FDA Radiology AI Devices

Deep Learning Image Reconstruction by GE Healthcare Japan Corporation is a deep learning based software integrated into CT scanners to reconstruct high-quality cross-sectional images of the head, whole body, cardiac, and vascular systems. It uses a trained deep neural network to reduce image noise and artifacts while maintaining spatial resolution, aiding clinicians in obtaining clearer diagnostic images with routine CT throughput.

Deep Learning Image Reconstruction by GE Healthcare is a software solution that uses a specialized deep neural network to reconstruct high-quality CT images from raw X-ray data. It produces images with less noise and improved image quality while maintaining low radiation dose. It supports head, whole body, cardiac, and vascular CT scans, helping clinicians obtain clearer images for diagnosis.

The Revolution Ascend is a CT scanner designed to generate detailed cross-sectional images of various body parts including the head, whole body, cardiac, and vascular areas. It uses x-ray transmission data taken at different angles and planes. The device supports multiple acquisition modes and post-processing of images, aiding clinicians in diagnosing disease, trauma, and abnormalities, as well as planning and monitoring therapy.

Deep Learning Image Reconstruction by GE Healthcare is a software that uses deep neural networks to reconstruct high-quality CT images from X-ray transmission data. It helps radiologists by producing images with improved noise reduction, spatial resolution, and artifact suppression, suitable for head, whole body, cardiac, and vascular CT scans at routine clinical throughput.

Deep Learning Image Reconstruction software by GE Healthcare is an AI-powered CT image reconstruction tool that uses a deep neural network to produce high-quality CT images for head, whole body, cardiac, and vascular scans. It improves image noise, contrast detectability, and spatial resolution, helping clinicians obtain clearer images from CT scans for accurate diagnosis and patient care.