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510(k) Data Aggregation
(28 days)
The Siemens Biograph Vision PET/CT systems are combined X-Ray Computed Tomography (CT) and Position Emission Tomography (PET) scanners that provide registration and fusion of high resolution physiologic and anatomic information.
The CT component produces cross-sectional images of the body by computer reconstruction of X-Ray wansmission data from either the same axial plane taken at different angles or spiral planes taken at different angles. The PET subsystem images and measures the distribution of PET radiopharmaceuticals in humans for the purpose of determining various metabolic (molecular) and physiologic functions within the human body and utilizes the CT for fast attenuation correction maps for PET studies and precise anatomical reference for the fused PET and CT images.
The system maintains independent functionality of the CT and PET devices, allowing for single modality CT and / or PET diagnostic imaging.
These systems are intended to be utilized by approxiately trained health care professionals to aid in detecting. diagnosing, staging and restaging of lesions, tumors, disease and organ function for the evaluation of diseases and disorders such as, but not limited to, cardiovascular disease, neurological disorders and cancer. The images produced by the system can also be used by the physician to aid in radiotherapy treatment planning and interventional radiology procedures.
This CT system can be used for low dose lung cancer screening in high risk populations * * As defined by professional medical societies. Please refer to clinical literature, including the results of the National Lune Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.
The Biograph Vision systems are combined multi-slice X-Ray Computed Tomography (CT) and Positron Emission Tomography (PET) scanners. These systems are designed for whole body oncology, neurology and cardiology examinations.
The Biograph Vision systems provide registration and fusion of high-resolution metabolic and anatomic information from the two major components of each system (PET and CT). Additional components of the system include a patient handling system and acquisition and processing workstations with associated software.
Biograph Vision software is a command based program used for patient management, data management, scan control, image reconstruction and image archival and evaluation. All images conform to DICOM imaging format requirements.
The Biograph Vision PET/CT, which is the subject of this application, is substantially equivalent to the commercially available Biograph Vision PET/CT (K180811). The key differences between the current Biograph Vision (predicate device) and the Biograph Vision PET/CT that is the subject of this application is the addition of two new models (Biograph Vision 450 and Biograph Vision 450 Edge) with a smaller axial PET Field of View, as well as additional software modifications to daily / weekly Quality Control that is applicable to all models of the Biograph Vision.
The provided text describes the acceptance criteria and performance of the Siemens Biograph Vision PET/CT system, which is a combined X-Ray Computed Tomography (CT) and Positron Emission Tomography (PET) scanner. This is a 510(k) premarket notification for a medical device; therefore, the "study" proving acceptance is primarily a technical performance evaluation against established industry standards and comparison to a predicate device, rather than a clinical trial with human subjects for AI assistance.
Here's a breakdown of the requested information based on the provided document:
1. Table of Acceptance Criteria and Reported Device Performance
The performance testing for the PET component was conducted in accordance with NEMA NU2:2018.
Device: Biograph Vision PET/CT (specifically models 450/450 Edge and 600/650 Edge are mentioned, performance results are generic for both where ranges are the same, or specific to each model where they differ).
| Performance Criteria | Acceptance Criteria (New Models: 450/450 Edge) | Reported Device Performance (New Models: 450/450 Edge) | Acceptance Criteria (Predicate: 600/650 Edge) | Reported Device Performance (Predicate: 600/650 Edge) |
|---|---|---|---|---|
| Resolution - Full Size | ||||
| Transverse Resolution FWHM @ 1 cm | ≤ 4.0 mm | Pass | ≤ 4.0 mm | Pass |
| Transverse Resolution FWHM @ 10 cm | ≤ 4.8 mm | Pass | ≤ 4.8 mm | Pass |
| Transverse Resolution FWHM @ 20 cm | ≤ 5.2 mm | Pass | ≤ 5.2 mm | Pass |
| Axial Resolution FWHM @ 1 cm | ≤ 4.3 mm | Pass | ≤ 4.3 mm | Pass |
| Axial Resolution FWHM @ 10 cm | ≤ 5.4 mm | Pass | ≤ 5.4 mm | Pass |
| Axial Resolution FWHM @ 20 cm | ≤ 5.4 mm | Pass | ≤ 5.4 mm | Pass |
| Count Rate / Scatter / Sensitivity / etc. | ||||
| Sensitivity @435 keV LLD | ≥ 8.0 cps/kBq | Pass | ≥ 15.0 cps/kBq | Pass |
| Count Rate peak NECR | ≥140 kcps @ ≤ 32 kBq/cc | Pass | ≥250 kcps @ ≤ 32 kBq/cc | Pass |
| Count Rate peak trues | ≥600 kcps @ ≤ 56 kBq/cc | Pass | ≥1100 kcps @ ≤ 56 kBq/cc | Pass |
| Scatter Fraction at peak NECR | ≤43% | Pass | ≤43% | Pass |
| Mean bias (%) at peak NEC | ≤ 6% | Pass | ≤ 6% | Pass |
| Co-Registration Accuracy | ≤ 5 mm | Pass | ≤ 5 mm | Pass |
| Time of Flight Resolution at peak NEC | ≤249 ps | Pass | ≤249 ps | Pass |
| Image Quality (4 to 1) - (% Contrast / Background Variability) | ||||
| 10mm sphere | ≥ 55% / ≤ 10% | Pass | ≥ 55% / ≤ 10% | Pass |
| 13mm sphere | ≥ 60% / ≤ 9% | Pass | ≥ 60% / ≤ 9% | Pass |
| 17mm sphere | ≥ 65% / ≤ 8% | Pass | ≥ 65% / ≤ 8% | Pass |
| 22mm sphere | ≥ 70% / ≤ 7% | Pass | ≥ 70% / ≤ 7% | Pass |
| 28mm sphere | ≥ 75% / ≤ 6% | Pass | ≥ 75% / ≤ 6% | Pass |
| 37mm sphere | > 80% / < 5% | Pass | > 80% / < 5% | Pass |
2. Sample Size Used for the Test Set and Data Provenance
The document describes performance testing in accordance with NEMA NU2:2018. This standard typically involves phantom-based measurements, not human subject data. Therefore, there's no "sample size" in the sense of patient data. The provenance is internal testing performed by Siemens Medical Solutions USA, Inc. The testing is retrospective in the sense that it evaluates the manufactured device against pre-defined standards.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
Ground truth for these technical performance tests is established by physical measurements using phantoms and adherence to the NEMA NU2:2018 standard. Experts, in terms of human readers interpreting data, are not typically involved in establishing the "ground truth" for this type of technical performance testing. The "ground truth" is defined by the physical properties of the phantoms and the established physical parameters of the imaging system.
4. Adjudication Method for the Test Set
Not applicable. This is a technical performance test using phantoms and established measurement procedures, not a clinical study requiring adjudicated interpretations.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done
No, an MRMC comparative effectiveness study was not done for this submission. The submission focuses on the technical performance specifications of the PET/CT device itself and its substantial equivalence to a predicate device, not on the impact of AI assistance on human reader performance. The document mentions software modifications for Quality Control (QualityGuard and Bad Block Correction), but these are related to system maintenance and image reconstruction quality, not AI-assisted image interpretation.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study was Done
Not applicable in the context of what is typically considered a standalone AI algorithm study. The "device" here is a PET/CT scanner, and the performance testing described is for the imaging system as a whole, including image reconstruction capabilities. While the system has software, it's not described as a standalone diagnostic AI algorithm.
7. The Type of Ground Truth Used
The ground truth used for the performance testing is phantom-based measurements as specified by the NEMA NU2:2018 standard.
8. The Sample Size for the Training Set
Not applicable. This submission describes the performance of a physical imaging device, not a machine learning model that requires a training set of data.
9. How the Ground Truth for the Training Set Was Established
Not applicable, as there is no training set for an AI model in this context.
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