GE Healthcare’s deep learning-based image reconstruction engine gains FDA clearance
GE Healthcare’s Deep Learning Image Reconstruction (DLIR) engine, designed to be used with its Revolution Apex CT solution, has gained FDA clearance. The engine can also be implemented as an upgrade to the company’s Revolution CT system.
The DLIR engine uses a deep neural network to produce CT images that “can elevate every image to a powerful first impression with impressive image quality performance,” according to GE Healthcare. This is the first time deep learning-powered CT reconstruction technology has received FDA approval.
“We are proud to usher in the next generation of image reconstruction,” Mike Barber, GE Healthcare’s president and CEO of molecular imaging & computed tomography, said in a prepared statement. “Our DLIR engine combines the ground truth image quality of filtered back projection with the low dose capabilities of iterative reconstruction to produce TrueFidelity CT Images. These images offer outstanding image quality and restore noise texture to improve radiologists’ confidence in diagnosing a wide range of clinical cases.”
“Physicians that have reviewed our new TrueFidelity CT Images consistently say they are among the best CT images they have ever seen, and our 510(k)-reader study also demonstrated this improvement,” Scott Schubert, GE Healthcare’s general manager of global premium CT, said in the same statement.
GE Healthcare also announced it received FDA approval for three other CT applications: Bone VCAR, Thoracic VCAR and GSI Pulmonary Perfusion, and SnapShot Freeze 2.
Michael has more than 18 years of experience as a professional writer and editor. He has written at length about cardiology, radiology, artificial intelligence and other key healthcare topics.