FDA Radiology AI Devices

The Aquilion Precision (TSX-304A/4) V10.14 with AiCE is an advanced whole-body CT scanner that leverages AI through deep convolutional neural networks to reduce image noise and improve image quality. It also uses an iterative reconstruction algorithm to reduce radiation dose while enhancing spatial resolution. This helps clinicians obtain clearer and more detailed images to support diagnosis and treatment planning across various body regions, including abdomen, chest, brain, and extremities.

The Aquilion Serve (TSX-307A/1) V1.2 with AiCE-i is a whole-body multi-slice helical CT scanner enhanced with AiCE, an AI-based noise reduction algorithm using deep convolutional neural networks. It improves image quality and reduces noise for abdomen, pelvis, lung, cardiac, extremities, head, and inner ear scans, helping clinicians obtain clearer diagnostic images faster and with potentially reduced radiation dose.

The uMR 680 is a 1.5 Tesla superconducting magnetic resonance imaging (MRI) system designed to produce diagnostic-quality images of the head, body, and extremities. It includes a deep-learning based image processing algorithm called DeepRecon for image denoising and super-resolution, enhancing image quality and assisting clinicians in diagnosis.

The uCT ATLAS Astound with uWS-CT-Dual Energy Analysis is an advanced CT scanner designed to produce cross-sectional images of various body regions, including head, whole body, cardiac, and vascular areas. It integrates deep learning for image reconstruction to enhance image quality and reduce noise, and includes software for dual energy analysis to differentiate tissue types based on their chemical composition. This aids clinicians in lung cancer screening and detailed anatomical evaluation, supporting early diagnosis and treatment.

The SIGNA Victor is a 1.5 Tesla whole body MRI scanner designed to provide high-resolution diagnostic images quickly and with a high signal-to-noise ratio. It produces images in multiple planes and supports imaging of the entire body, including brain, spine, joints, heart, abdomen, and more, aiding clinicians in diagnosing a variety of conditions.

The Philips CT 3500 is a computed tomography X-ray system designed to produce detailed images of the head, whole body, cardiac, and vascular regions. It supports diagnostic imaging and low dose lung cancer screening using advanced imaging features and includes AI-based image reconstruction for improved image quality at reduced dose, helping clinicians with early detection and diagnosis.

The uMR Omega with uWS-MR-MRS is a magnetic resonance imaging (MRI) system combined with advanced software solutions designed for viewing, manipulating, and analyzing MR images. It includes features such as AI-assisted image reconstruction and multiple post-processing tools that aid clinicians in evaluating anatomical and functional information. This helps radiologists and physicians diagnose more effectively by providing enhanced image quality and quantitative assessment capabilities.

The MAGNETOM Sola, MAGNETOM Altea, and MAGNETOM Sola Fit are MRI systems designed to produce detailed cross-sectional images and spectroscopic data of the head, body, and extremities. They support advanced imaging workflows and reconstruction frameworks, helping clinicians diagnose medical conditions by providing clear internal images and physical parameters. The system can also assist in interventional procedures with MR-compatible devices.

The Aquilion Precision (TSX-304A/4) V10.10 with AiCE is a whole-body CT scanner with ultra-high resolution that uses advanced AI deep convolutional neural networks to reduce image noise and enhance image quality, particularly for abdominal, pelvic, lung, and cardiac scans. It helps clinicians obtain clearer, more detailed images while potentially reducing radiation dose, thereby improving diagnostic confidence and patient care.

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.