论文标题
通过基于深度学习的视频插值来降低心血管造影的X射线辐射暴露频率
Reducing the X-ray radiation exposure frequency in cardio-angiography via deep-learning based video interpolation
论文作者
论文摘要
心脏冠状动脉血管造影是在心脏介入手术中为医生提供帮助的主要技术。在X射线辐射的暴露下，医生通过导管注入对比剂，以实时确定冠状动脉血管的位置和状态。为了获得高框架速率的冠状动脉造影视频，医生需要增加X射线的暴露频率和强度。这不可避免地会增加对患者和外科医生的X射线危害。在这项工作中，我们创新地利用了基于深度学习的视频插值算法来插值冠状动脉造影视频。此外，我们建立了一个新的冠状动脉血管造影图像数据集，其中包含95,039个三重态图像来重新审阅视频插值网络模型。使用重新训练网络，我们从低帧速率冠状动脉造影视频中合成高帧速率冠状动脉造影视频。这些合成视频帧的平均峰信号与噪声比（PSNR）达到34DB。广泛的实验结果表明，使用视频框架插值算法合成连续且清晰的高框架冠状动脉造影视频的可行性。借助这项技术，医生可以显着降低冠状动脉血管造影期间X射线的暴露频率和强度。
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.
