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Cardiac coronary angiography is a major technology to assist doctors duringcardiac interventional surgeries. Under the exposure of X-ray radiation,doctors inject contrast agents through catheters to determine the position andstatus of coronary vessels in real time. To get a coronary angiography videowith a high frame rate, the doctor needs to increase the exposure frequency andintensity of the X-ray. This will inevitably increase the X-ray harm to bothpatients and surgeons. In this work, we innovatively utilize a deep-learningbased video interpolation algorithm to interpolate coronary angiography videos.Moreover, we establish a new coronary angiography image dataset ,which contains95,039 triplets images to retrain the video interpolation network model. Usingthe retrained network we synthesize high frame rate coronary angiography videofrom the low frame rate coronary angiography video. The average peak signal tonoise ratio(PSNR) of those synthesized video frames reaches 34dB. Extensiveexperiment results demonstrate the feasibility of using the video frameinterpolation algorithm to synthesize continuous and clear high frame ratecoronary angiography video. With the help of this technology, doctors cansignificantly reduce exposure frequency and intensity of the X-ray duringcoronary angiography.

Reducing the X-ray radiation exposure frequency in cardio-angiography via deep-learning based video interpolation