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Attenuation correction is a method where the emission images acquired during SPECT acquisitions are compensated for attenuation within the patient. The purpose of attenuation correction is to compensate for varying attenuation of gamma rays in the body.

The intended use of the c.cam Attenuation Correction option is to provide the customer with means to measure the attenuation of gamma rays within the body of a patient, when using a c.cam gamma camera system for SPECT studies, and to map the measured attenuation into images that can be used to compensate emission images for the attenuation. The c.cam-AC includes additional acquisition of data for scatter correction of these images.

The c.cam-AC is an attenuation correction device, which will be marketed as an optional device for the c.cam gamma camera.

C.cam gamma camera is a dual detector system and the attenuation correction device (two units) will be mounted opposite of the two detectors, one device on each detector. The attenuation correction device is formed as two housings each containing 14 line sources in fixed positions, shutter and all shutter control.

The following described functions are performed on both detectors simultaneously.
When the scanning is initiated the emission image and the transmission images are acquired sequentially. When the emission image has been acquired, a shutter, common for all sources, located in the device housing will open for exposure from 14 line sources (max. 20 mCi each 153 Gd with a total of max. 96 mCi). A collimated beam of gamma ray photons is focused on the opposite detector field of view to create the transmission images of the patient placed in the field of view of the detector. Having acquired both emission and transmission images, the data for emission, transmission, emission scatter and transmission scatter are stored in one dataset. A scaled version of the blank transmission scan is stored in another dataset. The complete study can be export via DICOM to the OEM customer provided processing station for later processing and reviewing.

Here's a breakdown of the acceptance criteria and study information for the c.cam-AC device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text for K051460 describes a device that is essentially equivalent to existing devices and focuses on its design and radioactive source rather than specific performance metrics tied to acceptance criteria. The only performance-related data explicitly stated are for Radiation Leakage.

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

• Sample Size for Test Set: Not explicitly stated for performance evaluation that would lead to quantitative acceptance criteria beyond radiation leakage. The text mentions "acquiring Myocardial Perfusion stress studies on a Siemens e.cam system" as part of non-clinical test results collection, which implies a dataset, but the size is not specified.
• Data Provenance: The acquisition of "Myocardial Perfusion stress studies on a Siemens e.cam system" suggests clinical data. The country of origin is not specified, but the device manufacturer is Danish (3D, Danish Diagnostic Development A/S). The study appears to be retrospective in its description of collected clinical data.

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

This information is not provided in the document. The study described focuses on non-clinical tests and a comparison of technological characteristics to predicate devices. There is no mention of expert involvement in establishing ground truth for a test set in the context of diagnostic performance.

4. Adjudication Method for the Test Set

This information is not provided. As no expert-based ground truth establishment is described, an adjudication method is not applicable here.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not done or at least not described in the provided text. The device is an attenuation correction device, a component of a larger imaging system, and its evaluation centered on its technical performance and equivalence to predicate devices, not on direct human reader performance improvement.

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

The device itself, the c.cam-AC, is an algorithm/hardware combination designed for attenuation correction, which operates in a standalone manner as a component of the c.cam gamma camera system. The "non clinical test results have been collected by acquiring Myocardial Perfusion stress studies," suggesting the output of the attenuation correction was likely evaluated, but specific metrics of its standalone performance (e.g., accuracy of attenuation maps) are not provided beyond the radiation leakage. The focus is on its function within the entire imaging workflow.

7. The Type of Ground Truth Used

For the radiation leakage acceptance criteria, the ground truth would be established by physical measurement using appropriate radiation detectors. For the implicit functionality of attenuation correction, the "ground truth" for comparison would likely be the expected or desired attenuation map/corrected image quality, a comparison against which is implied in the "non clinical test results," but the specific methodology or precise ground truth used for that evaluation is not explicitly detailed.

8. The Sample Size for the Training Set

This information is not provided. The c.cam-AC device is described as an "attenuation correction device" and not explicitly as an AI/machine learning algorithm that would require a distinct "training set" in the modern sense. It implements a method to compensate for attenuation, which might involve pre-defined models or calculations, but not necessarily a trained AI model.

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

This information is not provided, as the document does not describe the use of a "training set" for an AI algorithm.

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