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Facebook is working with NYU in a bid to transform the speed at which MRI scans can be performed. Using artificial intelligence, and training on 3 million MRI scans, fastMRI can supposedly work ten times as fast as a traditional MRI scan.

MRI scans can offer medical professionals a level of detail that other scans cannot. But they can take some time, compared to, say, an X-ray. This is because MRI scans work by gathering a sequence of views which can then be turned into cross sections of a patient’s internal tissue. To get a detailed picture more data needs to be gathered in the scan.

However, with this project, the aim is to reduce the amount of data that needs to be collected. It will do this by using neural networks to build up the foundational components within a scan, so the scan can instead focus on what’s unique to that specific patient. This a bit like how humans are able to process information by filtering out what they already know/what is already familiar and focusing on what is important.

Some work has already been done by researchers at NYU on getting neural networks to produce high quality images from limited data.

What makes Facebook’s and NYU’s MRI research unique?

In a post published on Monday, Larry Zitnick from Facebook’s AI Research Lab and Daniel Sodickson from NYU School of Medicine explained why this project is different from similar artificial intelligence research in medicine:

“Unlike other AI-related projects, which use medical images as a starting point and then attempt to derive anatomical or diagnostic information from them (in emulation of human observers), this collaboration focuses on applying the strengths of machine learning to reconstruct the most high-value images in entirely new ways. With the goal of radically changing the way medical images are acquired in the first place, our aim is not simply enhanced data mining with AI, but rather the generation of fundamentally new capabilities for medical visualization to benefit human health.”

Facebook and NYU are ambitious about the scale of the project. They plan to open source the research to encourage wider participation in the area, and potentially push the boundaries of AI-informed medical research even further.

But the teams say that “its long-term impact could extend to many other medical imaging applications” such as CT scans.

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Co-editor of the Packt Hub. Interested in politics, tech culture, and how software and business are changing each other.


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