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This device is indicated to acquire and display cross-sectional volumes of the whole the head, with the capability to image whole organs in a single rotation. Whole organs include, but are not limited to brain, heart, pancreas, etc.

The Aquilion ONE has the capability to provide volume sets of the entire organ. These volume sets can be used to perform specialized studies, using indicated software, of the whole organ by a trained and qualified physician.

FIRST is an iterative reconstruction algorithm intended to reduce exposure dose and improve high contrast spatial resolution for abdomen, pelvis, chest, cardiac, extremities and head applications.

AiCE is a noise reduction algorithm that improves image quality and reduces image noise by employing Deep Convolutional Network methods for abdomen, pelvis, lung, cardiac, brain, inner ear and extremities applications.

The Spectral Imaging System allows the system to acquire two nearly simultaneous CT images of an anatomical location using distinct tube voltages and/or tube currents by rapid KV switching. The X-ray dose will be the sum of the dose at each respective tube voltage and current in a rotation.

Information regarding the material composition of various organs, tissues, and contrast materials may be gained from the differences in X-ray attenuation between these distinct energies.

When used by a qualified physician, a potential application is to determine the course of treatment.

Aquilion ONE (TSX-306A/3) V10.4 with Spectral Imaging System is a whole body multi-slice helical CT scanner, consisting of a gantry, couch and a console used for data processing and display. This device captures cross sectional volume data sets used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician. This system is based upon the technology and materials of previously marketed Canon CT systems.

Here's a breakdown of the acceptance criteria and the study details for the Aquilion ONE (TSX-306A/3) V10.4 with Spectral Imaging System, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Bench Testing (Phantom Studies): The text indicates "phantoms" were used for various image quality and dose-related tests. Specific numbers of phantoms are not provided, nor is their provenance (obviously not country of origin for phantoms).
• Clinical Images: "Representative abdomen/pelvis, lung, extremity and cardiac Spectral Images" were obtained. The exact sample size (number of images or patients) is not specified. The provenance (e.g., country of origin, retrospective/prospective) for these clinical images is also not explicitly stated, beyond being "representative."

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

• Clinical Images: "An American Board Certified Radiologist" reviewed the clinical images. This indicates one expert.
• Qualifications: American Board Certified Radiologist. The number of years of experience is not specified.

4. Adjudication Method for the Test Set

• For the clinical images, the review was performed by a single American Board Certified Radiologist. This implies no multi-reader adjudication method (like 2+1 or 3+1). The radiologist's assessment directly served as the ground truth for diagnostic quality.

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 comparative effectiveness study is mentioned. The primary focus of the clinical image review was to confirm the diagnostic quality of the spectral images themselves, not to assess human reader performance with or without AI assistance. The device includes "AiCE is a noise reduction algorithm," but its impact on human reader performance is not evaluated in this submission documentation.

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

• Yes, the majority of the image quality metrics (CNR, CT Number Accuracy, Uniformity, SSP, MTF-Wire, SD, NPS, LCD, Dose Neutral, Noise Reduction, CNR Improvement, Iodine Reduction) were evaluated in a standalone fashion using phantoms. This represents an algorithm-only performance assessment against predefined physical criteria.

7. The Type of Ground Truth Used

• Bench Testing: The ground truth for the bench tests was derived from known phantom properties and measurements against those properties (e.g., known iodine concentrations in phantoms, measurements of noise, etc.). This is essentially physical/objective measurements against known standards.
• Clinical Images: The ground truth for the clinical images was expert opinion/consensus from a single American Board Certified Radiologist regarding diagnostic quality.

8. The Sample Size for the Training Set

• The document does not specify a sample size for the training set for any algorithms (like FIRST or AiCE). The submission focuses on verification and validation of the device changes, not the development or training of its internal algorithms.

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 (if applicable to the algorithms like AiCE, which uses Deep Convolutional Network methods) was established. This information would typically be detailed during the development and initial validation of such algorithms, not necessarily in a submission for an updated device version that incorporates these existing algorithms.

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