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The Supria True64 system is indicated for head, whole body, and vascular X-ray Computed Tomography applications in patients of all ages. The images can be acquired in either axial, helical, or dynamic modes.

The volume datasets acquired by the Supria can be post processed by the system to provide additional information. Post processing capabilities included in the Supria software include CT angiography (CTA), Multi-planar reconstruction (MPR) and volume rendering.

Volume datasets acquired by the Supria can be transferred to external devices via a DICOM standard interface.

The guideShot Option adds a remote in-room display and controls to support interventional imaging. The device output can provide an aid to diagnosis when used by a qualified physician.

The Supria True64 is a multi-slice computed tomography system designed to perform multi-slice CT scanning supported by 64-detector technology. The system allows optimum clinical applications ranging from routine exams in response to the diversified circumstances in imaging whole body regions.

The Supria True64 system uses 64-slice CT technology, where the X-ray tube and detector assemblies are mounted on a frame that rotates continuously around the patient using slip ring technology. The solid-state detector assembly design collects up to 64 slices of data simultaneously. The X-ray sub-system features a high frequency generator. X-ray tube, and collimation system that produces a fan beam X-ray output. The system can operate in a helical (spiral) scan mode where the patient table moves during scanning. As the X-ray tube/detector assembly rotates around the patient, data is collected at multiple angles.

The collected data is then reconstructed into cross-sectional images by a high-speed reconstruction sub-system. The images are displayed on a Computer Workstation, stored, printed, and archived as required. The workstation is based on current PC technology using the Windows™ operating system.

The Supria True64 system consists of a Gantry, Operator's Workstation, Patient Table, High-Frequency X-ray Generator, and accessories.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Important Note: This submission is a 510(k) premarket notification for a new version of an existing device (Supria True64 CT system). The primary goal of a 510(k) is to demonstrate substantial equivalence to a previously legally marketed device (predicate device), not necessarily to establish novel clinical efficacy or new performance benchmarks against a specific disease. Therefore, the "acceptance criteria" here are largely focused on maintaining equivalent performance to the predicate and adhering to relevant standards for CT systems. The studies are primarily to confirm this equivalence.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" in a numerical or target-based fashion for all parameters. Instead, it relies heavily on demonstrating that the Supria True64's performance is "similar to" or "comparable to" the predicate device (HITACHI SUPRIA Whole-body X-ray CT System Phase 3, K163528) and complies with relevant industry standards.

Here's a table summarizing the implicit acceptance criteria and reported performance, derived from the "Performance Comparison" and "Technological Characteristic Differences" sections:

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

• Test Set Sample Size: The document does not specify a numerical sample size for the "clinical image examples." It only states "3 kinds of clinical image example."
• Data Provenance: The document does not explicitly state the country of origin or if the data was retrospective or prospective. Given the context of a 510(k) submission for a new device model, it's highly likely these "image examples" were selected from internal company testing or possibly from clinical partners, but specifics are not provided.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The document only states, "...which we judged to be sufficient to judge a clinical usability." This implies internal judgment, but no details on who made this judgment or their qualifications are given.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified. The phrase "we judged to be sufficient" suggests an internal decision, but no formal adjudication process (like 2+1, 3+1 consensus) is outlined.

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

• MRMC Study: No, a formal MRMC comparative effectiveness study is not mentioned. The clinical evaluation focuses on qualitative judgment of "clinical usability" and a comparison of FBP vs. Iterative Reconstruction for image quality, not on human reader performance with or without AI assistance.

6. Standalone Performance Study (Algorithm Only)

• Standalone Performance Study: Not explicitly. The device itself is an imaging system, not an AI algorithm performing a specific diagnostic task. The performance evaluation is for the imaging system's inherent capabilities (e.g., spatial resolution, noise, dose). The "Iterative Reconstruction" is an image processing algorithm, and its effect on image quality was evaluated, but not necessarily in a standalone study assessing its diagnostic accuracy independently.

7. Type of Ground Truth Used

• Type of Ground Truth: For the "clinical image examples," the ground truth establishment method is not detailed beyond "we judged to be sufficient to judge a clinical usability." For the bench testing, the ground truth is based on physical phantom measurements and established engineering metrics for CT performance (e.g., measuring noise in a uniform phantom, spatial resolution using a line pair phantom).

8. Sample Size for the Training Set

• Training Set Sample Size: Not applicable. This document describes a CT scanner (hardware and its integrated software for image acquisition and reconstruction), not a machine learning model that requires a "training set" in the conventional sense of AI/ML. The iterative reconstruction algorithms would have been developed and "trained" (in a broader sense of algorithm development and tuning) using various datasets, but these details are not provided in this 510(k) summary.

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

• Ground Truth for Training Set: Not applicable, as explained above. The development of the CT system's underlying physics models and reconstruction algorithms would rely on established scientific principles and engineering validation, rather than a "ground truth" derived from expert consensus on medical images that is typical for AI models.

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