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SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation

arXiv 2203.06823 ↗First posted 14 Mar 2022eess.IV cs.CVcited by 42

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Abstract (arXiv)

Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However, long image acquisition times, the need for qualitative expert analysis, and the lack of (and difficulty extracting) quantitative indicators that are sensitive to tissue health have curtailed widespread clinical and research studies. While recent machine learning methods for MRI reconstruction and analysis have shown promise for reducing this burden, these techniques are primarily validated with imperfect image quality metrics, which are discordant with clinically-relevant measures that ultimately hamper clinical deployment and clinician trust. To mitigate this challenge, we present the Stanford Knee MRI with Multi-Task Evaluation (SKM-TEA) dataset, a collection of quantitative knee MRI (qMRI) scans that enables end-to-end, clinically-relevant evaluation of MRI reconstruction and analysis tools. This 1.6TB dataset consists of raw-data measurements of ~25,000 slices (155 patients) of anonymized patient MRI scans, the corresponding scanner-generated DICOM images, manual segmentations of four tissues, and bounding box annotations for sixteen clinically relevant pathologies. We provide a framework for using qMRI parameter maps, along with image reconstructions and dense image labels, for measuring the quality of qMRI biomarker estimates extracted from MRI reconstruction, segmentation, and detection techniques. Finally, we use this framework to benchmark state-of-the-art baselines on this dataset. We hope our SKM-TEA dataset and code can enable a broad spectrum of research for modular image reconstruction and image analysis in a clinically informed manner. Dataset access, code, and benchmarks are available at https://github.com/StanfordMIMI/skm-tea.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). Builds on 10 of its 48 references that are in the map. First posted on arXiv 14 Mar 2022.

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10 of 48 references.

References · 10

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  3. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 340
  4. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 201
  5. 2020Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challengecited by 59
  6. 2021Measuring Robustness in Deep Learning Based Compressive Sensingcited by 53
  7. 2017Unrolled Optimization with Deep Priorscited by 40
  8. 2021fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Datacited by 29
  9. 2020The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Datasetcited by 9
  10. 2019Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networkscited by 3

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Citing papers per year: 2022: 5; 2023: 10; 2024: 11; 2025: 11; 2026: 5.

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  1. 2024A Survey on Diffusion Models for Inverse Problemscited by 173
  2. 2022Monarch: Expressive Structured Matrices for Efficient and Accurate Trainingcited by 100
  3. 2024Diffusion Models in Low-Level Vision: A Surveycited by 73
  4. 2023DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Modelscited by 50
  5. 2025nnInteractive: Redefining 3D Promptable Segmentationcited by 39
  6. 2024Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Datacited by 32
  7. 2023MRI Reconstruction with Side Information using Diffusion Modelscited by 15
  8. 2023Uncertainty Estimation and Propagation in Accelerated MRI Reconstructioncited by 10
  9. 2023Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Datacited by 10
  10. 2023FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imagingcited by 9
  11. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9
  12. 2024Segmentation-guided MRI reconstruction for meaningfully diverse reconstructionscited by 6
30 more
  1. 2024Boosting ViT-based MRI Reconstruction from the Perspectives of Frequency Modulation, Spatial Purification, and Scale Diversificationcited by 6
  2. 2023K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsetscited by 5
  3. 2025MedNeXt-v2: Scaling 3D ConvNeXts for Large-Scale Supervised Representation Learning in Medical Image Segmentationcited by 5
  4. 2025Benchmarking Self-Supervised Learning Methods for Accelerated MRI Reconstructioncited by 4
  5. 2023On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Diseasecited by 3
  6. 2023Learning Task-Specific Strategies for Accelerated MRIcited by 3
  7. 2023CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstructioncited by 3
  8. 2024INFusion: Diffusion Regularized Implicit Neural Representations for 2D and 3D accelerated MRI reconstructioncited by 3
  9. 2024MOST: MR reconstruction Optimization for multiple downStream Tasks via continual learningcited by 3
  10. 2025DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI Reconstructioncited by 3
  11. 2022Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformerscited by 2
  12. 2022Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networkscited by 2
  13. 2025Mind the Detail: Uncovering Clinically Relevant Image Details in Accelerated MRI with Semantically Diverse Reconstructionscited by 2
  14. 2025Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applicationscited by 2
  15. 2022Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classificationcited by 1
  16. 2024ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance Imagingcited by 1
  17. 2024Accelerating MRI with Longitudinally-informed Latent Posterior Samplingcited by 1
  18. 2025CUTE-MRI: Conformalized Uncertainty-based framework for Time-adaptivE MRIcited by 1
  19. 2022Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learningcited by 0
  20. 2024Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mambacited by 0
  21. 2024TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature Scalingcited by 0
  22. 2025Efficient Noise Calculation in Deep Learning-based MRI Reconstructionscited by 0
  23. 2025Understanding Benefits and Pitfalls of Current Methods for the Segmentation of Undersampled MRI Datacited by 0
  24. 2025MedVision: Benchmarking Quantitative Medical Image Analysiscited by 0
  25. 2025LM-CartSeg: Automated Segmentation of Lateral and Medial Cartilage and Subchondral Bone for Radiomics Analysiscited by 0
  26. 2026Accelerating Stroke MRI with Diffusion Probabilistic Models through Large-Scale Pre-training and Target-Specific Fine-Tuningcited by 0
  27. 2026Robustness of breast lesion segmentation under MRI undersampling improves with k-space-aware deep learningcited by 0
  28. 2026Self-Auditing Residual Drifting for Pathology-Preserving Accelerated Knee MRIcited by 0
  29. 2026A Systematic Benchmark of Intensity Normalisation Methods for 3D Knee MRI Segmentation and Cross-Domain Generalisabilitycited by 0
  30. 2026KneePreM: Towards 3D Knee MRI Foundation Models via Large-Scale Unlabeled Pretraining and Label-Efficient Fine-Tuningcited by 0