Advanced Quantitative CT for Coronary Artery Disease: Integrating Stenosis-, Plaque Quantification, and FFR-CT

Coronary artery disease (CAD) continues to be a leading cause of death globally. Radiological evaluation of CAD generally consists of stenosis detection by cardiac computed tomographic angiography (CCTA). However, this approach can be inefficient and subject to inter-observer variability. In this review we explore the newest developments related to CAD evaluation by CCTA, with an emphasis on plaque quantification. Artificial Intelligence-based quantitative computed tomography (AI-QCT) offers an efficient and reproducible method to analyse plaque biomarkers that support more refined risk-stratification for major adverse cardiovascular events (MACE). Artificial intelligence-based coronary stenosis quantification (AI-CSQ) can streamline workflow and improve consistency. The utilisation of a photon counting detector CT (PCD-CT) has been demonstrated to enhance spatial resolution, thereby allowing more precise coronary lumen analysis and artifact reduction. Fractional flow reserve-computed tomography (FFR-CT) delivers a non-invasive physiologic assessment of stenotic lesions. Finally, we discuss the impact of these changes on risk stratification and guiding preventive therapy as well as possible future directions in this rapidly evolving field.