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Invasive coronary angiography (ICA) is the gold standard in Coronary ArteryDisease (CAD) imaging. Detection of the end-diastolic frame (EDF) and, ingeneral, cardiac phase detection on each temporal frame of a coronaryangiography acquisition is of significant importance for the anatomical andnon-invasive functional assessment of CAD. This task is generally performed viamanual frame selection or semi-automated selection based on simultaneouslyacquired ECG signals - thus introducing the requirement of simultaneous ECGrecordings. We evaluate the performance of a purely image based workflow basedon deep neural networks for fully automated cardiac phase and EDF detection oncoronary angiographies. A first deep neural network (DNN), trained to detectcoronary arteries, is employed to preselect a subset of frames in whichcoronary arteries are well visible. A second DNN predicts cardiac phase labelsfor each frame. Only in the training and evaluation phases for the second DNN,ECG signals are used to provide ground truth labels for each angiographicframe. The networks were trained on 17800 coronary angiographies from 3900patients and evaluated on 27900 coronary angiographies from 6250 patients. Noexclusion criteria related to patient state, previous interventions, orpathology were formulated. Cardiac phase detection had an accuracy of 97.6%, asensitivity of 97.6% and a specificity of 97.5% on the evaluation set. EDFprediction had a precision of 97.4% and a recall of 96.9%. Several sub-groupanalyses were performed, indicating that the cardiac phase detectionperformance is largely independent from acquisition angles and the heart rateof the patient. The average execution time of cardiac phase detection for oneangiographic series was on average less than five seconds on a standardworkstation.

Deep Neural Networks for ECG-free Cardiac Phase and End-Diastolic Frame Detection on Coronary Angiographies