由 Lisa Chamoff
, Contributing Reporter | December 06, 2017
CHICAGO — Anyone who is into PC gaming has probably heard of NVIDIA. The company is known for its gaming platforms and graphics cards that make the real and virtual worlds nearly indistinguishable.
For the last decade, the company has quietly been bringing that expertise to health care, working with medical device companies like GE Healthcare on new algorithms for CT dose reduction and image processing. NVIDIA had never exhibited at RSNA — until this year.
“We’ve never exhibited at RSNA because we’ve largely been in the instrument or in the workstation that runs the PACS software,” Kimberly Powell, vice president of health care at NVIDIA, told HCB News during an interview at the show. “Over the last couple [of years] there’s all this really interesting new opportunity that’s coming about in all the image processing techniques, brand new visualization techniques and, of course, now artificial intelligence.”
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While gaming and self-driving cars, another area where NVIDIA is contributing its computing power, don’t seem to have much to do with health care, the company has used its experience to provide a foundation for the next radiology frontier of artificial intelligence (AI), which was everywhere at this year’s annual meeting.
“Gamers always want things to become more and more realistic,” Powell said. “They want hair to look like hair, fire to look like fire and water to look like water. That required us to use a lot of mathematics and physics to represent that. Because they were pushing us in that direction, we discovered that we had created this amazing processing device for computation.
“We believe that with the evolution of all of the algorithms and the instruments themselves, and with the new addition of artificial intelligence, there is definitely going to be an opportunity for a new computing platform that will be needed for health care,” Powell continued. “For self-driving cars, we’ve created an AI supercomputer that goes in the car. It is about the size of a textbook and it has the same computational capacity as 60 servers. If we can understand the problem in health care from start to finish, of when the patient enters the room to be imaged all the way through to presenting the information to the radiologists, we can make all these computational efficiencies across that, and introduce all sorts of new capabilities and innovations.”
About 10 years ago, NVIDIA came up with a new way of using a graphics processing unit (GPU) for fast general computation, allowing device companies to process medical images much more quickly.