A large-scale dataset of fully-sampled raw musculoskeletal MRI
MosaicMRI is the largest open-source raw musculoskeletal MRI dataset to date, with 2,671 volumes and 80,156 slices.
MosaicMRI extends open raw MRI benchmarks beyond brain and knee with diverse MSK anatomies and protocols.
Fully sampled multi-coil raw measurements suitable for accelerated reconstruction studies.
VarNet baselines demonstrate the usefulness of the dataset for analyzing scaling and cross-anatomy generalization.
Orientation-specific examples grouped by anatomy. Select a category to view axial, coronal, and sagittal samples.
MosaicMRI is designed for learning-based MRI under realistic clinical variability in anatomy, contrast, orientation, and coil configuration.
Data were collected on a 1.5T Siemens Magnetom Avantofit scanner between July 15, 2025 and September 23, 2025. We removed incomplete exams, localizers/planning scans, calibration-only acquisitions, and protocols not suited for slice-based reconstruction.
Remaining scans were visually quality-checked and stored as HDF5 with ISMRMRD-compatible headers and fastMRI-style internal layout.
AX/SAG/COR), coarse contrast, fat-suppression flag, and anatomical category.Splits are patient-disjoint to avoid leakage, with target ratios 70% train, 15% val, and 15% test. Assignment was optimized to balance slice counts while preserving per-anatomy coverage across splits.
| Split | Scans | Patients | Slices |
|---|---|---|---|
| train | 1,873 | 303 | 56,235 |
| val | 398 | 68 | 12,027 |
| test | 400 | 79 | 11,894 |
File organization and baseline usage for reconstruction experiments.
Directory layout (current release statistics):
MosaicMRI/
multicoil_train/ (1,744 files, 2,381.92 GiB)
*.h5
multicoil_val/ (398 files, 579.77 GiB)
*.h5
multicoil_test/ (64 files, 71.58 GiB)
*.h5
anatomy_transfer_challenge/
ankle/ (20 files, 49.40 GiB)
*.h5
contrast_generalization_challenge/
T1_FS/ (17 files, 20.74 GiB)
*.h5multicoil_train, multicoil_val, and multicoil_test are the standard reconstruction splits; multicoil_test contains both 4x and 8x accelerated test inputs.anatomy_transfer_challenge/ankle and contrast_generalization_challenge/T1_FS are challenge-specific evaluation subsets.ismrmrd_header, kspace, and reconstruction_rss.tStudyDescription and tProtocolName).A helper script in the GitHub repository reads these metadata fields and plots one reference slice.
Minimal steps to download a file, apply a mask, and run a baseline reconstruction.
git clone https://github.com/paularguello07/msk_mri_dataset
cd msk_mri_dataset
pip install -r requirements.txtpython demo_recon.py \
--file path/to/sample.h5 \
--mask random \
--acc 8 \
--out out.pngTo obtain access, please submit the request form below. We will contact you with instructions after review.
Evaluate cross-anatomy generalization for accelerated MRI reconstruction (8x). Train on released anatomies and submit results for hidden-ground-truth scoring.
Go to BenchmarkAccess is granted for research use after manual review.
MosaicMRI is released for non-commercial research and method development under the posted license terms.
Metadata is de-identified before release. Users may not attempt participant re-identification.
Please cite the dataset paper if you use MosaicMRI.
@article{mosaicmri_2026,
title = {MosaicMRI: A Diverse Dataset and Benchmark for Raw Musculoskeletal MRI},
author = {Arguello, Paula and Tinaz, Berk and Mohammad, Shahab Sepehri and Soltanolkotabi, Maryam and Soltanolkotabi, Mahdi},
journal = {arXiv},
year = {2026}
}Citation metadata will be updated if publication details change.
University of Utah
University of Southern California (USC)
University of California, Irvine (UCI)