The New York University (NYU) School of Medicine's department of radiology is releasing a knee MRI dataset of more than 1.5 million anonymous images as part of its ongoing collaboration with Facebook to make MRI scans 10 times faster with artificial intelligence (AI). The collaboration, known as fastMRI, involves NYU's Center for Advanced Imaging Innovation and Facebook AI Research (FAIR).
The images were compiled using 10,000 MRI scans and “raw measurement data” from nearly 1,600 scans.
“We hope that the release of this landmark dataset, the largest-ever collection of fully-sampled MRI raw data, will provide researchers with the tools necessary to overcome the challenges inherent in accelerating MR imaging,” Michael P. Recht, MD, chair and Louis Marx Professor of Radiology at NYU Langone Health, said in a prepared statement. “This work has the potential to not only help increase access to MR imaging, but also improve patient care worldwide.”
The statement noted the patient data used in the dataset is HIPAA-compliant. Also, no Facebook data of any kind was used in the project. In the future, the radiology department will release datasets that focus on liver and brain imaging.
The researchers believe that fastMRI could benefit various populations, including those who may not be able to stand long scan times and could also decrease the need for sedation. This may also help where there may be a shortage of MRI scanners and increased wait times for patient scans.
"fastMRI not only could have an important impact in the medical field, it's also an interesting research challenge that will help to advance the field of AI," Larry Zitnick, research manager at Facebook AI Research, said in the same statement. "To be medically useful, our AI-reconstructed images need to be more than just good-looking, they must also be accurate representations of the ground-truth, even though they're created from significantly less data."