Clinical Impact of CT-Based FFR in Everyday Cardiology: Bridging Computation and Decision-Making

A revolutionary non-invasive method for the thorough evaluation of coronary artery disease (CAD) is fractional flow reserve (FFR) obtained from coronary computed tomography angiography (CCTA). Computed tomography-derived FFR (FFR_CT) assesses both the anatomical and functional significance of coronary lesions simultaneously by utilizing sophisticated computational models, including computational fluid dynamics, machine learning (ML), and Artificial Intelligence (AI) methods. The technological development, validation research, clinical uses, and real-world constraints of FFR_CT are compiled in this review. Large multicenter trials and registries consistently show that FFR_CT is a reliable gatekeeper to invasive coronary angiography (ICA) and increases diagnostic accuracy significantly when compared to coronary Computed Tomography Angiography (CTA) alone, especially in patients with intermediate-risk anatomy. Additionally, FFR_CT has demonstrated benefits in populations with in-stent restenosis (ISR) and in virtual procedural planning. Notwithstanding its advantages, the technique still requires high-quality imaging, and its practical application is constrained by expenses, processing requirements, and image distortions. Continuous developments in automation and deep learning should improve accessibility, effectiveness, and workflow integration in clinical settings. FFR_CT is expected to become more and more important in the individualized treatment of CAD by minimizing unnecessary invasive procedures and improving patient selection for revascularization.