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upstart writes in with an IRC submission:

Facebook AI produces accurate MRI images 4 times faster than current tech:

Two years ago a team of radiologists from the New York University Grossman School of Medicine joined forces with Facebook's Artificial Intelligence Research (FAIR) group to try and develop a neural network that can produce effective MRI scans from as little data as possible.

[...] The collaborative project, called fastMRI, produced an AI model that can generate detailed MRI images from a quarter of the data traditionally needed. However, as outlined in a blog post penned by the Facebook AI team, creating accurate MRI images was only the first step for the researchers.

"Generating an accurate image isn't the only challenge," the Facebook team writes. "The AI model must also create images that are visually indistinguishable from traditional MRI images. Radiologists spend many hours carefully analyzing these images and an unfamiliar look and feel could make radiologists less likely to adopt fastMRI in their practices."

[...] "This study is an important step toward clinical acceptance and utilization of AI-accelerated MRI scans because it demonstrates for the first time that AI-generated images are essentially indistinguishable in appearance from standard clinical MRI exams and are interchangeable in regards to diagnostic accuracy," says lead author on the new study, Michael Recht. "This marks an exciting paradigm shift in how we are able to improve the patient experience and create images."

The results produced by the fastMRI project are open source, so the research team is hoping MRI hardware vendors can begin rapidly incorporating the new algorithms into their products. The innovation should also be easily incorporated over the next few years into pre-existing MRI hardware currently in hospitals, making patient experiences more comfortable while expanding MRI access to a greater number of people.

Journal Reference:
Michael P. Recht, MD, et. al., Using Deep Learning to Accelerate Knee MRI at 3T: Results of an Interchangeability Study, American Journal of Roentgenology (DOI: 10.2214/AJR.20.23313)

Original Submission