mlsanityData to 7 Oct 2026

High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss

arXiv 2107.09989 ↗First posted 21 Jul 2021eess.IV cs.CV cs.LGcited by 10

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Abstract (arXiv)

Magnetic resonance imaging (MRI) is an important medical imaging modality, but its acquisition speed is quite slow due to the physiological limitations. Recently, super-resolution methods have shown excellent performance in accelerating MRI. In some circumstances, it is difficult to obtain high-resolution images even with prolonged scan time. Therefore, we proposed a novel super-resolution method that uses a generative adversarial network (GAN) with cyclic loss and attention mechanism to generate high-resolution MR images from low-resolution MR images by a factor of 2. We implemented our model on pelvic images from healthy subjects as training and validation data, while those data from patients were used for testing. The MR dataset was obtained using different imaging sequences, including T2, T2W SPAIR, and mDIXON-W. Four methods, i.e., BICUBIC, SRCNN, SRGAN, and EDSR were used for comparison. Structural similarity, peak signal to noise ratio, root mean square error, and variance inflation factor were used as calculation indicators to evaluate the performances of the proposed method. Various experimental results showed that our method can better restore the details of the high-resolution MR image as compared to the other methods. In addition, the reconstructed high-resolution MR image can provide better lesion textures in the tumor patients, which is promising to be used in clinical diagnosis.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 24 of its 54 references that are in the map. First posted on arXiv 21 Jul 2021.

Builds on

24 of 54 references.

References · 24

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12 more
  1. 2017Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Losscited by 82
  2. 2019Deep Learning Methods for Parallel Magnetic Resonance Image Reconstructioncited by 81
  3. 2021Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformerscited by 76
  4. 2019Multi-Contrast Super-Resolution MRI Through a Progressive Networkcited by 45
  5. 2016Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connectionscited by 30
  6. 2017ADMM-Net: A Deep Learning Approach for Compressive Sensing MRIcited by 28
  7. 2019MRI Super-Resolution with Ensemble Learning and Complementary Priorscited by 27
  8. 2020Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studiescited by 24
  9. 2018MRI Reconstruction via Cascaded Channel-wise Attention Networkcited by 20
  10. 2020Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awarenesscited by 5
  11. 2019Rapid Whole-Heart CMR with Single Volume Super-resolutioncited by 4
  12. 2020Semi-Supervised Learning of Mutually Accelerated MRI Synthesis without Fully-Sampled Ground Truthscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 2; 2025: 2; 2026: 2.

10 papers.

  1. 2022Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolutioncited by 30
  2. 2024Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolutioncited by 29
  3. 2022Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRIcited by 14
  4. 2021Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Informationcited by 6
  5. 2022PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRIcited by 5
  6. 2024Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine Imagingcited by 1
  7. 2025A Comprehensive Survey on Magnetic Resonance Image Reconstructioncited by 1
  8. 2025Mamba-Based Modality Disentanglement Network for Multi-Contrast MRI Reconstructioncited by 0
  9. 2026Distributional Deep Learning for Super-Resolution of 4D Flow MRI under Domain Shiftcited by 0
  10. 2026MIAR: Medical Image Super-Resolution With Autoregressive Modelingcited by 0