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United Imaging, Debuting at AHRA as a Gold Sponsor, Announces FDA Clearance of Its Latest Deep Learning Reconstruction Technology, uAI HYPER DLR

As part of the company's Software Upgrades for Life program, the upgrade will be deployed to all installed U.S. molecular imaging systems at no additional cost to customers.

HOUSTON, Aug. 12, 2020 /PRNewswire/ -- United Imaging, a global leader in advanced medical imaging and radiotherapy equipment, has received FDA clearance for uAI HYPER DLR (Deep Learning Reconstruction), an advanced image processing function intended to reduce the noise of fluorodeoxyglucose (FDG) PET images.

The announcement comes as United Imaging sponsors the AHRA's virtual medical imaging management conference, which is happening in a virtual format for 2020. United Imaging is presenting live at three consective days during the show; its session, "Beyond the Status Quo: Getting More for Your Technology Investment" will cover strategies to reduce complexity, add value, and enhance the overall experience of buying, owning, and using medical imaging equipment. Details on how to attend the session on August 11, 12, and 13 can be found at united-imaging.com/event/ahra/.  

"uAI HYPER DLR is an example of how we simplify the overall experience of upgrading the capabilities of your system," said Cheri Gottke, Vice President of Molecular Imaging in the U.S. "It's provided to our entire MI installed base across the country because our Software Upgrades for Life program means that healthcare providers with a service contract should benefit from innovation on the systems they already own, rather than having to come back and buy new capabilities every time we add them." She confirmed that across all modalities, United Imaging's strategy is to provide price transparency and simplicity to make the buying process easier.

uAI HYPER DLR uses a deep learning technique post-processing to produce better SNR (signal-to-noise-ratio) and CNR (contrast-to-noise ratio), while preserving image consistency. Specifically, uAI HYPER DLR uses a pre-trained neural network to extract the noise components from the original image and subtract them to increase the SNR and CNR. The reason this is important is that PET/CT exams can suffer from long scanning time and high radiation dose – but typically when either is reduced, the image noise is increased and diagnostic image quality can decline. uAI HYPER DLR's ability to extract the noise from an image helps maintain the appropriate clarity needed for a good diagnosis.

The deployment of this artificial intelligence solution is another example of United Imaging fulfilling its "Equal Access to Healthcare for All" mission. "It's very important to us that even our most advanced innovations are available at all levels of our product portfolio," commented Jeffrey M. Bundy, Ph.D., CEO of United Imaging Healthcare Solutions. "That's what access means to us, so we're releasing it on machines at all price points, from the uMI 550 and uMI 780 to the uMI 550 mobile trailer configuration that is currently on the road."

United Imaging's AI subsidiary has won a number of awards globally and in the United States. The company recently opened its U.S. factory and expanded service center in Houston; the uAI HYPER DLR announcement is its third major announcement in three weeks.

ABOUT UNITED IMAGING

At United Imaging, we develop and produce advanced medical products, digital healthcare solutions, and intelligent solutions that cover the entire process of imaging diagnosis and treatment. Founded in 2011, our company has subsidiaries and R&D centers across the world. Our North American headquarters in Houston includes our corporate offices, factory, product showroom, service training center, and service parts distribution center. With a cutting-edge digital portfolio and a mission of broader access to healthcare for all, we help drive industry progress and bold change. To learn more, visit united-imaging.com or follow us on LinkedIn and Twitter @UnitedImagingHC.

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Source: United Imaging Healthcare Co., Ltd.
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