Background: The Ejection Fraction (EF) represents a fundamental pillar for the phenotypic classification and clinical management of cardiovascular diseases. Although trans-thoracic echocardiography (TTE) acts as the first-line examination and cardiac magnetic resonance (CMR) is the reference gold standard, cardiac computed tomography (CCT) has undergone a technological evolution. The advent of wide-detector scanners and artificial intelligence (AI) models has enabled CCT to transition from a purely morphological tool to a modality capable of comprehensive, three-dimensional morpho-functional assessments. Methods: This narrative review evaluates the literature across Scopus, MEDLINE, and Web of Science regarding the calculation of biventricular function and EF using CCT. It provides an updated summary of current clinical applications, technological advancements, and comparative diagnostic reliability against TTE and CMR. Results: The CCT “one-stop-shop” concept allows for the simultaneous acquisition of anatomical data and systolic function metrics (EDV, ESV, SV, EF), optimizing clinical workflows at no additional cost. Being intrinsically three-dimensional, CCT bypasses the geometric assumptions and apical foreshortening artifacts typical of 2D-TTE, demonstrating high volumetric concordance with CMR. Nevertheless, structural limitations persist, primarily regarding ionizing radiation exposure, contrast media toxicity, dependence on heart rhythm stability, and lower temporal resolution compared to CMR. Conclusions: EF determination via CCT has achieved technical maturity and clinical validation. While it does not intend to replace TTE or CMR, it offers synergistic data when integrated with primary anatomical indications. Furthermore, AI integration has been shown to potentially automate this workflow, transforming CCT into an opportunistic screening tool for subclinical cardiac dysfunction.

While traditionally focused on coronary anatomy, cardiac computed tomography now enables non-invasive myocardial tissue characterization. By evaluating late iodine enhancement (LIE) and extracellular volume (ECV), single-energy CT (SECT) provides a valuable alternative to cardiac magnetic resonance for assessing ischemic and non-ischemic pathologies. However, clinical implementation of SECT faces technical challenges, primarily the low contrast-to-noise ratio (CNR) of iodine and the reliance on image subtraction for ECV quantification, both of which increase radiation exposure and susceptibility to spatial misregistration. To address these issues, protocol optimization is essential. Evidence-based recommendations include using low tube voltages to shift the X-ray spectrum closer to the iodine K-edge, paired with high reference tube currents. Additionally, delayed acquisition timing should be tailored to specific pathological targets to account for differences in contrast kinetics, and advanced iterative or deep learning image reconstructions should be implemented to mitigate noise. Optimized SECT demonstrates diagnostic and prognostic utility in conditions like acute myocardial infarction, hypertrophic cardiomyopathy, cardiac amyloidosis, and left ventricular thrombus detection. While spectral imaging represents the future, optimizing SECT through technical adjustments and standardized training is crucial for integrating myocardial characterization into routine workflows.