FDA Radiology AI Devices

The uMI Panorama is a PET/CT system that provides combined metabolic and anatomical imaging to assist healthcare professionals in detecting, diagnosing, staging, and managing various diseases including oncology, cardiology, and neurology conditions. It supports both PET and standalone CT imaging and can be used for low dose CT lung cancer screening, enhancing patient care with fused functional and anatomical images.

The Biograph Vision.X and Biograph Vision.X Edge are advanced combined PET and CT scanners that provide high-resolution images by fusing metabolic and anatomical information. These systems assist healthcare professionals in detecting, diagnosing, and monitoring diseases such as cancer, cardiovascular and neurological disorders, and support treatment planning and interventional procedures.

The ECHELON Synergy MRI system is an advanced 1.5 Tesla MRI scanner that generates high-quality diagnostic images of the head, body, spine, and extremities without ionizing radiation. It incorporates deep learning reconstruction (DLR) and other AI-powered functions like AutoPose and AutoClip to enhance image quality and improve workflow efficiency, helping radiologists diagnose conditions with improved image clarity and reduced scan times.

DeepCatch is software that analyzes whole-body CT scans to automatically identify and measure various anatomical structures like skin, muscle, fat, internal organs, and the central nervous system. It produces 3D models and quantitative data to help doctors better understand the patient's body composition and anatomy, aiding clinical decisions by providing accurate volume and proportion measurements.

Precision DL by GE Healthcare is a deep learning software designed to improve the quality of non-Time-of-Flight PET images used in clinical oncology. It enhances image contrast and reduces noise, helping clinicians obtain clearer PET scans to better detect and evaluate cancer, ultimately supporting improved patient care.

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.

The Ingenia series MR Systems by Philips Medical Systems Nederland B.V. are advanced MRI devices that produce detailed cross-sectional images and spectra of the head, body, and extremities. Utilizing strong magnetic fields and radio signals, these systems aid physicians in diagnosing diseases, planning treatments, and guiding minimally invasive procedures by providing high-quality images. The new Precise Image software feature enhances image quality by employing deep learning to improve sharpness and reduce noise and artifacts, facilitating better clinical assessment.

MAGNETOM Amira and MAGNETOM Sempra are magnetic resonance imaging (MRI) systems designed to produce detailed images and spectra of the head, body, and extremities. They include advanced AI-powered image reconstruction features such as Deep Resolve Boost and Deep Resolve Sharp, which enhance image quality and sharpness. These systems assist clinicians by providing faster, higher-quality images to improve diagnosis and guide interventions.

The uMI Panorama is a diagnostic imaging system that combines PET and CT imaging modalities to provide detailed metabolic and anatomical information. It assists clinicians in detecting, diagnosing, staging, and managing diseases, inflammation, infection, and disorders in multiple clinical fields. This system can also independently function as a CT scanner for lung cancer screening and delivers enhanced image reconstruction through AI algorithms, improving image quality and diagnostic accuracy.

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.