Boosting speed, efficiency of MRI scans with machine learning

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Image Credit: iStock

Magnetic resonance imaging is an invaluable tool in the medical field, but it’s also a slow and cumbersome process. It may take fifteen minutes or an hour to complete a scan, during which time the patient, perhaps a child or someone in serious pain, must sit perfectly still. NYU has been working on a way to accelerate this process, and is now collaborating with Facebook  with the goal of cutting down MRI durations by 90 percent by applying AI-based imaging tools.

The reason MRIs take so long is because the machine must create a series of 2D images or slices, many of which must be stacked up to make a 3D image.

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The FastMRI project, begun in 2015 by NYU researchers, investigates the possibility of creating imagery of a similar quality to a traditional scan, but by collecting only a fraction of the data normally needed.

Related article:  We can’t embrace machine learning in healthcare without ‘serious vetting’ of the underlying data

Think of it like scanning an ordinary photo. You could scan the whole thing… but if you only scanned every other line (this is called “undersampling”) and then intelligently filled in the missing pixels, it would take half as long. And machine learning systems are getting quite good at tasks like that.

It’s easier on the patient, and one machine could handle far more people than it does doing a full scan every time, making scans cheaper and more easily obtainable.

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Read full, original post: NYU and Facebook team up to supercharge MRI scans with AI

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