Getting an MRI means being in a noisy, claustrophobia-inducing tube. For many, that’s no fun. For others—like children or the very unwell—it’s worse. So to make these diagnostic tools run even faster, researchers are exploring incorporating a new tactic: using artificial intelligence to take the raw data generated by the MRI machine and create readable images.
The reason MRI scans are slow, explains Daniel Sodickson, a professor in the department of radiology at NYU School of Medicine, is that they need to capture all the data necessary to generate a nice image for a radiologist to interpret. A knee scan can take around 15 to 20 minutes; a brain, 30 minutes; imaging a heart can last an hour. But what if you could run that machine faster and still get a usable image?
Using AI, “it may be possible to capture less data, and therefore image faster, while still preserving—or even enhancing—all the rich information content of the magnetic resonance images,” Sodickson says.
Here’s how they’d do it: They’d run the MRI scan faster, gathering less raw data in the process. But instead of interpreting that raw data the traditional way—which involves a tried-and-true non-AI mathematical process—they train artificial intelligence to do the data-to-image conversion. If researchers try to interpret the fast-MRI data the traditional way, the results are bad, because there’s not enough data in the first place. With AI, they are better.
The goal right now is to be able to run an MRI scan as much as 10 times faster and get an image with the required accuracy. Researchers at NYU have been working on this idea since 2016, and now they’ve announced they’re partnering with the AI research wing of Facebook, called FAIR, to push it forward.