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Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge

arXiv 2001.02518 ↗First posted 6 Jan 2020eess.IV cs.CVcited by 59

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Abstract (arXiv)

Purpose: To advance research in the field of machine learning for MR image reconstruction with an open challenge. Methods: We provided participants with a dataset of raw k-space data from 1,594 consecutive clinical exams of the knee. The goal of the challenge was to reconstruct images from these data. In order to strike a balance between realistic data and a shallow learning curve for those not already familiar with MR image reconstruction, we ran multiple tracks for multi-coil and single-coil data. We performed a two-stage evaluation based on quantitative image metrics followed by evaluation by a panel of radiologists. The challenge ran from June to December of 2019. Results: We received a total of 33 challenge submissions. All participants chose to submit results from supervised machine learning approaches. Conclusion: The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jan 2020.

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Citing papers per year: 2021: 15; 2022: 17; 2023: 9; 2024: 7; 2025: 7; 2026: 4.

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  1. 2021Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problemcited by 53
  2. 2021Zero-Shot Self-Supervised Learning for MRI Reconstructioncited by 53
  3. 2022Physics-Driven Deep Learning for Computational Magnetic Resonance Imagingcited by 49
  4. 2022SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluationcited by 42
  5. 2021Bayesian Uncertainty Estimation of Learned Variational MRI Reconstructioncited by 30
  6. 2021fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Datacited by 29
  7. 2021Multi-Modal MRI Reconstruction Assisted with Spatial Alignment Networkcited by 16
  8. 2024A study of why we need to reassess full reference image quality assessment with medical imagescited by 16
  9. 2021Universal Undersampled MRI Reconstructioncited by 15
  10. 2023Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstructioncited by 15
  11. 2022Scaling Laws For Deep Learning Based Image Reconstructioncited by 14
  12. 2021End-to-End Sequential Sampling and Reconstruction for MRIcited by 13
38 more
  1. 2022Near-Exact Recovery for Tomographic Inverse Problems via Deep Learningcited by 13
  2. 2023Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Datacited by 10
  3. 2022Image-to-Image MLP-mixer for Image Reconstructioncited by 9
  4. 2021Optimal MRI Undersampling Patterns for Ultimate Benefit of Medical Vision Taskscited by 7
  5. 2024Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Predictioncited by 7
  6. 2025A Trust-Guided Approach to MR Image Reconstruction with Side Informationcited by 6
  7. 2021Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolutioncited by 5
  8. 2022Undersampled MRI Reconstruction with Side Information-Guided Normalisationcited by 5
  9. 2022Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstructioncited by 5
  10. 2022Invertible Sharpening Network for MRI Reconstruction Enhancementcited by 5
  11. 2021On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbationscited by 4
  12. 2021End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image Reconstructioncited by 4
  13. 2022Deep, Deep Learning with BARTcited by 4
  14. 2021AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometrycited by 3
  15. 2021Estimating MRI Image Quality via Image Reconstruction Uncertaintycited by 3
  16. 2021Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructioncited by 3
  17. 2023On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Diseasecited by 3
  18. 2023HyperSLICE: HyperBand optimized Spiral for Low-latency Interactive Cardiac Examinationcited by 3
  19. 2023Towards Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency Regularizationcited by 3
  20. 2024Multi-dynamic deep image prior for cardiac MRIcited by 3
  21. 2022Iterative training of robust k-space interpolation networks for improved image reconstruction with limited scan specific training samplescited by 2
  22. 2022Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformerscited by 2
  23. 2022A Densely Interconnected Network for Deep Learning Accelerated MRIcited by 2
  24. 2025Time-Embedded Algorithm Unrolling for Computational MRIcited by 2
  25. 2021WARPd: A linearly convergent first-order method for inverse problems with approximate sharpness conditionscited by 1
  26. 2022Active Phase-Encode Selection for Slice-Specific Fast MR Scanning Using a Transformer-Based Deep Reinforcement Learning Frameworkcited by 1
  27. 2022Seeking Common Ground While Reserving Differences: Multiple Anatomy Collaborative Framework for Undersampled MRI Reconstructioncited by 1
  28. 2023Computationally Efficient 3D MRI Reconstruction with Adaptive MLPcited by 1
  29. 2023Predicting dynamic, motion-related changes in B0 field in the brain at a 7 T MRI using a subject-specific fine-tuned U-netcited by 1
  30. 2023NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity mapscited by 1
  31. 2024NeRF Solves Undersampled MRI Reconstructioncited by 1
  32. 2024On Instabilities of Unsupervised Denoising Diffusion Models in Magnetic Resonance Imaging Reconstructioncited by 1
  33. 2025JotlasNet: Joint Tensor Low-Rank and Attention-based Sparse Unrolling Network for Accelerating Dynamic MRIcited by 1
  34. 2022Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recoverycited by 0
  35. 2022FReSCO: Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring using deep artifact suppression and segmentationcited by 0
  36. 2022Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selectioncited by 0
  37. 2023The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private datacited by 0
  38. 2024The object detection method aids in image reconstruction evaluation and clinical interpretation of meniscal abnormalitiescited by 0

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