In a new publication from Cardiovascular Innovations and Applications; DOI https://doi.org/10.15212/CVIA.2021.0011, Xiao-lei Yin, Dong-xue Liang, Lu Wang, Jing Qiu, Zhi-yun Yang, Jian-zeng Dong and Zhao-yuan Ma from Tsinghua University, Beijing, China; Capital Medical University, Beijing, China and The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China analyse coronary angiography video interpolation methods to reduce x-ray exposure frequency based on deep learning.
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, the authors 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.
The authors 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 and applied six popular frame interpolation methods to the dataset to confirm that the video frame interpolation technology can reduce the video frame rate and reduce exposure of physicians to X-rays.