Huang Jiahao, Ding Weiping, Lv Jun, Yang Jingwen, Dong Hao, Del Ser Javier, Xia Jun, Ren Tiaojuan, Wong Stephen T, Yang Guang
College of Information Science and Technology, Zhejiang Shuren University, 310015 Hangzhou, China.
National Heart and Lung Institute, Imperial College London, London, UK.
Appl Intell (Dordr). 2022;52(13):14693-14710. doi: 10.1007/s10489-021-03092-w. Epub 2022 Jan 28.
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 -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.
在临床医学中，磁共振成像（MRI）是诊断、分诊、预后评估和治疗规划最重要的工具之一。然而，MRI存在固有的数据采集过程缓慢的问题，因为数据是在空间中顺序采集的。近年来，文献中提出的大多数MRI重建方法都侧重于整体图像重建，而不是增强边缘信息。这项工作通过详细阐述边缘信息的增强，偏离了这一总体趋势。具体而言，我们引入了一种新颖的并行成像耦合双判别器生成对抗网络（PIDD-GAN），通过合并多视图信息来进行快速多通道MRI重建。双判别器设计旨在改善MRI重建中的边缘信息。一个判别器用于整体图像重建，而另一个负责增强边缘信息。为生成器提出了一种具有局部和全局残差学习的改进型U-Net。在生成器中嵌入频率通道注意力块（FCA块）以纳入注意力机制。引入内容损失来训练生成器以获得更好的重建质量。我们在卡尔加里-坎皮纳斯公共脑MR数据集上进行了全面实验，并将我们的方法与当前最先进的MRI重建方法进行了比较。在MICCAI13数据集上进行了残差学习的消融研究，以验证所提出的模块。结果表明，我们的PIDD-GAN提供了高质量的重建MR图像，边缘信息得到了很好的保留。单图像重建时间低于5毫秒，满足了更快处理的需求。
