  Invasive coronary angiography (ICA) is the gold standard in Coronary Artery
Disease (CAD) imaging. Detection of the end-diastolic frame (EDF) and, in
general, cardiac phase detection on each temporal frame of a coronary
angiography acquisition is of significant importance for the anatomical and
non-invasive functional assessment of CAD. This task is generally performed via
manual frame selection or semi-automated selection based on simultaneously
acquired ECG signals - thus introducing the requirement of simultaneous ECG
recordings. We evaluate the performance of a purely image based workflow based
on deep neural networks for fully automated cardiac phase and EDF detection on
coronary angiographies. A first deep neural network (DNN), trained to detect
coronary arteries, is employed to preselect a subset of frames in which
coronary arteries are well visible. A second DNN predicts cardiac phase labels
for each frame. Only in the training and evaluation phases for the second DNN,
ECG signals are used to provide ground truth labels for each angiographic
frame. The networks were trained on 17800 coronary angiographies from 3900
patients and evaluated on 27900 coronary angiographies from 6250 patients. No
exclusion criteria related to patient state, previous interventions, or
pathology were formulated. Cardiac phase detection had an accuracy of 97.6%, a
sensitivity of 97.6% and a specificity of 97.5% on the evaluation set. EDF
prediction had a precision of 97.4% and a recall of 96.9%. Several sub-group
analyses were performed, indicating that the cardiac phase detection
performance is largely independent from acquisition angles and the heart rate
of the patient. The average execution time of cardiac phase detection for one
angiographic series was on average less than five seconds on a standard
workstation.
