Results of the 2020 fastMRI challenge for machine learning MR image reconstruction
Open accessAbstract
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans (de-identified via a HIPAA-compliant procedure by NYU Langone Health), holding back the fully-sampled data from 894 of these scans for challenge evaluation purposes. In contrast to the 2019 challenge, we focused our radiologist evaluations on pathological assessment in brain images. We also debuted a new Transfer track that required participants to submit models evaluated on MRI scanners from outside the training set. We received 19 submissions from eight different groups. Results showed one team scoring best in both SSIM scores and qualitative radiologist evaluations. We also performed analysis on alternative metrics to mitigate the effects of background noise and collected feedback from the participants to inform future challenges. Lastly, we identify common failure modes across the submissions, highlighting areas of need for future research in the MRI reconstruction community.
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fastMRI is a large-scale dataset for medical image reconstruction using machine learning, containing more than 1500 knee MRIs and DICOM images from 10,000 clinical knee MRIs.
“The fastMRI [117,118] 15 is a large-scale dataset for medical image reconstruction using machine learning approaches.”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [118]
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Measuring Robustness in Deep Learning Based Compressive SensingPreprint
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Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis
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Multimodal MRI Reconstruction Assisted With Spatial Alignment Network
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DCT-net: Dual-domain cross-fusion transformer network for MRI reconstruction
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Self-citationA Study of Why We Need to Reassess Full Reference Image Quality Assessment with Medical Images
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Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization
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Intrafraction Motion Management With MR-Guided Radiation Therapy
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Will AI Replace Physicians in the Near Future? AI Adoption Barriers in Medicine
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K2S Challenge: From Undersampled K-Space to Automatic Segmentation
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Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning
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Stochastic optimization of three‐dimensional non‐Cartesian sampling trajectory
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Self-citationDIRECT: Deep Image REConstruction Toolkit
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Self-citationComplexities of deep learning-based undersampled MR image reconstruction
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Deep Learning Techniques for Prostate Cancer Analysis and Detection: Survey of the State of the Art
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Physics-Informed Compressed Sensing for PC-MRI: An Inverse Navier-Stokes Problem
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The scientific evidence of commercial AI products for MRI acceleration: a systematic review
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A critical assessment of artificial intelligence in magnetic resonance imaging of cancer
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Beyond the Conventional Structural MRI
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Self-citationvSHARP: Variable Splitting Half-quadratic ADMM algorithm for reconstruction of inverse-problems
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Self-citationUnsupervised reconstruction of accelerated cardiac cine MRI using neural fields
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Self-citationFederated End-to-End Unrolled Models for Magnetic Resonance Image Reconstruction
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Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI
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Deep learning image reconstruction: a tremendous advance for clinical MRI but be careful…
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Learning the sampling density in 2D SPARKLING MRI acquisition for optimized image reconstruction
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Self-citationKleineisel, J., Heidenreich, J. F., Eirich, P., Petri, N., Köstler, H., Petritsch, B., Bley, T. A., & Wech, T. (2022) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29357 · Cited by 15 · see citing works
Adversarial Robustness of MR Image Reconstruction Under Realistic Perturbations
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Ensemble-based eye disease detection system utilizing fundus and vascular structures
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Self-citationZhang, C., Karkalousos, D., Bazin, P., Coolen, B. F., Vrenken, H., Sonke, J., Forstmann, B. U., Poot, D. H. J., & Caan, M. W. A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119680 · Cited by 14 · see citing works
Self-citationThe Current Status of AI-accelerated MRI Techniques in Clinical Use
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Self-citationKarkalousos, D., Noteboom, S., Hulst, H. E., Vos, F. M., & Caan, M. W. A. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac6cc2 · Cited by 13 · see citing works
Self-citationWave-Encoded Model-Based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction
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Hallucinations in medical devices
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Accelerated MRI reconstructions via variational network and feature domain learning
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Bayesian inverse Navier–Stokes problems: joint flow field reconstruction and parameter learning
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Deep learning-based denoising image reconstruction of body magnetic resonance imaging in children
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End‐to‐end memory‐efficient reconstruction for cone beam CT
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Self-citationA densely interconnected network for deep learning accelerated MRI
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Deep Learning-Based Meniscus Tear Detection From Accelerated MRI
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Self‐supervised learning for improved calibrationless radial MRI with NLINV‐Net
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CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstruction
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Training Adaptive Reconstruction Networks for Blind Inverse Problems
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NESTANets: stable, accurate and efficient neural networks for analysis-sparse inverse problems
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- Authors
- Muckley, M. J., Riemenschneider, B., Radmanesh, A., Kim, S., Jeong, G., Ko, J., Jun, Y., Shin, H., Hwang, D., Mostapha, M., Arberet, S., Nickel, D., Ramzi, Z., Ciuciu, P., Starck, J.-L., Teuwen, J., Karkalousos, D., Zhang, C., Sriram, A., Huang, Z., Yakubova, N., Lui, Y. W., & Knoll, F.
- Affiliations
- Meta (United States) (US)
- NYU Langone Health (US)
- Yonsei University (KR)
- Siemens Healthcare (United States) (US)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Radboud University Nijmegen (NL)
- Amsterdam University Medical Centers (NL)
- ORCID
- Muckley, Matthew J.
- Riemenschneider, Bruno
- Radmanesh, Alireza
- Kim, Sunwoo
- Jeong, Geunu
- Jun, Yohan
- Shin, Hyungseob
- Hwang, Dosik
- Mostapha, Mahmoud
- Arberet, Simon
- Nickel, Dominik
- Ramzi, Zaccharie
- Ciuciu, Philippe
- Starck, Jean-Luc
- Teuwen, Jonas
- Karkalousos, Dimitrios
- Zhang, Chaoping
- Sriram, Anuroop
- Lui, Yvonne W.
- Knoll, Florian
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2021-09
- Volume
- 40
- Issue
- 9
- Pages
- 2306-2317
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- PMC
- PMC8428775
- OpenAlex
- W3157723514
- Cited by
- 313
- References
- 80
- Citations by year
- 2026: 40 · 2025: 76 · 2024: 76 · 2023: 56 · 2022: 48 · 2021: 15 · 2020: 1 · 2012: 1
- FWCI
- 25.73
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (acceptedVersion, cc-by)
- PubMed Central (acceptedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- NIH (R01EB024532, P41EB017183, R21EB027241)
- NIH funding was awarded to NYU
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- MRI reconstruction, accelerated MRI, brain MRI, radiologist evaluation, transfer learning, failure modes, background noise
- MeSH terms
- Machine Learning, Brain, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Neuroimaging
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