  Cardiac coronary angiography is a major technology to assist doctors during
cardiac interventional surgeries. Under the exposure of X-ray radiation,
doctors inject contrast agents through catheters to determine the position and
status of coronary vessels in real time. To get a coronary angiography video
with a high frame rate, the doctor needs to increase the exposure frequency and
intensity of the X-ray. This will inevitably increase the X-ray harm to both
patients and surgeons. In this work, we innovatively utilize a deep-learning
based video interpolation algorithm to interpolate coronary angiography videos.
Moreover, we establish a new coronary angiography image dataset ,which contains
95,039 triplets images to retrain the video interpolation network model. Using
the retrained network we synthesize high frame rate coronary angiography video
from the low frame rate coronary angiography video. The average peak signal to
noise ratio(PSNR) of those synthesized video frames reaches 34dB. Extensive
experiment results demonstrate the feasibility of using the video frame
interpolation algorithm to synthesize continuous and clear high frame rate
coronary angiography video. With the help of this technology, doctors can
significantly reduce exposure frequency and intensity of the X-ray during
coronary angiography.
