Imperial College London
Portrait Picture

DrGuangYang

Faculty of Engineering, Department of Bioengineering

Senior Lecturer
 
 
 
//

Contact

 

g.yang Website

 
 
//

Location

 

229Sir Michael Uren HubWhite City Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Huang:2022,
author = {Huang, J and Ding, W and Lv, J and Yang, J and Dong, H and Del, Ser J and Xia, J and Ren, T and Wong, S and Yang, G},
journal = {Applied Intelligence},
pages = {14693--1470},
title = {Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information},
url = {https://link.springer.com/article/10.1007/s10489-021-03092-w},
volume = {52},
year = {2022}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in k-space. In recent years, most MRI reconstruction methods proposed in the literature focus on holistic image reconstruction rather than enhancing the edge information. This work steps aside this general trend by elaborating on the enhancement of edge information. Specifically, we introduce a novel parallel imaging coupled dual discriminator generative adversarial network (PIDD-GAN) for fast multi-channel MRI reconstruction by incorporating multi-view information. The dual discriminator design aims to improve the edge information in MRI reconstruction. One discriminator is used for holistic image reconstruction, whereas the other one is responsible for enhancing edge information. An improved U-Net with local and global residual learning is proposed for the generator. Frequency channel attention blocks (FCA Blocks) are embedded in the generator for incorporating attention mechanisms. Content loss is introduced to train the generator for better reconstruction quality. We performed comprehensive experiments on Calgary-Campinas public brain MR dataset and compared our method with state-of-the-art MRI reconstruction methods. Ablation studies of residual learning were conducted on the MICCAI13 dataset to validate the proposed modules. Results show that our PIDD-GAN provides high-quality reconstructed MR images, with well-preserved edge information. The time of single-image reconstruction is below 5ms, which meets the demand of faster processing.
AU  - Huang,J
AU  - Ding,W
AU  - Lv,J
AU  - Yang,J
AU  - Dong,H
AU  - Del,Ser J
AU  - Xia,J
AU  - Ren,T
AU  - Wong,S
AU  - Yang,G
EP  - 1470
PY  - 2022///
SN  - 0924-669X
SP  - 14693
TI  - Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information
T2  - Applied Intelligence
UR  - https://link.springer.com/article/10.1007/s10489-021-03092-w
UR  - http://hdl.handle.net/10044/1/93257
VL  - 52
ER  -
Download