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Deep Learning-Based CT Image Reconstruction Technology

By 13th March 2020No Comments


GE Healthcare has announced its Deep Learning Image Reconstruction engine on its new Revolution Apex CT device and as an upgrade to its Revolution CT system in the United States. Mike Barber of GE Healthcare said “Our Deep Learning Image Reconstruction engine combines the ground truth image quality of filtered back projection (FBP) 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.

Deep Learning Image Reconstruction (DLIR) is the next generation image reconstruction option that uses a dedicated Deep Neural Network (DNN) to generate TrueFidelity CT Images, whch have the potential to improve reading confidence in a wide range of clinical applications such as head, whole body and cardiovascular, for patients of all ages.

Compared to current iterative reconstruction technology, TrueFidelity CT Images can elevate every image to a powerful first impression with outstanding image quality performance, and preferred image sharpness and noise texture, without compromising dose performance.

Physicians who 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,” said Scott Schubert, General Manager of Global Premium CT, GE Healthcare. “Revolution Apex delivers CT technology innovations including the Quantix 160 x-ray tube and Deep Learning Image Reconstruction, and so we are pleased to bring these innovations as optional upgrades to our Revolution CT users as well.

GE Healthcare, Chicago IL, USA