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Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information

arXiv 2112.05758 ↗First posted 10 Dec 2021eess.IV cs.AI cs.CVcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 27 of its 74 references that are in the map. First posted on arXiv 10 Dec 2021.

Builds on

27 of 74 references.

References · 27

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15 more
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Cited by year

Citing papers per year: 2022: 3; 2023: 3.

6 papers.

  1. 2022Swin Transformer for Fast MRIcited by 56
  2. 2022Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRIcited by 14
  3. 2022Fast MRI Reconstruction: How Powerful Transformers Are?cited by 7
  4. 2023ViGU: Vision GNN U-Net for Fast MRIcited by 4
  5. 2023Fast MRI Reconstruction via Edge Attentioncited by 3
  6. 2023A Practical Framework for Unsupervised Structure Preservation Medical Image Enhancementcited by 2