mlsanityData to 7 Oct 2026

Unsupervised MRI Reconstruction with Generative Adversarial Networks

arXiv 2008.13065 ↗First posted 29 Aug 2020eess.IV cs.LG stat.MLcited by 24

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Abstract (arXiv)

Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications. However, most approaches require large-scale fully-sampled ground truth data for supervised training. Acquiring fully-sampled data is often either difficult or impossible, particularly for dynamic contrast enhancement (DCE), 3D cardiac cine, and 4D flow. We present a deep learning framework for MRI reconstruction without any fully-sampled data using generative adversarial networks. We test the proposed method in two scenarios: retrospectively undersampled fast spin echo knee exams and prospectively undersampled abdominal DCE. The method recovers more anatomical structure compared to conventional methods.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Aug 2020.

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Citing papers per year: 2021: 7; 2022: 6; 2023: 7; 2024: 2; 2025: 2.

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  1. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
  2. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 117
  3. 2022Monarch: Expressive Structured Matrices for Efficient and Accurate Trainingcited by 100
  4. 2021Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformerscited by 76
  5. 2021End-to-end reconstruction meets data-driven regularization for inverse problemscited by 32
  6. 2022A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noisecited by 29
  7. 2021Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancementcited by 17
  8. 2021A review and experimental evaluation of deep learning methods for MRI reconstructioncited by 13
  9. 2023Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstructioncited by 13
  10. 2021Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI Reconstructioncited by 11
  11. 2023Cross-Modal Vertical Federated Learning for MRI Reconstructioncited by 9
  12. 2024Deep Learning for Accelerated and Robust MRI Reconstruction: a Reviewcited by 9
12 more
  1. 2022Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoisingcited by 8
  2. 2022Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstructioncited by 5
  3. 2023K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsetscited by 5
  4. 2021Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructioncited by 3
  5. 2023On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Diseasecited by 3
  6. 2022Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformerscited by 2
  7. 2023Cross-Modal Synthesis of Structural MRI and Functional Connectivity Networks via Conditional ViT-GANscited by 2
  8. 2024Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstructioncited by 1
  9. 2025A Comprehensive Survey on Magnetic Resonance Image Reconstructioncited by 1
  10. 2023Exploring the Power of Generative Deep Learning for Image-to-Image Translation and MRI Reconstruction: A Cross-Domain Reviewcited by 0
  11. 2023Unsupervised Adaptive Implicit Neural Representation Learning for Scan-Specific MRI Reconstructioncited by 0
  12. 2025Dual-domain Multi-path Self-supervised Diffusion Model for Accelerated MRI Reconstructioncited by 0