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Cardiac coronary angiography is a major technique that assists physicians during interventional heart surgery. Under X-ray irradiation, the physician injects a contrast agent through a catheter and determines the coronary arteries’ state in real time. However, to obtain a more accurate state of the coronary arteries, physicians need to increase the frequency and intensity of X-ray exposure, which will inevitably increase the potential for harm to both the patient and the surgeon. In the work reported here, we use advanced deep learning algorithms to find a method of frame interpolation for coronary angiography videos that reduces the frequency of X-ray exposure by reducing the frame rate of the coronary angiography video, thereby reducing X-ray-induced damage to physicians. We established a new coronary angiography image group dataset containing 95,039 groups of images extracted from 31 videos. Each group includes three consecutive images, which are used to train the video interpolation network model. We apply six popular frame interpolation methods to this dataset to confirm that the video frame interpolation technology can reduce the video frame rate and reduce exposure of physicians to X-rays.

Analysis of Coronary Angiography Video Interpolation Methods to Reduce X-ray Exposure Frequency Based on Deep Learning