Reducing the X-ray radiation exposure frequency in cardio-angiography via deep-learning based video interpolation
Xiao-Lei Yin, Dong-Xue Liang, Lu Wang, Jing Qiu, Zhi-Yun Yang, Jun-Hui, Xing, Jian-Zeng Dong, Zhao-Yuan Ma

TL;DR
This paper presents a deep-learning based video interpolation method to synthesize high frame rate coronary angiography videos from low frame rate videos, significantly reducing X-ray exposure during cardiac surgeries.
Contribution
It introduces a new dataset and a retrained deep-learning model for coronary angiography video interpolation, enabling lower X-ray exposure without compromising video quality.
Findings
Achieved an average PSNR of 34dB in synthesized videos
Demonstrated the feasibility of reducing X-ray exposure in clinical settings
Produced high-quality interpolated videos from low frame rate inputs
Abstract
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…
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Taxonomy
TopicsMedical Imaging Techniques and Applications · Advanced MRI Techniques and Applications · Radiation Dose and Imaging
