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The Vantage Pro Attenuation Correction Device option to gamma camera systems marketed by ADAC, a Philips Medical Systems Company, produces images that depict the anatomical density of a patient. The system is intended to provide an enhancement to the emission images acquired using the gamma camera system by correcting for attenuation effects in the patient. Vantage Pro includes additional quality assurance tools to evaluate the resulting density image.

Vantage™ Pro (with ExSPECT™ III a truncation compensation algorithm) is an attenuation correction device, which will be marketed as an optional addition to ADAC Laboratories Gamma Camera products or cameras markcted by ADAC.

Vantage Pro is an option that provides a patient's functional information, which is further improved by using the anatomical information, obtained using the external radioactive scanning line sources with special collimation to minimize patient exposure.

In operation, the Vantage system utilizes two 250 mCi, easily managed, shielded radioactive scanning line sources containing 153GD, which arc attached to an ADAC gamma camera or a gamma camera marketed by ADAC. They are positioned opposite each of the two dctectors with parallel hole collimation while in 90-degree orientation. When a shutter on the shicided source is opened, a collimated beam of gamma ray photons is focused at cach opposing detector to form a transmission image of a patient placed in the field of view. Simultaneously, emission data is collected from the patient. Both sets of data are acquired and processed to produce an attenuation corrected emission image via MLEM iterative processor to production than a conventional Single Photon Emission Computed Tomography (SPECT) image.

The MLEM reconstruction with attenuation correction includes image quality enhancements by correcting for the photopeak scatter via single energy window subtraction, downscatter via single energy window subtraction, and resolution recovery (RR). Also provided is a Post Acquisition QA tool that provides the uscr with feedback regarding the quality of the acquired images in that it alerts the user as to the level of any banding or truncation in the data as well as the level of counts, acquired in each data set.

The attenuation correction method includes a user-selectable Bayesian itcrative reconstruction method, or an ordered subsct expectation maximization (OSEM) method, for reconstructing the transmission map. The Bayesian method is an iterative method that reduces image noise. The key components in this method are using a gamma prior to constrain the results, and a gradient algorithm approach for determining updates. In addition, this method includes a prior for reducing noise in woor count regions and reducing biases in the soft tissue region in the transmission data set. Vantage Pro improves the Bayesian method by incorporating a new algorithm to compensate for truncation of the transmission map.

Vantage Pro (with ExSPECT III) is a modification of Vantage ExSPECT II. The algorithm modification updates the Bayesian attenuation correction algorithm to compensate for the truncated attenuation map (ExSPECT III) that may result from having a patient larger than the camera field of view.

The Post Acquisition QA tool consists of three components: banding detection, truncation detection, and count level determination. Banding refers to the presence of bright sections or strips in the acquired image data set caused by a highly irregular heartbeat. Truncation occurs when a portion of the patient's body is not fully captured within the dimensions of the acquired image. Reconstruction of an image data set which exhibits truncation or banding would yield inappropriate clinical data containing major artifacts. The Post Acquisition QA tool detects banding or truncation which allows the user to reassess the image. In addition, this tool informs the user whether the image contains sufficient count density that would cosure proper data reconstruction. These features are of informative nature only, so that the raw data is not altered. The user may then use the warning information, raw data, and their own clinical experience to procced with a diagnostic decision.

This 510(k) submission for the Vantage™ Pro Attenuation Correction Device does not include specific acceptance criteria or a dedicated study demonstrating the device's performance against those criteria. Instead, it focuses on demonstrating substantial equivalence to a predicate device, Vantage ExSPECT II (K992078).

The document states that the modified device, Vantage Pro, is substantially equivalent to the predicate device due to "identical indications for use, technological comparison and overall system performance." This implies that the performance of Vantage Pro is considered acceptable because it is comparable to an already legally marketed device.

However, the submission does mention inherent functionalities and improvements of the Vantage Pro that could be considered performance indicators, although not explicitly framed as acceptance criteria.

1. Table of Acceptance Criteria and Reported Device Performance

As explicit acceptance criteria are not provided, the table below will summarize the stated functions and improvements of the Vantage Pro.

2. Sample Size for Test Set and Data Provenance
The document does not describe a specific test set or clinical study with a defined sample size for directly evaluating the Vantage Pro's performance against stated acceptance criteria. The claim of "overall system performance" being comparable to the predicate suggests that performance was assessed, but details of the data and its provenance are not provided. Given the nature of a 510(k) submission focused on substantial equivalence, detailed clinical trial data specifically for the new device is often not required if equivalence can be established through technological comparison.

3. Number of Experts and Qualifications for Ground Truth
No information is provided regarding the number or qualifications of experts used to establish ground truth for a test set, as a dedicated performance study with a test set is not described.

4. Adjudication Method for Test Set
No adjudication method is described, as a formal test set for independent performance evaluation is not detailed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
There is no mention of an MRMC comparative effectiveness study being conducted or its results (e.g., effect size of human reader improvement with AI assistance). The submission focuses on the device as an add-on attenuation correction tool to a gamma camera system, rather than a diagnostic AI system requiring reader performance studies.

6. Standalone (Algorithm Only) Performance Study
The document describes the algorithm and its functions, such as attenuation correction, truncation compensation, and image quality enhancements. The device itself (Vantage Pro) is the "algorithm only" component that enhances the images from the gamma camera system. However, a standalone performance study as defined in a typical diagnostic device context (e.g., sensitivity, specificity for a specific clinical condition without human interaction) is not explicitly detailed with quantitative metrics. The focus is on the algorithmic improvements to image quality.

7. Type of Ground Truth Used
The document does not explicitly state the type of ground truth used for any performance evaluation. Given the device's function to correct for physical attenuation effects and enhance image quality, ground truth would likely refer to objective measures of image quality (e.g., phantom studies, accuracy of attenuation map reconstruction compared to known models, reduction of artifacts) rather than clinical outcomes or pathology for specific diseases. However, these specific studies are not detailed.

8. Sample Size for Training Set
The document does not provide any information regarding a training set size. While the Bayesian iterative reconstruction method is mentioned as being improved, details on how this improvement was developed or trained (if applicable in a machine learning sense for "training") are not provided.

9. How Ground Truth for Training Set was Established
No information is provided on how ground truth for a training set was established, as a training set is not explicitly mentioned or described in the context of machine learning model development. The modifications are described algorithmically (e.g., "a new algorithm to compensate for truncation," "modification has been introduced to the definition of and use of the initial estimate (prior) to allow activity outside of the field of view").

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