mlsanityData to 7 Oct 2026

Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss

arXiv 1709.00753 ↗First posted 3 Sep 2017cs.CVcited by 82

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Abstract (arXiv)

Compressed Sensing MRI (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and image processing, but their adaptation to MRI reconstruction is still in an early stage. In this paper, we propose a novel deep learning-based generative adversarial model, RefineGAN, for fast and accurate CS-MRI reconstruction. The proposed model is a variant of fully-residual convolutional autoencoder and generative adversarial networks (GANs), specifically designed for CS-MRI formulation; it employs deeper generator and discriminator networks with cyclic data consistency loss for faithful interpolation in the given under-sampled k-space data. In addition, our solution leverages a chained network to further enhance the reconstruction quality. RefineGAN is fast and accurate -- the reconstruction process is extremely rapid, as low as tens of milliseconds for reconstruction of a 256x256 image, because it is one-way deployment on a feed-forward network, and the image quality is superior even for extremely low sampling rate (as low as 10%) due to the data-driven nature of the method. We demonstrate that RefineGAN outperforms the state-of-the-art CS-MRI methods by a large margin in terms of both running time and image quality via evaluation using several open-source MRI databases.

Key facts. Cited by 82 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2017.

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Citing papers per year: 2021: 25; 2022: 24; 2023: 10; 2024: 12; 2025: 8; 2026: 3.

82 papers.

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  1. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 117
  2. 2025InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciencescited by 67
  3. 2022Swin Transformer for Fast MRIcited by 56
  4. 2021Multi-Modal Transformer for Accelerated MR Imagingcited by 42
  5. 2021AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysiscited by 38
  6. 2022A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noisecited by 29
  7. 2021SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networkscited by 18
  8. 2021End-to-End Sequential Sampling and Reconstruction for MRIcited by 13
  9. 2021A review and experimental evaluation of deep learning methods for MRI reconstructioncited by 13
  10. 2023Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstructioncited by 13
  11. 2021Dual-Octave Convolution for Accelerated Parallel MR Image Reconstructioncited by 12
  12. 2021Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI Reconstructioncited by 11
38 more
  1. 2022Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networkscited by 11
  2. 2021High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Losscited by 10
  3. 2021An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivitiescited by 10
  4. 2021DONet: Dual-Octave Network for Fast MR Image Reconstructioncited by 9
  5. 2021MRI Reconstruction Using Deep Energy-Based Modelcited by 8
  6. 2022Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoisingcited by 8
  7. 2022Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Imagescited by 8
  8. 2021FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolutioncited by 7
  9. 2021A Multi-attribute Controllable Generative Model for Histopathology Image Synthesiscited by 7
  10. 2023Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detectioncited by 7
  11. 2023A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensingcited by 7
  12. 2021High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Losscited by 6
  13. 2021Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Informationcited by 6
  14. 2022A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstructioncited by 6
  15. 2021Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyondcited by 5
  16. 2022PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRIcited by 5
  17. 2022An Interpretable MRI Reconstruction Network with Two-grid-cycle Correction and Geometric Prior Distillationcited by 5
  18. 2022Invertible Sharpening Network for MRI Reconstruction Enhancementcited by 5
  19. 2023Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstructioncited by 5
  20. 2024X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Modelscited by 5
  21. 2021Model Adaptation for Image Reconstruction using Generalized Stein's Unbiased Risk Estimatorcited by 4
  22. 2022A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjectscited by 4
  23. 2022Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariancecited by 4
  24. 2024NPB-REC: A Non-parametric Bayesian Deep-learning Approach for Undersampled MRI Reconstruction with Uncertainty Estimationcited by 4
  25. 2024MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)cited by 4
  26. 2021Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectivescited by 3
  27. 2021Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructioncited by 3
  28. 2022Investigating the limited performance of a deep-learning-based SPECT denoising approach: An observer-study-based characterizationcited by 3
  29. 2022Learning Apparent Diffusion Coefficient Maps from Accelerated Radial k-Space Diffusion-Weighted MRI in Mice using a Deep CNN-Transformer Modelcited by 3
  30. 2022MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolationcited by 3
  31. 2022JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRIcited by 3
  32. 2023Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRIcited by 3
  33. 2023PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classificationcited by 3
  34. 2023Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECTcited by 3
  35. 2024MRPD: Undersampled MRI reconstruction by prompting a large latent diffusion modelcited by 3
  36. 2024Score-based Generative Priors Guided Model-driven Network for MRI Reconstructioncited by 3
  37. 2024A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learningcited by 3
  38. 2021Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learningcited by 2

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