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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 device in question is the Supria True64 Whole-body X-ray CT System, intended for head, whole body, and vascular X-ray Computed Tomography applications in patients of all ages. This 510(k) submission (K183291) focuses on comparing it to the legally marketed predicate device, Supria True64 Whole-body X-ray CT System (K171738), particularly highlighting the addition of an ECG Prospective Scan (Axial) feature.

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly present a table of "acceptance criteria" in the typical sense of quantitative targets for clinical endpoints (e.g., sensitivity, specificity for a diagnostic algorithm). Instead, the acceptance criteria are implicitly defined by conformance to regulatory standards and demonstration of substantial equivalence to a predicate device, particularly regarding physical and performance characteristics and the new ECG Prospective Scan feature.

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

The document explicitly states: "Validation Testing - Clinical: None required".
This implies that no dedicated clinical test set (i.e., patient data) was used for proving the performance of the modified device (specifically the ECG Prospective Scan feature). The assessment primarily relied on bench testing and comparison to the predicate device's established performance. Therefore, there is no sample size for a clinical test set, nor specific data provenance (country of origin, retrospective/prospective) for such a set discussed.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

Since no clinical testing was performed and the evaluation was based on non-clinical performance and substantial equivalence to a predicate, there were no experts used to establish ground truth for a clinical test set. The "ground truth" for the non-clinical tests would be defined by engineering specifications and physical measurements, rather than clinical expert consensus.

4. Adjudication Method for the Test Set:

Given that no clinical test set was used, there was no adjudication method applied. The evaluation was based on conformance to engineering specifications and regulatory standards in bench testing.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done:

No MRMC comparative effectiveness study was done or reported. The submission explicitly states "Clinical: None required" for validation testing. Therefore, there is no reported effect size of how much human readers improve with AI vs. without AI assistance, as AI assistance is not the primary subject of this submission, nor is a reader study presented.

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

Yes, a standalone performance evaluation was conducted, in the sense that non-clinical bench tests were performed on the device itself.

• "Validation Testing - Bench" was conducted. The report states: "Based on the results of the ECG Prospective Scan Performance Testing Report contained in Verification and Validation Documentation of Section 9, Hitachi judged that Supria True64 with ECG Prospective Scan (Axial) performs to specifications."
• "ECG Prospective Scan feature was subject to performance tests, which confirmed that the X-ray was in synchronization with the ECG trigger and the acquired images. The results confirmed that images which are synchronized with ECG R-wave signal can be acquired by using ECG Prospective Scan."

These tests evaluate the intrinsic technical performance of the device's new feature, independent of a human operator's interpretation, making it akin to a standalone performance assessment for that specific function.

7. The type of ground truth used:

For the non-clinical bench testing, the ground truth was based on engineering specifications and physical validation of the device's functionality, particularly the synchronization of the X-ray with the ECG trigger and the resulting image acquisition. For the substantial equivalence argument, the ground truth was the established performance and safety of the predicate device (Supria True64 K171738) and compliance with recognized industry standards.

8. The sample size for the training set:

Not applicable. This submission describes a hardware device (CT system) and a software feature (ECG Prospective Scan) that is a modification to an existing cleared device. It is not an AI/ML algorithm that requires a "training set" in the computational sense. The "training" for such a system would involve engineering design and calibration, not data-driven model training.

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

Not applicable. As explained above, there is no AI/ML training set in this context. The "ground truth" for developing and calibrating such a CT system and its features would be based on established physics, engineering principles, and phantom measurements to ensure accurate imaging and synchronization.

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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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