Coronary CT angiography–derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemiaPhilipp L. von Knebel Doeberitz, Christian Tesche, John W. Nance et al.|European Radiology|2018Cited by 98
Prognostic value of CT myocardial perfusion imaging and CT-derived fractional flow reserve for major adverse cardiac events in patients with coronary artery diseaseMarly van Assen, U. Joseph Schoepf, Carlo N. De Cecco et al.|Journal of cardiovascular computed tomography|2019Cited by 66
Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography AngiographyAndreas Fischer, U. Joseph Schoepf, Marwen Eid et al.|Journal of Thoracic Imaging|2020Cited by 52
Impact of Coronary Computerized Tomography Angiography-Derived Plaque Quantification and Machine-Learning Computerized Tomography Fractional Flow Reserve on Adverse Cardiac OutcomePhilipp L. von Knebel Doeberitz, Christian Tesche, Carlo N. De Cecco et al.|The American Journal of Cardiology|2019Cited by 44
Feasibility of extracellular volume quantification using dual-energy CTMarly van Assen, U. Joseph Schoepf, Carlo N. De Cecco et al.|Journal of cardiovascular computed tomography|2018Cited by 40