Basic Principles, Technological Development, Technical Notes, and Interpretation of CT-FFR

This chapter provides a comprehensive overview of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR), a noninvasive technique integrating anatomical and physiological data to assess coronary artery disease. It details the principles of CT-FFR, including computational fluid dynamics (CFD), machine learning (ML), and deep learning (DL) algorithms, and reviews various CT-FFR software (e.g., HeartFlow, uCT-FFR, Siemens) with their workflows, advantages, and limitations. Standardization guidelines emphasize optimal CCTA image quality, measurement protocols (e.g., lesion-specific and vessel-specific CT-FFR at standardized sites), and clinical indications for intermediate stenoses (30–90%). Challenges include variability in software accuracy, image quality dependencies, and limitations in severe calcification or complex lesions. The chapter underscores CT-FFR’s role in enhancing diagnostic precision and guiding treatment, while advocating for further research to address current gaps in clinical validation and standardization.