Super-resolution construction of intravascular ultrasound images using generative adversarial networks.
10.12122/j.issn.1673-4254.2019.01.13
- Author:
Yangyang WU
1
;
Feng YANG
1
;
Jing HUANG
1
;
Yaqin LIU
1
Author Information
1. Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
- Publication Type:Journal Article
- Keywords:
generative adversarial network;
intravascular ultrasound;
sub-pixel convolution layer;
super-resolution reconstruction
- MeSH:
Blood Vessels;
diagnostic imaging;
Endosonography;
methods;
Image Enhancement;
methods;
Image Processing, Computer-Assisted;
methods;
Signal-To-Noise Ratio
- From:
Journal of Southern Medical University
2019;39(1):82-87
- CountryChina
- Language:Chinese
-
Abstract:
The low-resolution ultrasound images have poor visual effects. Herein we propose a method for generating clearer intravascular ultrasound images based on super-resolution reconstruction combined with generative adversarial networks. We used the generative adversarial networks to generate the images by a generator and to estimate the authenticity of the images by a discriminator. Specifically, the low-resolution image was passed through the sub-pixel convolution layer -feature channels to generate -feature maps in the same size, followed by realignment of the corresponding pixels in each feature map into × sub-blocks, which corresponded to the sub-block in a high-resolution image; after amplification, an image with a -time resolution was generated. The generative adversarial networks can obtain a clearer image through continuous optimization. We compared the method (SRGAN) with other methods including Bicubic, super-resolution convolutional network (SRCNN) and efficient sub-pixel convolutional network (ESPCN), and the proposed method resulted in obvious improvements in the peak signal-to-noise ratio (PSNR) by 2.369 dB and in structural similarity index by 1.79% to enhance the diagnostic visual effects of intravascular ultrasound images.
