ventricular outflow tract (LVOT) morphology on severity grading remain insufficiently investigated. Methods: We retrospectively analyzed 307 patients with normal-flow, high-gradient calcific AS who underwent echocardiography, MDCT, and cardiac catheterization. AVA was calculated using (1) echocardiographic LVOT diameter, (2) hybrid Doppler–MDCT planimetric LVOT area, and (3) corrected echocardiographic LVOT diameter (×1.13). Agreement, correlation, and diagnostic performance were assessed using Bland–Altman analysis, Pearson correlation, ROC analysis, and McNemar’s test. Subgroups defined by diagnostic concordance and MDCT-derived LVOT size were compared using ANOVA. Results: Hybrid AVA showed a strong correlation with echocardiographic AVA (r = 0.749, p < 0.001), with a mean difference of +0.11 ± 0.15 cm². Both methods demonstrated similar relationships with invasive and non-invasive hemodynamic markers of AS severity. When combined with echocardiography, the hybrid method increased concordant classification of severe AS by 8%. In contrast, corrected AVA performed significantly worse, leading to more discordant classifications. LVOT size was significantly associated with variability in AVA and Doppler velocity index, independent of flow status. Conclusions: Hybrid MDCT-derived AVA provides diagnostic performance equivalent to echocardiography and improves concordance in selected patients. LVOT size influences key echocardiographic parameters and may warrant tailored diagnostic thresholds.