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fastMRI

Introduced by Jure Zbontar et al. in fastMRI: An Open Dataset and Benchmarks for Accelerated MRI archive 2025-07-28

The fastMRI dataset includes two types of MRI scans: knee MRIs and the brain (neuro) MRIs, and containing training, validation, and masked test sets. The deidentified imaging dataset provided by NYU Langone comprises raw k-space data in several sub-dataset groups. Curation of these data are part of an IRB approved study. Raw and DICOM data have been deidentified via conversion to the vendor-neutral ISMRMD format and the RSNA clinical trial processor, respectively. Also, each DICOM image is manually inspected for the presence of any unexpected protected health information (PHI), with spot checking of both metadata and image content. Knee MRI: Data from more than 1,500 fully sampled knee MRIs obtained on 3 and 1.5 Tesla magnets and DICOM images from 10,000 clinical knee MRIs also obtained at 3 or 1.5 Tesla. The raw dataset includes coronal proton density-weighted images with and without fat suppression. The DICOM dataset contains coronal proton density-weighted with and without fat suppression, axial proton density-weighted with fat suppression, sagittal proton density, and sagittal T2-weighted with fat suppression. Brain MRI: Data from 6,970 fully sampled brain MRIs obtained on 3 and 1.5 Tesla magnets. The raw dataset includes axial T1 weighted, T2 weighted and FLAIR images. Some of the T1 weighted acquisitions included admissions of contrast agent.

Source: https://fastmri.med.nyu.edu/ Image Source: https://fastmri.med.nyu.edu/

Benchmarks archive 2025-07-28

All 5 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
MRI Reconstruction fastMRI Knee 8x HUMUS-Net (train+val data) SSIM 0.8945 HUMUS-Net: Hybrid unrolled multi-scale network... z-fabian/HUMUS-Net +1 4 Compare
MRI Reconstruction fastMRI Knee Val 8x PromptMR SSIM 0.8983 Fill the K-Space and Refine the Image: Prompting for... hellopipu/promptmr 4 Compare
MRI Reconstruction fastMRI Brain 4x End-to-end variational network SSIM 0.959 End-to-End Variational Networks for Accelerated MRI... facebookresearch/fastMRI +2 2 Compare
MRI Reconstruction fastMRI Brain 8x End-to-end variational network SSIM 0.943 End-to-End Variational Networks for Accelerated MRI... facebookresearch/fastMRI +2 2 Compare
MRI Reconstruction fastMRI Knee 4x End-to-end variational network SSIM 0.930 End-to-End Variational Networks for Accelerated MRI... facebookresearch/fastMRI +2 2 Compare

Papers archive 2025-07-28

4 shown of 4 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 332. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction 1 1 25 Sep 2023 not harvested
HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction 2 4 15 Mar 2022 ran 5 of 5 samples (0 unverified)
XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge 3 4 15 Oct 2020 ran 1 of 1 samples (0 unverified)
End-to-End Variational Networks for Accelerated MRI Reconstruction 3 5 14 Apr 2020 ran 1 of 5 samples (4 unverified)

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

Custom (internal research-only)

Modalities archive 2025-07-28

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • fastMRI
  • fastMRI Knee 4x
  • fastMRI Knee 8x
  • fastMRI Brain 4x
  • fastMRI Brain 8x
  • fastMRI Knee Val 8x

6 variant names, as the archive lists them.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections