Removing Unsharpness of Coronary Angiography Moving Images Using Deep Learning

Abstract

Unsharpnesses are likely to occur with a high heart rate in angiography. In this study, U-Net was used to remove unsharpness for the purpose of improving the image quality of x-ray movies in the cardiovascular imaging. Dynamic x-ray images including unsharpness were taken with the moving speed of the metronome at 100, 200 beats/minute （bpm）. Standard deviation（SD）and modulation transfer function（MTF）were measured and used to evaluate the effect of artifact removal. As a result, mean SDs of original images and processed images by U-Net were 4.34 and 0.54, respectively. Similarly, mean cut-off frequencies of MTF of original images and processed images by U-Net were 0.52 mm⁻¹ and 4.6 mm⁻¹, respectively. Since SD was greatly reduced and MTF was greatly improved, U-Net would improve the image quality of improvement cardiovascular dynamic x-ray images.