FDA Radiology AI Devices

aPROMISE is a cloud-based software platform that helps healthcare professionals and researchers manage, review, and analyze PSMA PET/CT medical images. It offers tools to mark and quantify regions of interest, supporting oncology care by providing quantitative analysis and report generation to inform patient management decisions.

The MAGNETOM Free.Max is a magnetic resonance imaging (MRI) system that creates detailed cross-sectional images of the internal structures and functions of the head, body, and extremities. It includes advanced hardware and software features to enhance image quality and assist clinicians in diagnosing various conditions by providing clear and accurate images.

RayStation 10.1 is a software platform used to plan, analyze, and administer radiation therapy treatment and medical oncology. It helps clinicians generate and review treatment plans based on imaging data and user input, including advanced features like brachytherapy and ocular proton treatment planning. This system supports improved treatment accuracy and workflow efficiency for radiation oncology professionals.

Precise Position is an AI-enabled accessory for Philips Incisive CT systems that assists technologists in accurately positioning patients before CT scans. It uses a ceiling-mounted camera to detect patient orientation, determine the scan range, and automatically center the patient's anatomy. This reduces positioning time and enhances accuracy for head, chest, abdomen, and pelvic CT imaging, ultimately improving imaging workflow and patient comfort.

HYPER AiR is a software device that enhances PET images by using pre-trained neural networks integrated into the image reconstruction process. It helps radiologists and nuclear medicine physicians by reducing noise and improving image contrast in fluorodeoxyglucose (FDG) PET scans, thereby potentially improving diagnostic confidence.

Cartesion Prime (PCD-1000A/3) V10.8 is a diagnostic imaging system that combines Positron Emission Tomography (PET) and multi-slice Computed Tomography (CT). It provides fused images to help clinicians assess metabolic and functional information for evaluating oncological, cardiovascular, and neurological diseases. The device incorporates an AI algorithm called AiCE-i that improves PET image quality and reduces noise using deep learning methods, facilitating better diagnosis and treatment planning.

The Aquilion ONE (TSX-306A/3) V10.4 with Spectral Imaging System is a whole-body CT scanner that can capture detailed cross-sectional volume images of organs in a single rotation. It uses advanced AI algorithms such as deep convolutional neural networks to improve image quality and reduce noise, helping clinicians to better visualize anatomy and potentially optimize iodinated contrast media use. The spectral imaging system acquires images at different energy levels nearly simultaneously to differentiate tissues and materials, assisting in diagnosis and treatment planning.

MaxFOV 2 is a software option for GE CT scanners that uses deep learning to extend the display field of view in CT images beyond the scanner's nominal field, providing improved visualization of patient anatomy, especially useful in radiation therapy planning and for large patients. This helps clinicians obtain more complete images for diagnosis and treatment planning when parts of the body are outside standard scan coverage.

The Vantage Orian 1.5T, MRT-1550, V6.0 with AiCE Reconstruction Processing Unit is a 1.5 Tesla MRI system from Canon Medical Systems designed to produce detailed cross-sectional images of the head or body for diagnostic use. It includes AI-based image reconstruction technology (AiCE) that enhances image quality and processing for a variety of anatomical regions, helping clinicians make more accurate diagnoses.

The Aquilion Lightning (TSX-036A/7) V10.2 with AiCE-i is a computed tomography (CT) scanner designed for whole body imaging, including the head. It incorporates an AI-driven image reconstruction algorithm called AiCE-i that reduces image noise and improves image quality by using deep convolutional neural networks. This enhancement supports clinicians by providing clearer, more detailed images with potentially lower radiation dose, facilitating better diagnosis and patient care.