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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

• Flow dynamics
• Chemical Shift

•Chemical Shift

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

The Vantage Galan (Model MRT-3020/A9) is a 3 Tesla Magnetic Resonance Imaging (MRI) System, previously cleared under K181593. This system is based upon the technology and materials of previously marketed Canon Medical Systems and is intended to acquire and display cross-sectional transaxial, coronal, sagittal, and oblique images of anatomic structures of the head or body.

The provided text describes a 510(k) submission for a modified MRI system, the Vantage Galan 3T, MRT-3020/A9, V5.0. It aims to demonstrate substantial equivalence to a previously cleared device. However, the document primarily focuses on system modifications, safety parameters, and regulatory compliance, and does not contain explicit acceptance criteria or detailed results from a study proving the device meets specific performance criteria in the way a diagnostic AI/CADe would.

The document states: "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." The performance is generally evaluated in terms of image quality and safety, implying that the images produced by the new system should be diagnostically equivalent to those from the predicate device.

Given the information available, I can construct a table for "Acceptance Criteria" based on the safety and imaging performance claims made. The "Reported Device Performance" for this specific submission is mainly comparative to the predicate device, emphasizing that the new features do not negatively impact safety or general image quality for diagnostic purposes.

Here's an attempt to extract and present the requested information, with caveats where specific details are not provided in the document:

Acceptance Criteria and Study for Vantage Galan 3T, MRT-3020/A9, V5.0

The provided 510(k) summary for the Vantage Galan 3T, MRT-3020/A9, V5.0 concerns a modified Magnetic Resonance Imaging (MRI) system. The acceptance criteria for this type of device are primarily related to safety, image quality, and functional equivalence to its predicate device, rather than specific diagnostic accuracy metrics typically seen with AI/CADe devices. The study demonstrating compliance is implicitly the collection of verification and validation testing performed.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document mentions "volunteer clinical imaging" as part of the testing. However, it does not specify the sample size for this clinical imaging, nor does it provide details on the country of origin or whether the data was retrospective or prospective. It is likely the imaging was conducted specifically for the verification of the modified system's performance.

3. Number of Experts and Qualifications for Ground Truth

The document states, "When interpreted by a trained physician, these images yield information that can be useful in diagnosis." However, it does not specify the number of experts used to establish ground truth for any test set or their specific qualifications. For an MRI system, the "ground truth" for image quality assessment is often implicitly based on the ability of trained radiologists to make a diagnosis from the images without specific expert adjudication for a fixed dataset, assuming the image quality is acceptable.

4. Adjudication Method

The document does not describe an adjudication method (e.g., 2+1, 3+1). This level of detail is typically not required for an MRI system 510(k) submission unless there's a specific diagnostic accuracy claim being made (e.g., for a CADe device integrated into the MRI).

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

The document does not mention an MRMC comparative effectiveness study, nor does it quantify any effect size of human readers improving with AI vs. without AI assistance. This is expected as the device is an MRI scanner itself, not an AI/CADe diagnostic aid for interpretation.

6. Standalone (Algorithm only) Performance

The device is an MRI system, not a standalone diagnostic algorithm. Therefore, no standalone (algorithm-only) performance was conducted or reported in this context. The performance is inherently tied to the system's ability to acquire images for human interpretation.

7. Type of Ground Truth Used

For an MRI system, the "ground truth" for evaluating image quality and diagnostic utility is typically based on:

• Clinical usability/diagnostic interpretability by trained physicians: Images are evaluated for clarity, resolution, contrast, and freedom from artifacts, allowing for accurate anatomical visualization and diagnosis.
• Physical phantom measurements: Used to verify technical performance metrics like signal-to-noise ratio, spatial resolution, and geometric accuracy.
• Volunteer clinical imaging: Utilized to ensure the system performs as expected in a human subject, producing images suitable for diagnostic purposes.

The document implicitly refers to these types of evaluations.

8. Sample Size for the Training Set

The document does not specify a sample size for a training set. This is because the device is an MRI system, not an AI/ML algorithm that requires a "training set" in the conventional sense for diagnostic performance. Its development involves engineering design, safety testing, and image quality optimization.

9. How Ground Truth for the Training Set Was Established

Since there is no "training set" in the AI/ML context for this MRI system, the concept of establishing ground truth for a training set does not apply as described. Development and validation of the MRI system rely on established engineering principles, physics, phantom testing, and clinical evaluations for diagnostic image quality.

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