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Vantage Galan 3T systems are indicated for use as a diagnostic imaging modality that produces cross-sectional transaxial, coronal, sagittal, and oblique images that display anatomic structures of the head or body. Additionally, this system is capable of non-contrast enhanced imaging, such as MRA.

MRI (magnetic resonance imaging) images correspond to the spatial distribution of protons (hydrogen nuclei) that exhibit nuclear magnetic resonance (NMR). The NMR properties of body tissues and fluids are:

·Proton density (PD) (also called hydrogen density)

• ·Spin-lattice relaxation time (T1)
• ·Spin-spin relaxation time (T2)
• ·Flow dynamics
• ·Chemical Shift

Depending on the region of interest, contrast agents may be used. When interpreted by a trained physician, these images vield information that can be useful in diagnosis.

The Vantage Galan (Model MRT-3020) is a 3 Tesla Magnetic Resonance Imaging (MRI) System, previously cleared under K203553. This system is based upon the technology and materials of previously marketed Canon Medical Systems and is intended to acquire and display crosssectional transaxial, coronal, sagittal, and oblique images of anatomic structures of the head or body.

This FDA 510(k) summary describes modifications to an existing device, the Vantage Galan 3T MRI system with AiCE Reconstruction Processing Unit, rather than a completely new device. Therefore, the "study" described focuses on validating that the changes do not negatively impact the device's performance or safety compared to the previously cleared predicate device.

Here's an analysis based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state acceptance criteria in a quantitative table format for performance metrics. Instead, it demonstrates through testing that the device modifications meet existing safety and image quality standards and are equivalent to the predicate device. The performance reports are qualitative confirmations that the changes do not degrade performance.

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

• Sample Size: The document mentions "phantom images" for image quality metrics and "volunteer clinical imaging" but does not specify numerical sample sizes for either.
• Data Provenance: The document does not specify the country of origin of the data. It mentions "bench testing" and "volunteer clinical imaging" as testing methods, implying a prospective collection for the purpose of this submission, but no further details are given.

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

The document does not provide details on experts used to establish ground truth for the test set. For a device like an MRI system with software modifications, ground truth validation usually involves quantitative image quality metrics using phantoms, and potentially qualitative assessment by radiologists in clinical imaging, but this is not explicitly detailed here.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method (e.g., 2+1, 3+1) for the test set.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or described in this document. The submission is for modifications to a cleared device and focuses on establishing substantial equivalence and safety, not on demonstrating improved diagnostic accuracy with human-AI assistance.

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

The focus of the testing mentioned (image homogeneity, distortion, SNR, low contrast, software functionality) primarily aligns with standalone algorithm performance as applied to image reconstruction and quality. The "AiCE Reconstruction Processing Unit" is an AI-powered reconstruction technology, and the tests would assess its direct impact on image characteristics. However, there isn't a specific section titled "standalone performance study" with detailed metrics outside of the general image quality evaluations.

7. The Type of Ground Truth Used

• For image quality metrics (homogeneity, distortion, SNR, low contrast), the ground truth is often established via physical phantoms with known properties and computational models.
• For software functionality, the ground truth is adherence to design specifications and system requirements.
• For clinical imaging validation, the "ground truth" often refers to a consensus reading by expert radiologists, but details here are absent.

8. The Sample Size for the Training Set

The document does not refer to a "training set" in the context of this submission. The AiCE Reconstruction Processing Unit (which likely uses AI) was part of the predicate device (K203553). This submission is for modifications to the system around that already cleared AI component (e.g., new pulse sequences, increased acceleration factors). Therefore, details about the training data for AiCE itself would have been part of the K203553 submission, not this one.

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

As explained above, this submission focuses on modifications to a previously cleared device. Information regarding the training set and its ground truth for the AiCE Reconstruction Processing Unit would have been established during the development and clearance of the predicate device (K203553). This document does not provide those details.

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