uMR Jupiter

The uMR Jupiter is a 5.0 Tesla magnetic resonance diagnostic device designed for producing high-resolution anatomical, functional, and spectroscopic images of the head, body, and extremities. It employs an 8-channel RF transmit system, high gradient amplitudes, and advanced software including AI-assisted Compressed Sensing and motion monitoring to enhance image quality and diagnostic utility for patients over 20 kg.

The uMR Jupiter is a 5.0 Tesla superconducting magnetic resonance diagnostic device (MRDD) designed to produce a comprehensive range of images, including sagittal, transverse, coronal, oblique cross-sectional, and spectroscopic views. It visualizes internal anatomical structures and functions of the head, body, and extremities for patients weighing over 20 kg (44 lbs), utilizing an 8-channel RF transmit system within a 60cm patient bore. The system, which conforms to NEMA and DICOM standards, relies on components such as a magnet, RF and gradient systems, patient table, spectrometer, and computer to acquire data that, when interpreted by a trained physician, aids in diagnosis, with the option of using contrast agents for specific regions of interest. Key technological advancements compared to its predicate device include a higher field strength (5.0T versus 3.0T), improved magnet homogeneity, and a significantly larger maximum gradient amplitude (120 mT/m versus 45 mT/m), alongside more RF transmit channels (8 versus 2) intended for better B1 uniformity, though it features a smaller patient bore (60cm versus 75cm). The uMR Jupiter introduces new software features such as Inline T2 Mapping using MASS (Multi-echo Acquisition of Steady State), CASS (Constructive Acquisition of Steady State), PASS (Pair-Echo Acquisition of Steady State), and MoCap-Monitoring for real-time motion detection. Extensive non-clinical testing, including performance evaluations of its AI-assisted Compressed Sensing (ACS) algorithm and various imaging sequences, demonstrated its capability to produce diagnostic quality images. The ACS algorithm, trained and validated on diverse datasets, showed improved signal-to-noise ratio and resolution over traditional compressed sensing techniques while maintaining image integrity and structural accuracy.

AI Regulatory Concerns

Powered by

Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. How does the increased static field strength of 5.0T in the uMR Jupiter, compared to the predicate's 3.0T, specifically impact image quality (e.g., SNR, CNR, spatial resolution) and diagnostic capabilities for challenging anatomies or pathologies, beyond general improvements?; What are the specific advantages and potential trade-offs of the 8-channel RF transmit system in the uMR Jupiter, particularly concerning B1 uniformity, SAR management, and power requirements at 5T, especially given the lower amplifier peak power per channel compared to the predicate?; Could you elaborate on the clinical performance metrics (e.g., scan time reduction, artifact reduction, diagnostic confidence) of the new software features like Inline T2 Mapping using MASS, CASS, and PASS, and how they specifically enhance diagnostic capabilities compared to standard sequences?; How does the MoCap-Monitoring system quantitatively improve image quality or reduce scan time by providing real-time motion alerts, and what is the sensitivity and specificity of this system in detecting clinically significant motion across different scan types?; Considering the higher maximum gradient amplitude (120 mT/m), what were the key findings from the volunteer study on peripheral nerve stimulation (PNS) thresholds, and how do these thresholds influence sequence design and operational safety limits for diverse patient populations?