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

arXiv: https://arxiv.org/abs/2107.09989 · HTML: https://mlsanity.com/paper/2107.09989/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 54 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Accelerating the Super-Resolution Convolutional Neural Network](https://mlsanity.com/paper/1608.00367/) (2016)
- [Deeply-Recursive Convolutional Network for Image Super-Resolution](https://mlsanity.com/paper/1511.04491/) (2015)
- [SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning](https://mlsanity.com/paper/1611.05594/) (2016)
- [Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1802.01221/) (2018)
- [Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss](https://mlsanity.com/paper/1709.00753/) (2017)
- [Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/1904.01112/) (2019)
- [Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers](https://mlsanity.com/paper/2105.08059/) (2021)
- [Multi-Contrast Super-Resolution MRI Through a Progressive Network](https://mlsanity.com/paper/1908.01612/) (2019)
- [Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections](https://mlsanity.com/paper/1606.08921/) (2016)
- [ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI](https://mlsanity.com/paper/1705.06869/) (2017)
- [MRI Super-Resolution with Ensemble Learning and Complementary Priors](https://mlsanity.com/paper/1907.03063/) (2019)
- [Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies](https://mlsanity.com/paper/2012.13736/) (2020)
- [MRI Reconstruction via Cascaded Channel-wise Attention Network](https://mlsanity.com/paper/1810.08229/) (2018)
- [Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness](https://mlsanity.com/paper/2006.12915/) (2020)
- [Rapid Whole-Heart CMR with Single Volume Super-resolution](https://mlsanity.com/paper/1912.10503/) (2019)
- [Semi-Supervised Learning of Mutually Accelerated MRI Synthesis without Fully-Sampled Ground Truths](https://mlsanity.com/paper/2011.14347/) (2020)

## Built on by (most cited first, 10 of 10)

- [Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution](https://mlsanity.com/paper/2203.13963/) (2022)
- [Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution](https://mlsanity.com/paper/2404.04785/) (2024)
- [Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI](https://mlsanity.com/paper/2207.02390/) (2022)
- [Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information](https://mlsanity.com/paper/2112.05758/) (2021)
- [PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI](https://mlsanity.com/paper/2204.11189/) (2022)
- [Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine Imaging](https://mlsanity.com/paper/2407.03034/) (2024)
- [A Comprehensive Survey on Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/2503.07097/) (2025)
- [Mamba-Based Modality Disentanglement Network for Multi-Contrast MRI Reconstruction](https://mlsanity.com/paper/2512.19095/) (2025)
- [Distributional Deep Learning for Super-Resolution of 4D Flow MRI under Domain Shift](https://mlsanity.com/paper/2602.15167/) (2026)
- [MIAR: Medical Image Super-Resolution With Autoregressive Modeling](https://mlsanity.com/paper/2609.26103/) (2026)

## 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.