A vision that pushes imaging further with deep learning and you.

Deep learning image reconstruction promises unparalleled benefits for patients, along with the radiologists and technologists dedicated to their care. And after nearly half a century at the forefront of computed tomography, GE Healthcare is uniquely positioned to ensure this latest advance keeps its promise.

GE Healthcare pioneered and consistently pushed the science of image reconstruction further. TrueFidelity CT Images are more than a radical, next-generation improvement. They elevate the vision of what you and TrueFidelity can achieve—together.

Introducing a new era
of image reconstruction.


TrueFidelity images on a BMI 62 patient (400 lbs, 1.73 m)

Where deep learning
does its learning matters.

A deep learning image reconstruction application is only as good as the training it receives. GE Healthcare trained its reconstruction engine using a library of thousands of low noise, filtered back projection (FBP) images—considered the gold standard of image quality.

Confidence. Not compromise.

Compared with even the most sophisticated Model-Based Iterative Reconstruction, TrueFidelity CT Images are scanning taken to another level. Contrast visualization is maintained, noise and artifacts are minimized, edges are maintained—just enough—so there’s remarkable clarity and none of the compromise that comes with unfamiliar noise texture.1


Testimonials


Dhiraj Baruah, MD
Froedtert & the Medical College of Wisconsin

"Reduced noise in deep learning cardiac imaging allows for reduction in kVp while maintaining image quality."


See for Yourself

For current Revolution CT users: Contact your GE Healthcare representative to see your own images reconstructed using TrueFidelity.
It's time to get a closer look at a better way of seeing. Learn more about GE Healthcare's TrueFidelity Images.

Resources

TrueFidelity BrochureDownload
Technical white paper on deep learning image reconstructionDownload
Deep Learning GlossaryDownload

1. As demonstrated in a clinical evaluation consisting of 60 cases and 9 physicians, where each case was reconstructed with both DLIR and ASiR-V and evaluated by 3 of the physicians. In 100% of the reads, DLIR's image sharpness was rated the same as or better than ASiR-V's. In 91% of the reads, DLIR's noise texture was rated better than ASiR-V's. This rating was based on each individual reader's preference.