The fastMRI team built a neural network and trained it using NYU Langone Health’s open source dataset of knee MRIs, with NYU Langone ensuring that all scans were de-identified. By removing three-fourths of the raw data in each scan and then feeding the remaining information into the AI model, the model learned to generate complete images from the limited data. These images produced by the AI model didn’t just look like generic MRIs, but matched the ground truth image created by the standard slow MRI process. Radiologists and clinicians would be able to use these AI-generated images as they would traditional images. The only difference would be that the patient spent less time in the machine.

In a retrospective clinical study published in the American Journal of Roentgenology, radiologists reviewed both traditional MRIs and images generated with the AI model using 75 percent less raw data. The radiologists produced the same diagnoses with both. They also could not tell which images were created using the AI model. The study indicates that fastMRI images are interchangeable with those of regular MRIs, producing images that are just as accurate, useful, and reliable as those from a standard MRI.

Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J. Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzal, Adriana Romero, Michael Rabbat, Pascal Vincent, Nafissa Yakubova, James Pinkerton, Duo Wang, Erich Owens, C. Lawrence Zitnick, Michael P. Recht, Daniel K. Sodickson, Yvonne W. Lui