(88 days)
The uMI Panorama is a diagnostic imaging system that combines two existing imaging modalities PET and CT. The quantitative distribution information of PET radiopharmaceuticals within the patient body measured by PET can assist healthcare providers in assessing metabolic and physiological functions. CT provides diagnostic tomographic anatomical information as well as photon attenuation information for the scanned region. The accurate registration and fusion of PET and CT images provides anatomical reference for the findings in the PET images.
This system is intended to be operated by qualified healthcare professionals to assist in the detection, localization, diagnosis, staging, restaging, treatment planning and treatment response evaluation for diseases, inflammation, infection and disorders in, but not limited to oncology, cardiology and neurology. The system maintains independent functionality of the CT device, allowing for single modality CT diagnostic imaging.
This CT system can be used for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer. The screening must be performed within the established inclusion criteria of programs / protocols that have been approved and published by either a governmental body or professional medical society.
The proposed device uMI Panorama GS combines a 148 cm axial field of view (FOV) PET and multi-slice CT system to provide high quality functional and anatomical images, fast PET/CT imaging and better patient experience. The system includes PET gantry, CT gantry, patient table, power supply cabinet, console and reconstruction system, chiller, vital signal module.
The uMI Panorama GS has been previously cleared by FDA via K231572. The mainly modifications performed on the uMI Panorama GS (K231572) in this submission are due to the algorithm update of AIIR, the addition of HYPER Iterative, uExcel DPR, RMC, AIEFOV, Motion Management, CT-less AC, uKinetics, Retrospective Respiratory-gated Scan, uExcel Unity and uExcel iQC.
The provided text describes the performance data for the uMI Panorama device, focusing on the AIEFOV algorithm. Here's a breakdown based on your request:
Acceptance Criteria and Reported Device Performance for AIEFOV Algorithm
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Bench Tests: | Bench Tests: |
| 1. AI EFOV shall improve the accuracy of CT value, and improve the accuracy and uniformity of PET image SUV by performing attenuation correction with CT generated with AIEFOV algorithm when scanned object exceed CT field of view. | Bench tests showed that performing attenuation correction with AIEFOV can improve the CT number and the accuracy of SUV, in cases where the scanned object exceeds the CT field of scan-FOV. |
| 2. AI EFOV shall have consistent CT value, and PET image SUV by performing attenuation correction with CT generated with AIEFOV algorithm when scanned object does not exceed the CT field of view. | Meanwhile, when the scanned object did not exceed the CT scan-FOV, either AIEFOV or EFOV results in consistent SUV and CT number. |
| Clinical Evaluation: | Clinical Evaluation: |
| Image quality of PET images attenuated with AIEFOV should provide sufficient diagnostic confidence, with blind comparison regarding image Artifacts and homogeneity of same tissue by qualified clinical experts. | Clinical evaluation concluded the image quality of PET attenuated with AIEFOV provides sufficient diagnostic confidence. (Implied that artifacts and homogeneity were acceptable, as confidence was sufficient). |
| Overall Summary: The performing attenuation correction with AIEFOV CT can improve the accuracy of image SUV in cases where the scanned object exceeds the CT field of view. | Based on the bench tests and clinical evaluation, the performing attenuation correction with AIEFOV CT can improve the accuracy of image SUV, in cases where the scanned object exceeds the CT field of view. |
Study Details Proving Device Meets Acceptance Criteria:
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Sample Size and Data Provenance for Test Set:
- Test Set Sample Size: 9303 images from 13 patients.
- Data Provenance: Not explicitly stated regarding country of origin, but described as "clinical images" scanned in uMI Panorama GS. The study appears retrospective or a controlled prospective study for validation.
- Patient Characteristics (N=13):
- Age: 62 ± 14 years (range: 35-79)
- Sex: 7 male, 6 female
- BMI: 25.0 ± 3.5 kg/m² (range: 21.2-31.4)
- Injected activity: 0.10 ± 0.01 mCi/kg (range: 0.04-0.11)
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Number of Experts and Qualifications for Ground Truth for Test Set:
- Number of Experts: Two (2)
- Qualifications: "American Board qualified clinical experts"
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Adjudication Method for Test Set:
- The experts performed a "blind comparison" regarding image Artifacts, homogeneity of same tissue, and diagnostic confidence in PET images. Details of how disagreements were resolved (e.g., 2+1, 3+1, or if consensus was required) are not specified.
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Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
- Not explicitly stated as a formal MRMC study comparing human readers with AI vs. without AI assistance. The clinical evaluation involved two experts reviewing images generated with AIEFOV for diagnostic confidence, rather than a comparative trial measuring improvement in human reader performance aided by AI. Therefore, an effect size of human reader improvement with AI vs. without AI assistance is not provided.
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Standalone (Algorithm Only) Performance:
- Yes, the "Bench tests" portion of the performance evaluation appears to assess the algorithm's performance directly on quantitative metrics (CT value, SUV accuracy and uniformity) using phantoms and patient studies in different truncation situations. The clinical evaluation also assessed the quality of images produced by the algorithm, implying a standalone assessment of its output for diagnostic confidence.
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Type of Ground Truth Used:
- For bench tests: Quantitative measurements from phantom scans and potentially patient studies where the "true" CT values and SUV could be established or inferred relative to known conditions (e.g., non-truncated scans serving as reference).
- For clinical evaluation: Expert consensus/assessment by "American Board qualified clinical experts" regarding subjective image quality metrics (artifacts, homogeneity, diagnostic confidence).
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Sample Size for Training Set:
- The training data for the AIEFOV algorithm contained 506,476 images.
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How Ground Truth for Training Set was Established:
- "All data were manually quality controlled before included for training." This suggests a process of human review and verification to ensure the accuracy and suitability of the training images. Further details on the specific criteria or expert involvement for this manual QC are not provided.
- It is explicitly stated that "The training dataset used for the training of AIEFOV algorithm was independent of the dataset used to test the algorithm."
§ 892.1200 Emission computed tomography system.
(a)
Identification. An emission computed tomography system is a device intended to detect the location and distribution of gamma ray- and positron-emitting radionuclides in the body and produce cross-sectional images through computer reconstruction of the data. This generic type of device may include signal analysis and display equipment, patient and equipment supports, radionuclide anatomical markers, component parts, and accessories.(b)
Classification. Class II.