项目克拉拉: NVIDIA 超级计算机应用平台重新定义医学影像
这篇文章来自 nvidia.com。原始 url 是: https://blogs.nvidia.com/blog/2018/03/28/ai-healthcare-gtc/
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There are about 3 million medical imaging instruments installed around the world. With only a couple hundred thousand new ones sold each year, it would take decades to update this install base.
NVIDIA’s Project Clara, a medical imaging supercomputer, renews the capabilities of these machines in place. Unveiled this week at the GPU 技术会议, in Silicon Valley, Project Clara takes advantage of incredible advancements in computation.
Medical imaging instruments have been vital to early detection and improvement of patient outcomes for more than four decades. Innovation in the field has come from improvements in detector technology and, more recently, parallel computing.
A decade ago, researchers realized NVIDIA GPUs provide the most efficient architecture for medical imaging applications and could help reduce radiation exposure, improve image quality and produce images in real time. More recently, deep learning is dominating, with more than half of new research in medical imaging applications involving AI.
Tremendous Advancements Come to Imaging From Computation
Computational game-changers like CT iterative reconstruction and MR compressed sensing are reducing radiation exposure up to 90 percent and shortening the time it takes for an MRI image to be captured.
Deep learning and AI are generating exciting opportunities for advanced image analysis and quantification. A recent algorithm called V-Net uses 3D volumetric segmentation and can automatically measure the volume of blood flowing through the heart. Fifteen years ago, this algorithm would’ve needed a computer that cost $10 million and consumed 500 kW of power. Today, it can run on a fewTesla V100 GPUs.
Vision for Clara
Building on our 10 years in medical imaging and work with our development partners, we see the potential to re-imagine how computing can improve medical imaging.
Clara is virtual: it can run many computational instruments simultaneously. Clara is remote: it leverages NVIDIA vGPUs to enable multi-user access. Clara is universal: it can perform the computation for any instrument, whether CT, MR, ultrasound, X-ray or Mammography. And Clara is scalable: it uses Kubernetes on GPUs to efficiently scale compute with demand.
Working with us are dozens of healthcare companies, startups and research hospitals. Their AI applications like AutoMap and V-Net bring intangible value to radiology.
AutoMap, from the MGH Martinos center, can shorten acquisition time of MRI and boosts image quality. V-Net can automatically measure anatomy and assess functionality. Cinematic rendering pioneered by Elliot Fishman at Johns Hopkins University brings a new level of quality, ultimately saving time for radiologists and improving patient outcomes.
Subtle Medical, which is working on dozens of applications in medical imaging, recently won over more than a quarter of a million dollars in the healthcare category in our Inception program awards.
“New technologies are transforming healthcare,” said Dr. Greg Zaharchuk, founder of Subtle Medical, radiologist and associate professor in Radiology at Stanford. “NVIDIA’s vision for a virtualized imaging supercomputer is an exciting new chapter that will revolutionize our ability to deliver AI-powered healthcare.”
Modern medical imaging applications demand new levels of computing, scale and accessibility. Clara is our computing platform to revolutionize medical imaging.