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The device is a diagnostic imaging system that combines Positron Emission Tomography (PET) and X-ray Computed Tomography (CT) systems. The CT component produces crosssectional images of the body by computer reconstruction of x-ray transmission data. The PET component images the distribution of PET radiopharmaceuticals in the patient body. The PET component utilizes CT images for attenuation correction and anatomical reference in the fused PET and CT images.

This device is to be used by a trained health care professional to gather metabolic and functional information from the distribution of the radiopharmaceutical in the body for the assessment of metabolic and physiologic functions. This information can assist in the evaluation, detection, diagnosis, therapeutic planning and therapeutic outcome assessment of (but not limited to) cancer, cardiovascular disease and brain dysfunction. Additionally, this device can be operated independently as a whole body multi-slice CT scanner.

Celesteion, PCA-9000A/3, V6.5, is a large bore, TOF, PET-CT system, which combines a high-end CT system with a high-throughput PET system. The high-end CT system is a multi-slice helical CT scanner with a gantry aperture of 900 mm and a maximum scanning field of 700 mm. The highthroughput PET system has a time of flight (TOF) detector with temporal resolution of 450 ps. Celesteion, PCA-9000A/3, V6.5 is intended to acquire PET images of any desired region of the whole body and CT images of the same region (to be used for attenuation correction or image fusion), to detect the location of positron emitting radiopharmaceuticals in the obtained images. This device is used to gather the metabolic and functional information from the distribution of radiopharmaceuticals in the body for the assessment of metabolic and physiologic functions. This information can assist research, diagnosis, therapeutic planning, and therapeutic outcome assessment. This device can also function independently as a whole body multi-slice CT scanner.

The provided text describes a 510(k) premarket notification for a medical device, the Celesteion, PCA-9000A/3, V6.5, which is a PET-CT system. However, the document primarily focuses on demonstrating substantial equivalence to a predicate device (Celesteion, PCA-9000A/3, V6.4) based on modifications to existing features and the addition of new features (Variable Bed Time/vBT, Clear Adaptive Low-Noise Method/CaLM Reconstruction, and PET ECG gating).

Crucially, the provided text does not contain detailed information about acceptance criteria or a comprehensive study that proves the device meets specific performance criteria through a rigorous scientific study with statistical analysis, as would be expected for demonstrating clinical performance or improvement in human reader performance. Instead, it focuses on technical validation and equivalence.

Therefore, I cannot provide a complete answer to all parts of your request based solely on the provided text. I will address the points that can be gleaned from the document and explicitly state what information is not present.

Here's a breakdown based on the provided text:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state quantitative acceptance criteria for features like image quality, diagnostic accuracy, or human reader improvement, nor does it provide a table of reported device performance against such criteria. The "testing" section mentions:

• "Bench testing utilizing phantoms were conducted and it was determined that use of CaLM Reconstruction resulted in images with reduced noise while preserving detail and contrast."
• "Representative clinical images were acquired to demonstrate that the subject device is capable of obtaining multi-phase, PET ECG gated data and that the new feature performs as intended."

This is a qualitative statement of performance rather than a quantitative measurement against defined acceptance criteria.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

The document mentions "Bench testing utilizing phantoms" and "Representative clinical images were acquired."

• Sample Size for Test Set: Not specified. It only mentions "phatoms" and "representative clinical images," implying a limited number rather than a statistically robust test set for clinical performance.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). The device is from Japan, but the location of clinical image acquisition is not mentioned.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable/Not mentioned. The testing described is bench testing with phantoms and demonstration with "representative clinical images." There is no indication of expert review or ground truth establishment for a test set to assess diagnostic performance.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

Not applicable/Not mentioned. No expert review or adjudication process is described for a test set.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

No MRMC study is mentioned. The device is a PET-CT system, not an AI-assisted diagnostic software, and its validation focuses on the performance of the hardware/software regarding image acquisition and reconstruction, not on improving human reader performance.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

The device itself is a diagnostic imaging system (a PET-CT scanner). Its performance is inherently "standalone" in generating images and data. The document does not describe an "algorithm only" component separate from the imaging system's fundamental operation. The stated performance enhancements ("reduced noise while preserving detail and contrast" for CaLM) refer to the quality of the output generated by the device's algorithms.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

For the phantom study, the "ground truth" would be the known properties of the phantom. For "representative clinical images," no formal ground truth establishment (like pathology, expert consensus, or outcomes data) is described in the context of a performance study.

8. The sample size for the training set

Not applicable/Not mentioned. This device is a PET-CT scanner, not an AI model trained on a large dataset of patient images in the typical sense for a diagnostic AI algorithm. Its algorithms (like CaLM) are likely based on signal processing and reconstruction principles, not deep learning requiring a large "training set" of annotated images.

9. How the ground truth for the training set was established

Not applicable/Not mentioned, for the reasons outlined in point 8.

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