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The ExacTrac 3rd Party system is intended to be used in conjunction with the MHI-TM2000 radiation therapy linear accelerator system manufactured by Mitsubishi Heavy Industries, Ltd.

ExacTrac 3rd Party uses the images received from the MHI-TM2000 linear accelerator for analyzing the current patient position and calculating - when applicable - a necessary correction shift. The correction shift is then exported to the MHI-TM2000 linear accelerator.

The ExacTrac 3rd Party system uses stereoscopic x-ray or cone beam CT registration and optical tracking of infrared reflective markers in order to localize and correct the patient position before and during treatment.

ExacTrac 3d Party is an Image Processing System for patient positioning on the MHI-TM2000 linear accelerator. It is based on an imported isocenter from a planning system or on an isocenter imported from a simulator. It allows verification and, if necessary correction of the patient's position. Correction of patient's position is based on a comparison of

• a) digital reconstructed images (DRR) calculated from a corresponding CT set (reference image) and xray images (live images) from the imaging system of the MHI-TM2000 linear accelerator with the patient on the treatment couch.
• b) the treatment planning CT and a Cone Beam CT taken on the imaging system of the MHJ-TM2000 linear accelerator with the patient on the treatment couch.

The x-ray images, accomplished as stereoscopic x-rays or a Cone Beam CT scan, are created by the corresponding kV x-ray imaging system of the MHI-TM2000 linear accelerator and loaded into the ExacTrac 3rd Party System. Structures on the images to be compared can be either anatomical landmarks or implanted internal markers. Based on the imaging data, ExacTrac 3rd Party determines the correct treatment position, which is then applied by couch motion of the MHI-TM2000 linear accelerator.

The provided document, K072046, is a 510(k) summary for the ExacTrac 3rd Party system. It's a regulatory submission to the FDA, not a detailed scientific study publication. Therefore, it primarily focuses on establishing substantial equivalence to a predicate device rather than presenting a full-fledged study with detailed acceptance criteria and performance metrics in the way a clinical trial report would.

Based on the provided text, here's what can be extracted and what information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria or detailed device performance metrics in a structured table. Given that this is a 510(k) summary, the "acceptance criteria" are implied by the claim of "substantial equivalence" to the predicate device, ExacTrac 4.0 (K040585). The validation and verification activities mentioned are intended to demonstrate that the new device performs at least as safely and effectively as the predicate.

The closest to "performance" stated is that the device "uses the images received from the MHI-TM2000 linear accelerator for analyzing the current patient position and calculating - when applicable - a necessary correction shift. The correction shift is then exported to the MHI-TM2000 linear accelerator." This describes its function rather than a specific performance metric like accuracy or precision.

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

This information is not provided in the document. The 510(k) summary states that "ExacTrac 3rd Party has been verified and validated according to BrainLAB's procedures for product design and development." However, it does not detail the specifics of these verification and validation tests, including the size of any test sets or the provenance of data used.

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

This information is not provided in the document.

4. Adjudication Method for the Test Set

This information is not provided in the document.

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

A multi-reader multi-case (MRMC) comparative effectiveness study is not mentioned or implied in the document. The focus is on the device's standalone function in patient positioning, not on how human readers interact with or improve with AI assistance from this specific device.

6. Standalone Performance

Yes, the information provided describes the device's standalone performance in its intended function. The "Device Description" details how ExacTrac 3rd Party "determines the correct treatment position" based on imaging data (stereoscopic x-rays or Cone Beam CT scans) without direct human-in-the-loop diagnostic interpretation that would involve reader performance. Its output (correction shift) is then applied by the linear accelerator. The validation and verification mentioned would have been to assess this standalone functionality.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for its validation. However, for a patient positioning system like ExacTrac, the ground truth would typically be established by:

• Physical Phantoms/Fiducials with Known Positions: In controlled laboratory settings, phantoms with precisely known positions of markers or anatomical landmarks are often used to assess accuracy.
• High-Resolution Imaging Modalities: Reference images often come from high-resolution CT scans or other precise imaging, which serve as the "ground truth" for anatomical positions.
• Mechanical Measurement Systems: Highly accurate mechanical measurement systems can sometimes be used as a reference to determine the true position of objects.

The document implicitly refers to "digital reconstructed images (DRR) calculated from a corresponding CT set (reference image)" and "the treatment planning CT." These serve as the reference or "ground truth" for comparison against live images from the imaging system.

8. Sample Size for the Training Set

This information is not provided in the document. A 510(k) summary generally focuses on the final validated device, not necessarily the details of its development, including training sets for algorithms. Given that this device uses image processing for registration, it would likely involve algorithms that were trained, but the details are omitted.

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

This information is not provided in the document. As with the test set, the nature of a patient positioning system suggests that ground truth for training would likely involve known anatomical structures or markers within imaging data.

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