Here at the Center for Advanced Imaging Innovation and Research (CAI2R), in the Department of Radiology at NYU School of Medicine and NYU Langone Health, we bring people together to create new ways of seeing. We are committed to the translation of new imaging techniques and technologies into clinical practice, for the improvement of human health. In particular, we are pushing the boundaries of rapid image acquisition and advanced image reconstruction, with the aim of providing uniquely valuable biomedical information to advance the understanding of disease and improve the care of patients.
We are partnering with Facebook AI Research (FAIR) on fastMRI – a collaborative research project to investigate the use of AI to make MRI scans up to 10X faster.
NYU Langone and FAIR are providing open-source AI models, baselines, and evaluation metrics. For more information, and to participate in open challenges related to accelerated MRI, visit fastMRI.org
The anonymized imaging dataset provided by NYU Langone comprises raw k-space 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. Curation of these datasets are part of an IRB approved study. 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. Raw and DICOM data have been anonymized via conversion to the vendor-neutral ISMRMD format and the RSNA clinical trial processor, respectively. We also performed manual inspection of each DICOM image for the presence of any unexpected protected health information (PHI), with spot checking of both metadata and image content.
Interested scientists may apply for access to fastMRI data for the purposes of internal research or education only. Access is contingent on adherence to the fastMRI Dataset Sharing Agreement shown below, which also outlines policies for publication and citation. Note: This agreement is subject to updates
The application process includes acceptance of the Data Sharing Agreement (found below) and submission of an online application form. The application must include the investigator’s institutional affiliation and the proposed uses of the data. NYU fastMRI data may be used for internal research or educational purposes only as described in the data use agreement and may not be redistributed in any way without prior permission.
Read and agree to the data use agreement below to apply for access.