Cardiac CT in Non-Obstructive Coronary Artery Disease (NOCAD): A Literature Review

Non-obstructive coronary artery disease (NOCAD) encompasses a heterogeneous group of conditions in which patients present with angina, ischemia or myocardial infarction despite the absence of obstructive epicardial stenoses. This spectrum includes myocardial infarction with non-obstructive coronary arteries (MINOCA) and angina or ischemia with non-obstructive coronary arteries (ANOCA/INOCA), entities increasingly recognized as clinically significant and associated with adverse outcomes. Advances in cardiac computed tomography (CT) have expanded the diagnostic capabilities beyond the exclusion of obstructive coronary artery disease, enabling comprehensive anatomical, functional and tissue-level assessment relevant to NOCAD. CT allows precise identification of non-obstructive atherosclerosis, high-risk plaque features, myocardial bridging and structural vascular remodelling. Quantitative and qualitative characterization of plaque burden correlates with ischemic risk and provides prognostic information that complements traditional stenosis-based evaluation. Emerging CT-derived biomarkers, such as pericoronary fat attenuation index and epicardial adipose tissue metrics, offer insight into vascular inflammation and microvascular dysfunction, key mechanisms in NOCAD. Functional CT techniques, such as CT-derived fractional flow reserve and CT perfusion imaging, enable non-invasive assessment of hemodynamic significance and microvascular impairment, although their routine use is limited by methodological variability and evolving clinical evidence. Beyond coronary evaluation, CT also provides myocardial tissue characterization, detects extracardiac causes of symptoms and contributes to comprehensive differential diagnosis. Despite its strengths, cardiac CT remains limited by spatial resolution, radiation exposure and its inability to directly visualize the microcirculation. Nevertheless, ongoing technological refinement and integration of computational modelling are likely to enhance its diagnostic and prognostic role.