Revolution Ascend is a computed tomography (CT) scanner designed for head, whole body, cardiac and vascular imaging. It produces detailed cross-sectional images using X-ray data taken from multiple angles. The system incorporates advanced AI-enabled features like deep learning-based auto positioning and machine learning-enabled intelligent protocoling to improve workflow and image acquisition. It aids clinicians in diagnosis, treatment planning, and monitoring.
The system is intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data taken at different angles and planes, including Axial, Cine, Helical (Volumetric), Cardiac, and Gated acquisitions. These images may be obtained either with or without contrast. The system is indicated for head, whole body, cardiac and vascular X-ray Computed Tomography applications in patients of all ages.
Revolution Ascend uses the same fundamental principles as its predicate device, involving a gantry, patient table, operator console, host computer, and power distribution unit. It includes image acquisition and reconstruction hardware/software, general system software, and accessories. It incorporates advanced AI-enabled workflow features such as Auto Pilot workflow with deep learning-based patient Auto Positioning and Intelligent Protocoling enabled by Machine Learning. It has a widened bore gantry to accommodate large patients and supports diverse acquisition modes including axial, cine, helical, cardiac, and gated.
The device underwent extensive verification and validation testing including risk analysis, design and integration testing, safety and simulated use testing. Non-clinical bench testing demonstrated performance equivalence with the predicate device. Image quality and dose performance were evaluated using standard phantoms including for large patient simulations. Clinical testing was not required due to successful engineering bench testing. Compliance with IEC 60601-1, NEMA standards, and FDA regulatory requirements was established.
No predicate devices specified
Submission
10/23/2020
FDA Approval
11/20/2020
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