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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 cross-sectional 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, localization, diagnosis, staging, restaging, follow-up, therapeutic planning and therapeutic outcome assessment of (but not limited to) oncological, cardiovascular, neurological diseases and disorders. Additionally, this device can be operated independently as a whole body multi-slice CT scanner.

AiCE-i for PET is intended to improve image quality and reduce image noise for FDG whole body data by employing deep learning artificial neural network methods which can explore the statistical properties of the signal and noise of PET data. The AiCE algorithm can be applied to improve image quality and denoising of PET images.

Deviceless PET Respiratory gating system, for use with Cartesion Prime PET-CT system, is intended to automatically generate a gating signal from the list-mode PET data. The generated signal can be used to reconstruct motion corrected PET images affected by respiratory motion. In addition, a single motion corrected volume can automatically be generated. Resulting motion corrected PET images can be used to aid clinicians in detection, localization, evaluation, diagnosis, staging, restaging, follow-up of diseases and disorders, radiotherapy planning, as well as their therapeutic planning, and therapeutic outcome assessment. Images of lesions in the thorax, abdomen and pelvis are mostly affected by respiratory motion. Deviceless PET Respiratory gating system may be used with PET radiopharmaceuticals, in patients of all ages, with a wide range of sizes, body habitus and extent/type of disease.

The Cartesion Prime (PCD-1000A/3) V10.21 combines a high-end CT and a high-throughput PET designed to acquire CT, PET and fusion images.

The high-end CT system is a multi-slice helical CT scanner with a gantry aperture of 780 mm and a maximum scan field of view (FOV) of 700 mm. The high-throughput PET system has a digital PET detector utilizing SiPM sensors with temporal resolution of < 250 ps (238 ps typical). Cartesion Prime (PCD-1000A/3) V10.21 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 body with 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, detection, localization, evaluation, diagnosis, staging, restaging, follow-up of diseases and disorders, as well as their therapeutic planning, and therapeutic outcome assessment. This device can also function independently as a whole body multi-slice CT scanner.

The subject device incorporates the latest reconstruction technology, AiCE-i for PET (Advanced Intelligent Clear-IQ Engine- integrated), intended to improve image quality and reduce image noise for FDG whole body data by employing deep learning artificial neural network methods which can more fully explore the statistical properties of the signal and noise of PET data. The AiCE algorithm will be able to better differentiate signal from noise and can be applied to improve image quality and denoising of PET images compared to conventional PET imaging reconstruction.

A Deviceless PET Respiratory gating system has been implemented for use with the subject device. With this subject device, respiration is extracted using a pre-trained neural network. Respiratory-gated reconstruction is performed at a speed equal to or faster than that with "Normal".

Here's a breakdown of the acceptance criteria and study details for the Cartesion Prime PET-CT System, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance for Cartesion Prime PET-CT System (K251370)

The submission describes two primary feature enhancements: AiCE-i for PET (AiCE2) and Deviceless PET Respiratory gating system (DRG2).

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

For AiCE-i for PET (AiCE2) - Clinical Images:

• Sample Size: 10 PET DICOM clinical 18F-FDG whole body cases.
• Data Provenance: Not explicitly stated, but the submission notes "selected to cover characteristics common to the intended U.S. patient population." The training data for AiCE2 is mentioned to have over half acquired from the U.S.

For Deviceless PET Respiratory Gating (DRG2) - Clinical Images:

• Sample Size: 10 patients.
• Data Provenance: Not explicitly stated, but the submission notes "selected to cover characteristics common to the intended U.S. patient population." The training data for DRG2 was acquired entirely from the U.S.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

For AiCE-i for PET (AiCE2) - Clinical Images:

• Number of Experts: Three (3) physicians.
• Qualifications: At least 20 years of experience in nuclear medicine.

For Deviceless PET Respiratory Gating (DRG2) - Clinical Images:

• Number of Experts: Three (3) physicians.
• Qualifications: At least 20 years of experience in nuclear medicine.

4. Adjudication Method for the Test Set

The adjudication method is not explicitly stated as 2+1, 3+1, or none. However, for both clinical image evaluations, it states that "All three physicians reported/determined that..." This implies a consensus-based adjudication among the three experts was used to reach the conclusions. It does not indicate individual disagreements were arbitrated by a fourth reader.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

A formal MRMC comparative effectiveness study, designed to quantify the effect size of human readers improving with AI assistance, was not explicitly described in the provided text. The clinical image evaluations involved expert review and comparison, but the focus was on the algorithm's performance and image quality, not a direct measurement of human reader improvement with vs. without AI assistance.

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

Yes, standalone performance was extensively evaluated for both features:

• AiCE-i for PET:
  • Bench tests for pediatric use (CRC, BGV, CNR, artifacts, SUVmean equivalence).
  • Bench tests for image intensity (SUV max/mean accuracy, tumor volume improvement).
  • Phantom testing (NEMA NU-2, Adult and Pediatric NEMA phantoms, Small Pool phantom) comparing AiCE2 to AiCE1 and conventional methods across quantitative metrics (SUVmean, BGV, CRC, SNR) and for artifact absence.
• Deviceless PET Respiratory Gating:
  • Bench tests for AI operational mode (equivalence to external device gating, improvements in SUV max/mean, tumor volume).
  • Evaluation against predicate DRG1 using reconstructed clinical raw data and quantified outputs.

7. The Type of Ground Truth Used

• For AiCE-i for PET (AiCE2):
  • Phantom Studies: Objective, physics-based ground truth (e.g., known sphere sizes, activity concentrations) for quantitative metrics like SUV, CRC, BGV, SNR.
  • Clinical Image Evaluation: Expert consensus/opinion of three nuclear medicine physicians for subjective assessments like diagnostic quality, image sharpness, and noise levels.
• For Deviceless PET Respiratory Gating (DRG2):
  • Bench Tests/Comparison to DRG1: Quantitative measurements of SUV (max and mean) and tumor volume from reconstructed data, likely compared against a known or established ground truth from reference reconstructions.
  • Clinical Image Evaluation: Expert consensus/opinion of three nuclear medicine physicians for subjective assessments related to diagnostic quality and motion correction effectiveness.

8. The Sample Size for the Training Set

• For AiCE-i for PET (AiCE2): Subset assembled from FDG studies of sixteen (16) cancer patients.
• For Deviceless PET Respiratory Gating (DRG2): FDG studies of 27 cancer patients.

9. How the Ground Truth for the Training Set was Established

The text indicates that both AI algorithms (AiCE2 and DRG2) use deep learning artificial neural network methods. The ground truth for training these networks is implicitly derived from the input PET data itself, with the algorithms learning statistical properties of signal and noise or motion patterns.

• For AiCE-i for PET: The algorithm was "trained to automatically adapt to different noise levels to produce consistently high-quality images." This suggests the training data contained examples of both "noisy" input and perhaps "ideal" or "denoised" outputs (or features that guided the network to achieve denoised outputs with improved image quality), where the "ground truth" was likely the desired image characteristics or underlying signal.
• For Deviceless PET Respiratory Gating: The neural network was "trained on FDG studies... to extract motion information from acquired PET data and to generate a corresponding gating signal." This implies the "ground truth" for training involved identifying and characterizing respiratory motion within the raw PET data, possibly using external motion tracking data if available during training, or highly curated datasets where experts delineated motion patterns. The text does not explicitly state how this ground truth was established, only that it was trained on these patient studies.

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