mlsanityData to 7 Oct 2026

fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

arXiv 1811.08839 ↗First posted 21 Nov 2018cs.CV cs.LG eess.SPcited by 464

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Cited by 90 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measurements and clinical MR images, that can be used for training and evaluation of machine-learning approaches to MR image reconstruction. By introducing standardized evaluation criteria and a freely-accessible dataset, our goal is to help the community make rapid advances in the state of the art for MR image reconstruction. We also provide a self-contained introduction to MRI for machine learning researchers with no medical imaging background.

Key facts. Cited by 464 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Nov 2018.

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Citing papers per year: 2021: 54; 2022: 77; 2023: 86; 2024: 90; 2025: 86; 2026: 71.

464 papers.

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  1. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 514
  2. 2021Score-based diffusion models for accelerated MRIcited by 308
  3. 2021Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contractioncited by 306
  4. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
  5. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  6. 2022Transformers in Medical Imaging: A Surveycited by 245
  7. 2023A Variational Perspective on Solving Inverse Problems with Diffusion Modelscited by 217
  8. 2024A Survey on Diffusion Models for Inverse Problemscited by 173
  9. 2024The Road Less Scheduledcited by 158
  10. 2024Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealingcited by 157
  11. 2022Solving 3D Inverse Problems using Pre-trained 2D Diffusion Modelscited by 130
  12. 2023Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problemscited by 129
38 more
  1. 2023Prodigy: An Expeditiously Adaptive Parameter-Free Learnercited by 129
  2. 2021Deep Equilibrium Architectures for Inverse Problems in Imagingcited by 126
  3. 2023Learning-Rate-Free Learning by D-Adaptationcited by 113
  4. 2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Surveycited by 108
  5. 2022Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imagingcited by 107
  6. 2021MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluationcited by 88
  7. 2022COIN++: Neural Compression Across Modalitiescited by 81
  8. 2024Diffusion Models in Low-Level Vision: A Surveycited by 73
  9. 2025InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciencescited by 67
  10. 2022MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusioncited by 66
  11. 2022Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstructioncited by 64
  12. 2024Learning Diffusion Priors from Observations by Expectation Maximizationcited by 60
  13. 2022Towards performant and reliable undersampled MR reconstruction via diffusion model samplingcited by 59
  14. 2023A Review of Uncertainty Estimation and its Application in Medical Imagingcited by 59
  15. 2023Zero-Shot Noise2Noise: Efficient Image Denoising without any Datacited by 59
  16. 2021Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurementscited by 56
  17. 2022Swin Transformer for Fast MRIcited by 56
  18. 2023Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligencecited by 52
  19. 2023Provable Probabilistic Imaging using Score-Based Generative Priorscited by 51
  20. 2022Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Modelscited by 46
  21. 2021Early Stopping for Deep Image Priorcited by 44
  22. 2021Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration Networkcited by 42
  23. 2021Multi-Modal Transformer for Accelerated MR Imagingcited by 42
  24. 2022High-Frequency Space Diffusion Models for Accelerated MRIcited by 41
  25. 2023Optimal Linear Decay Learning Rate Schedules and Further Refinementscited by 41
  26. 2023Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Datacited by 40
  27. 2023GSURE-Based Diffusion Model Training with Corrupted Datacited by 40
  28. 2021AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysiscited by 38
  29. 20243D MedDiffusion: A 3D Medical Latent Diffusion Model for Controllable and High-quality Medical Image Generationcited by 37
  30. 2021Specificity-Preserving Federated Learning for MR Image Reconstructioncited by 35
  31. 2021Deep reinforcement learning in medical imaging: A literature reviewcited by 34
  32. 2021Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstructioncited by 34
  33. 2022Locality Guidance for Improving Vision Transformers on Tiny Datasetscited by 33
  34. 2025Guided Diffusion Sampling on Function Spaces with Applications to PDEscited by 33
  35. 2022DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstructioncited by 30
  36. 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructioncited by 30
  37. 2022Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolutioncited by 30
  38. 2021Equivariant neural networks for inverse problemscited by 29

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