Coronary artery disease (CAD) remains the first cause of morbidity and mortality in the Western world and it is expected that this trend will continue in the coming years. In clinical routine, patients with CAD are increasingly identified using non-invasive coronary CT angiography (CCTA), a non-invasive imaging tool for detection and exclusion of the obstructive coronary artery stenosis. Despite its high sensitivity, CCTA is currently not capable of determining the functional significance of the detected stenosis. Therefore, after undergoing CCTA, many patients undergo invasive coronary angiography (ICA). In this project, we aim to allow better selection of patients who need to undergo invasive coronary catheterization and to design AI methods to predict invasively measured fractional flow reserve measurements noninvasively through analysis of cardiac CT angiography images. The project is funded by Pie Medical Imaging.