Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
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.
Date Issued
2022-10-01
Date Acceptance
2021-12-10
Citation
Applied Intelligence, 2022, 52, pp.14693-1470
ISSN
0924-669X
Publisher
Springer
Start Page
14693
End Page
1470
Journal / Book Title
Applied Intelligence
Volume
52
Copyright Statement
© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Sponsor
British Heart Foundation
European Research Council Horizon 2020
Commission of the European Communities
Innovative Medicines Initiative
Boehringer Ingelheim Ltd
Medical Research Council (MRC)
Medical Research Council (MRC)
Identifier
https://link.springer.com/article/10.1007/s10489-021-03092-w
Grant Number
PG/16/78/32402
H2020-SC1-FA-DTS-2019-1 952172
101005122
101005122
PO:4700244755 Study:1199-0457
MR/V023799/1
MC_PC_21013
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
Fast MRI
Parallel imaging
Multi-view learning
Generative adversarial networks
Edge enhancement
RECONSTRUCTION
ATTENTION
SENSE
Edge enhancement
Fast MRI
Generative adversarial networks
Multi-view learning
Parallel imaging
eess.IV
eess.IV
cs.AI
cs.CV
cs.LG
Artificial Intelligence & Image Processing
0801 Artificial Intelligence and Image Processing
Publication Status
Published
Date Publish Online
2022-01-28