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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, 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 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, 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.

Cartesion Prime (PCD-1000A/3) V10.15 system 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

This document describes the marketing authorization for the Cartesion Prime (PCD-1000A/3) V10.15 system, which combines Positron Emission Tomography (PET) and X-ray Computed Tomography (CT). The submission focuses on two new features: AiCE-i for PET and Deviceless PET Respiratory-gating system.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics for AiCE-i for PET or the Deviceless PET Respiratory-gating system in a quantifiable manner (e.g., target SUV accuracy and achieved SUV accuracy).

However, for the Deviceless PET Respiratory-gating system, a qualitative assessment is provided.

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

• Deviceless PET Respiratory-gating system (Clinical Images Test Set): 10 patients.
• Data Provenance: The document does not explicitly state the country of origin or if the data was retrospective or prospective. It refers to "clinical images," which typically implies prospective data collection, but this is not explicitly confirmed.

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

• Number of Experts: Three physicians.
• Qualifications: "having at least 20 years of experience in nuclear medicine."

4. Adjudication Method for the Test Set

The document states: "All three physicians determined that all images were of diagnostic quality and images with deviceless had better or same performance as non-gated images or device-based gated images." This implies a consensus-based adjudication method, where all three experts had to agree on the diagnostic quality and comparative performance. It doesn't specify if a majority rule (e.g., 2+1) or an independent assessment followed by discussion was used, but the phrasing "All three physicians determined" suggests a unanimous agreement or a strong consensus.

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

Yes, a comparative effectiveness study involving human readers (the three physicians) was performed for the Deviceless PET Respiratory-gating system.

• Effect Size: The document qualitatively states that the deviceless system "had better or same performance as non-gated images or device-based gated images" and that "quantitative parameters such as accuracy of SUV (max and mean) and tumor volume, is improved over the current device-based gating method." However, no specific numerical effect size or statistical measure of improvement is provided (e.g., % improvement in SUV accuracy, statistically significant difference in diagnostic confidence scores).

6. If a Standalone Study (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• AiCE-i for PET: The description of AiCE-i for PET implies a standalone algorithm's performance in improving image quality and denoising. However, no specific standalone study details (e.g., metrics, dataset) are provided in this summary.
• Deviceless PET Respiratory-gating system: Bench tests were conducted to demonstrate substantial equivalence and improvement in quantitative parameters (SUV max/mean, tumor volume) against device-based gating. This suggests an evaluation of the algorithm's output without direct human interpretation in that specific test phase, although the subsequent clinical image review involved human readers. Therefore, yes, a standalone performance evaluation for quantitative parameters was performed for the Deviceless PET Respiratory-gating system.

7. The Type of Ground Truth Used

• Deviceless PET Respiratory-gating system (Clinical Images Test Set): The ground truth for the clinical image review was based on expert consensus (the three physicians' determination of diagnostic quality and comparative performance). The statement regarding "accuracy of SUV (max and mean) and tumor volume" in bench testing implies the use of a quantitative ground truth, likely derived from established phantom measurements or gold-standard methodologies, though not explicitly detailed.

8. The Sample Size for the Training Set

The document does not provide information on the sample size for the training set for either AiCE-i for PET or the Deviceless PET Respiratory-gating system.

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

The document does not provide information on how the ground truth for the training set was established.

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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, 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 an input PET image. The AiCE algorithm can be applied to improve image quality and denoising of PET images.

Cartesion Prime (PCD-1000A) V10.8 system 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

Here's a breakdown of the acceptance criteria and the study information for the Canon Medical Systems Corporation's Cartesion Prime (PCD-1000A/3) V10.8 with AiCE-i for PET, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The document primarily describes a series of tests conducted to demonstrate the improvement provided by AiCE-i for PET rather than explicit "acceptance criteria" with defined numerical thresholds. However, we can infer the performance metrics and the reported outcomes from the "Testing" section.

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

• NEMA NU 2-2018 & Phantom Studies (Artifact Check, Quantification Accuracy, Preservation of Quantification): These studies used phantoms (e.g., IEC Body phantom, NEMA NU 2-2018 phantom). The number of phantoms is not specified.
• Clinical Data Check & PSNR Measurements:
  • Test Set Size: 5 patients for visual inspection by physicians.
  • Data Provenance: Clinical data was used. For PSNR measurements, "clinical data of long scan duration that is not used in the DCNN training process" was utilized. The country of origin is not specified but is likely internal data from Canon Medical Systems based on the context of the submission. The studies appear to be retrospective as they involve existing clinical data for evaluation.

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

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

4. Adjudication Method for the Test Set

The document states: "All three physicians determined that all five AiCE-i for PET images were of diagnostic quality. Overall image quality, image sharpness and image noise were determined to be either improved or significantly improved in AiCE-i for PET images when compared to Gaussian images, with the exception of image noise, where one physician determined that the noise in AiCE-i for PET images is about the same as Gaussian images."

This suggests a consensus or majority opinion approach. While not explicitly stated as "2+1" or "3+1", the fact that "all three physicians determined" diagnostic quality implies a unanimous decision for that criterion. For image quality aspects (sharpness, noise), it acknowledges a single dissenting opinion but emphasizes the overall improvement. So, we can infer a form of consensus-based adjudication, with individual expert opinions recorded.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was it done? A limited MRMC-style evaluation was done with 3 physicians reviewing 5 cases. However, it was focused on assessing image quality improvements and diagnostic utility, not specifically on comparative effectiveness with vs. without AI assistance in terms of diagnostic accuracy or reader performance metrics.
• Effect size of human reader improvement: The document does not report a specific effect size in how much human readers improve with AI vs. without AI assistance in terms of diagnostic performance metrics (e.g., AUC, sensitivity, specificity). The physicians assessed image quality and diagnostic utility, concluding improvement and diagnostic quality, but not a quantifiable improvement in diagnostic accuracy compared to a baseline without AI.

6. Standalone (i.e., algorithm only without human-in-the-loop performance) Study

Yes, standalone performance was evaluated through various bench tests and phantom studies:

• NEMA NU 2-2018 Image Quality (CRC, BGV, Lung Residual Error measured on phantoms)
• AiCE-i for PET Phantom Artifact Check (visual inspection of phantom images)
• AiCE-i for PET Quantification Accuracy (phantom study)
• AiCE-i for PET Preservation of Quantification (phantom study)
• AiCE-i for PET PSNR Measurements (using clinical data, comparing algorithm output to long scan duration images)
• Statements like "AiCE significantly improved Signal to Noise Ratio, improved quantification at the same noise, reduced the count rates while preserving noise" demonstrate standalone algorithmic performance.

7. Type of Ground Truth Used

• Phantom Studies: The ground truth is the known physical properties and activity distribution within the phantoms.
• Clinical Data (Visual Inspection & PSNR):
  • For the visual inspection by physicians, the ground truth for diagnostic quality and image characteristics was expert consensus/opinion.
  • For PSNR measurements, the ground truth was considered the "long scan duration image," which represents a reference image with higher signal and lower noise due to extended acquisition time, against which the processed images were compared for similarity. This acts as a proxy for an ideal image.

8. Sample Size for the Training Set

The document mentions that "clinical data of long scan duration that is not used in the DCNN training process" was used for PSNR measurements. However, the sample size for the training set itself is not specified in the provided text.

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

The document does not explicitly state how the ground truth for the training set was established. It only implies that a "Deep Learning Artificial Neural Network" (DCNN) was trained. Typically, for such AI systems in medical imaging, training data ground truth is established through:
* Expert annotations/labels: Radiologists or nuclear medicine physicians marking regions of interest, identifying pathologies, or rating image quality.
* Higher quality reference scans: Using longer acquisition times or different imaging modalities as a "gold standard" for what an ideal image should look like for noise reduction and image enhancement tasks (as was partially done for the test set PSNR comparison).

Without further information, the specific method for training ground truth establishment remains undisclosed in this document.

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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, 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.

Cartesion Prime, PCD-1000A, V10.7 system 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 scanning field of 700 mm. The highthroughput PET system has a digital PET detector utilizing SIPM sensors with temporal resolution of

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics. Instead, it describes performance improvements and successful testing against standards. However, key performance indicators and improvements mentioned can be synthesized into a pseudo-acceptance table based on comparisons to the predicate device and improved image quality.

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

The document does not specify a patient sample size for the test set. The testing described primarily involves "bench testing." It mentions improvements in image quality (Signal-to-Noise Ratio, noise, contrast, spatial resolution) with various features and functionalities. The data provenance is not explicitly stated as patient data from a specific country or as retrospective/prospective. The description suggests testing was conducted on phantoms or simulated data suitable for bench testing, rather than human subjects.

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

The document does not mention the use of experts to establish ground truth for a test set. This is consistent with the nature of the described "bench testing," which would typically involve objective measurements against known physical standards or simulated conditions, rather than expert interpretation of medical images.

4. Adjudication Method for the Test Set:

No adjudication method is mentioned, as expert interpretation and ground truth establishment (as typically understood in clinical studies) are not described for the test set.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done based on the provided information. The submission focuses on device modifications and performance improvements validated through bench testing, rather than human reader performance studies.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:

The testing described appears to be a standalone (algorithm only without human-in-the-loop performance) study, specifically focusing on the technical performance characteristics of the device and its included software features. The document highlights improvements in image quality metrics (SNR, noise, contrast, spatial resolution) directly attributable to the device's algorithms and hardware, without involving human interpretation of the output.

7. Type of Ground Truth Used:

The type of ground truth used appears to be based on objective physical measurements and established imaging science principles relevant to the technical specifications of a PET/CT system. For example, "Signal-to-Noise Ratio," "noise," "contrast," and "spatial resolution" are quantifiable metrics. The improvements observed with CaLM, PSF correction, and gating are against an implicit baseline or ideal performance under controlled bench testing conditions, rather than against clinical outcomes, pathology, or expert consensus on patient cases.

8. Sample Size for the Training Set:

The document does not provide any information regarding a training set size. This indicates that the validation performed for this submission was not based on machine learning model training and evaluation using labeled datasets in the traditional sense, but rather on direct performance testing of the device's imaging capabilities and software features.

9. How Ground Truth for the Training Set Was Established:

As no training set is mentioned, the method for establishing its ground truth is not applicable in this context.

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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, 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.

Cartesion Prime, PCD-1000A, system 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 scanning field of 700 mm. The high-throughput PET system has a digital PET detector utilizing SiPM sensors with temporal resolution of 280 ps. Cartesion Prime, PCD-1000A 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 does not contain acceptance criteria or a study proving that an AI/algorithm-based device meets specific acceptance criteria.

The document is a 510(k) summary for a PET/CT imaging system (Cartesion Prime, PCD-1000A). It describes the device, its intended use, and argues for its substantial equivalence to previously cleared predicate devices. While it mentions "testing" and "risk analysis and verification/validation testing," this largely refers to standard engineering and performance testing of the imaging hardware and software, rather than a clinical study evaluating an AI/algorithm's diagnostic performance against established criteria and ground truth.

Specifically, the document focuses on:

• Device Description: The physical and functional characteristics of the PET/CT scanner.
• Indications for Use: What the device is intended for (diagnostic imaging, assessment of metabolic/physiologic functions, aiding in evaluation, diagnosis, therapeutic planning, outcome assessment for various diseases).
• Substantial Equivalence: A comparison of the subject device's technical specifications (PET sensitivity, timing resolution, CT detector, etc.) with predicate devices to demonstrate it performs similarly.
• Safety and Standards Compliance: Adherence to quality systems regulations (ISO 13485, 21 CFR 820) and various IEC/NEMA standards, as well as general statements about software documentation and cybersecurity.
• Testing (General): "Risk analysis and verification/validation testing conducted through bench testing" and that "PET image quality metrics were performed which validated that the subject device met established specifications for spatial resolution, sensitivity, NECR, energy/timing resolution and PET/CT alignment." This is about the scanner's image quality and technical performance, not an AI's diagnostic accuracy.

There is no mention of:

• An AI/algorithm component requiring specific performance evaluation.
• Diagnostic performance metrics (e.g., sensitivity, specificity, AUC) for an AI.
• Human readers, MRMC studies, or human-in-the-loop performance.
• Ground truth establishment methods for a diagnostic algorithm.
• Training or test sets for an AI.

Therefore, I cannot fulfill the request to describe the acceptance criteria and the study that proves an AI/algorithm device meets those criteria based on the provided text. The document is about a medical imaging device (a PET/CT scanner), not an AI/algorithm that performs diagnostic tasks.

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