학술논문

Removing Unsharpness of Coronary Angiography Moving Images Using Deep Learning / 深層学習を用いた心臓血管撮影動画像における冠動脈の動きによる不鋭の除去
Document Type
Journal Article
Source
医用画像情報学会雑誌 / Medical Imaging and Information Sciences. 2019, 36(2):98
Subject
artifact reduction
coronary angiography
deep convolutional neural network
image quality
unsharpness
Language
Japanese
ISSN
0910-1543
1880-4977
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.