mlsanityData to 7 Oct 2026

End-to-End Variational Networks for Accelerated MRI Reconstruction

arXiv 2004.06688 ↗First posted 14 Apr 2020eess.IV cs.CVcited by 201

Not on the map. Why?

Cited by 26 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing). While the combination of these methods has the potential to allow much faster scan times, reconstruction from such undersampled multi-coil data has remained an open problem. In this paper, we present a new approach to this problem that extends previously proposed variational methods by learning fully end-to-end. Our method obtains new state-of-the-art results on the fastMRI dataset for both brain and knee MRIs.

Key facts. Cited by 201 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Apr 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 20; 2022: 31; 2023: 44; 2024: 42; 2025: 30; 2026: 34.

201 papers.

Sort:

By area:

  1. 2021Score-based diffusion models for accelerated MRIcited by 308
  2. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
  3. 2022Transformers in Medical Imaging: A Surveycited by 245
  4. 2024The Road Less Scheduledcited by 158
  5. 2023Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problemscited by 129
  6. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 117
  7. 2023Learning-Rate-Free Learning by D-Adaptationcited by 113
  8. 2021Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformerscited by 76
  9. 2022ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformercited by 52
  10. 2023Provable Probabilistic Imaging using Score-Based Generative Priorscited by 51
  11. 2022Physics-Driven Deep Learning for Computational Magnetic Resonance Imagingcited by 49
  12. 2022SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluationcited by 42
38 more
  1. 2022High-Frequency Space Diffusion Models for Accelerated MRIcited by 41
  2. 2023Optimal Linear Decay Learning Rate Schedules and Further Refinementscited by 41
  3. 2022Imaging with Equivariant Deep Learningcited by 35
  4. 2021Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstructioncited by 34
  5. 2021Bayesian Uncertainty Estimation of Learned Variational MRI Reconstructioncited by 30
  6. 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructioncited by 30
  7. 2021Over-and-Under Complete Convolutional RNN for MRI Reconstructioncited by 29
  8. 2022A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noisecited by 29
  9. 2021Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimizationcited by 27
  10. 2024SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse Problemscited by 23
  11. 2023IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRIcited by 22
  12. 2022Stable Deep MRI Reconstruction using Generative Priorscited by 21
  13. 2021Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimizationcited by 19
  14. 2021Data augmentation for deep learning based accelerated MRI reconstruction with limited datacited by 19
  15. 2021SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networkscited by 18
  16. 2023Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstructioncited by 17
  17. 2021Multi-Modal MRI Reconstruction Assisted with Spatial Alignment Networkcited by 16
  18. 2022A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesiscited by 16
  19. 2024A study of why we need to reassess full reference image quality assessment with medical imagescited by 16
  20. 2021Universal Undersampled MRI Reconstructioncited by 15
  21. 2022On learning adaptive acquisition policies for undersampled multi-coil MRI reconstructioncited by 14
  22. 2022Scaling Laws For Deep Learning Based Image Reconstructioncited by 14
  23. 2023One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstructioncited by 14
  24. 2023Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstructioncited by 14
  25. 2021End-to-End Sequential Sampling and Reconstruction for MRIcited by 13
  26. 2021A review and experimental evaluation of deep learning methods for MRI reconstructioncited by 13
  27. 2022Near-Exact Recovery for Tomographic Inverse Problems via Deep Learningcited by 13
  28. 2023Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstructioncited by 13
  29. 2024Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRIcited by 13
  30. 2021Deep Bayesian inference for seismic imaging with taskscited by 12
  31. 2023On Retrospective k-space Subsampling schemes For Deep MRI Reconstructioncited by 12
  32. 2022Adversarial Robustness of MR Image Reconstruction under Realistic Perturbationscited by 11
  33. 2022One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstructioncited by 11
  34. 2023A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imagingcited by 10
  35. 2023Uncertainty Estimation and Propagation in Accelerated MRI Reconstructioncited by 10
  36. 2023Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Datacited by 10
  37. 2024A study on the adequacy of common IQA measures for medical imagescited by 10
  38. 2022Image-to-Image MLP-mixer for Image Reconstructioncited by 9

50 most cited of 201 All 201 (JSON)