uMI Panorama

The uMI Panorama GS is a hybrid PET/CT imaging system designed for comprehensive diagnostic evaluations across oncology, cardiology, and neurology by fusing functional PET data with anatomical CT information. This iteration enhances diagnostic precision and workflow through the integration of multiple advanced AI-driven software algorithms, including improved iterative reconstruction, comprehensive motion correction, extended field-of-view imaging, and CT-less attenuation correction.

The uMI Panorama is a diagnostic imaging system integrating Positron Emission Tomography (PET) and Computed Tomography (CT) to provide comprehensive functional and anatomical information. PET imaging assesses metabolic and physiological functions by measuring radiopharmaceutical distribution, while CT offers detailed anatomical tomographic data and photon attenuation information critical for PET image correction. The system fuses these modalities for precise anatomical referencing of metabolic findings, assisting healthcare professionals in detecting, diagnosing, staging, planning treatment, and evaluating treatment response for diseases in oncology, cardiology, and neurology, and also supports low-dose CT lung cancer screening. This version of the uMI Panorama GS incorporates significant software enhancements, validated through non-clinical testing and reviews of sample clinical images by board-certified radiologists, which confirmed their ability to produce diagnostically sufficient images as intended. Key updates feature an AI-based Iterative Reconstruction (AIIR) with a 3D Deep Convolutional Neural Network to reduce noise, artifacts, and potentially lower CT radiation dose. Other additions include HYPER Iterative for optimized reconstruction, uExcel DPR for improved PET contrast, Respiratory Motion Correction (RMC) and a comprehensive Motion Management (MM) system to mitigate motion artifacts. An AI-Extended Field of View (AIEFOV) algorithm, proven with extensive data, enhances image quality and SUV accuracy for subjects exceeding the CT scan-FOV. Additionally, CT-less Attenuation Correction (CT-less AC) employs deep learning for PET AC without CT, alongside uKinetics for parametric imaging, Retrospective Respiratory-gated Scan, uExcel Unity for SUV normalization, and uExcel iQC for automated quality control, all expanding the system’s advanced capabilities.

AI Regulatory Concerns

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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 performance of the CT-less Attenuation Correction algorithm compare to traditional CT-based attenuation correction in terms of quantitative accuracy (SUV values) across diverse patient demographics and radiotracers?; What are the specific quantitative improvements in high contrast spatial resolution and low contrast detectability with the updated AIIR (3D-DCNN) in CT, and what is the validated range of CT radiation dose reduction achievable in clinical practice without compromising diagnostic information?; For the AIEFOV algorithm, what is the impact on PET quantification accuracy and image artifact levels when imaging highly truncated anatomy, and are there specific patient body habitus or positioning scenarios where its performance is suboptimal?; How does the integrated Motion Management (MM) system differentiate between and correct for various types of patient motion (e.g., respiratory, bulk, head), and what is its efficacy in improving lesion detectability and volumetric accuracy in dynamic PET studies?; Considering the suite of new AI-driven algorithms (AIIR, uExcel DPR, RMC, AIEFOV, CT-less AC), what is the cumulative effect on total examination time, including image acquisition and reconstruction, and how does this impact overall clinical workflow efficiency?