uPMR Zenith
Shanghai United Imaging Healthcare Co., Ltd.The uPMR Zenith is a combined PET/MR scanner that uses advanced AI and deep learning features to help clinicians acquire, reconstruct, and process high-quality images of anatomy and function in a single system. Its AI tools improve image clarity, speed up workflow, and reduce artifacts for better diagnosis across the body.
Intended Use
The uPMR Zenith system combines magnetic resonance diagnostic devices (MRDD) and Positron Emission Tomography (PET) scanners. This product provides image recognition and image fusion technologies for clinical diagnosis. PET and MR can be scanned simultaneously or separately. This product utilizes the tissue imaging from MR to provide attenuation correction maps for PET studies. The combined system assists healthcare professionals in detecting, localizing, and diagnosing diseases throughout the body.
Technology
The uPMR Zenith system is a hybrid imaging device that combines MR and PET technology to provide simultaneous or separate acquisition of anatomic and physiologic information. It features AI-powered image reconstruction (ACS, DeepRecon, uExcel DPR), motion correction (AiCo, Inline MOCO), workflow automation (EasyScan, EasyBolus, EasyCrop), and artifact correction (SparkCo, ImageGuard). The system consists of hardware (PET detector, superconducting magnet, gradient coils, RF coils, host computer, patient table, etc.) and software, conforms to DICOM/NEMA standards, and provides image fusion, registration, and processing tools.
Performance
Performance was established through a series of non-clinical, software, and independent clinical image quality tests. Non-clinical tests included conformance to MR and PET standards (NEMA, IEC, ISO), as well as image quality, safety, and effectiveness verification. AI algorithms (ACS, DeepRecon, EasyScan, AiCo, SparkCo, ImageGuard, etc.) were validated on independent datasets and showed improved or equivalent image quality, SNR, artifact suppression, and workflow automation versus conventional approaches. Clinical images were reviewed by board-certified radiologists, confirming diagnostic quality images. PET/MR MRAC with Bone Segmentation and uExcel DPR deep learning PET reconstruction also surpassed conventional techniques in quantitative performance metrics.
Predicate Devices
No predicate devices specified
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