  Dynamic reconstructions (3D+T) of coronary arteries could give important
perfusion details to clinicians. Temporal matching of the different views,
which may not be acquired simultaneously, is a prerequisite for an accurate
stereo-matching of the coronary segments. In this paper, we show how a neural
network can be trained from angiographic sequences to synchronize different
views during the cardiac cycle using raw x-ray angiography videos exclusively.
First, we train a neural network model with angiographic sequences to extract
features describing the progression of the cardiac cycle. Then, we compute the
distance between the feature vectors of every frame from the first view with
those from the second view to generate distance maps that display stripe
patterns. Using pathfinding, we extract the best temporally coherent
associations between each frame of both videos. Finally, we compare the
synchronized frames of an evaluation set with the ECG signals to show an
alignment with 96.04% accuracy.
