AI for CCTA-based prediction of FFR

In this project, we design AI methods to predict invasively measured fractional flow reserve measurements  noninvasively through analysis of cardiac CT angiography images. Through collaboration with Pie Medical Imaging BV, in this project we aim to allow better selection of patients who need to undergo invasive coronary catheterization.


Journal Articles


N. Hampe, J.M. Wolterink, S.G.M. van Velzen, T. Leiner, I. Išgum

Machine learning for assessment of coronary artery disease in cardiac CT: a survey Journal Article

Frontiers in Cardiovascular Medicine, 6 (172), 2019.

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