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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 yield information that can be useful in diagnosis.

The Vantage Galan (Model MRT-3020/A7) is a 3 Tesla Magnetic Resonance Imaging (MRI) System, previously cleared under K173382. 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.

Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the Vantage Galan 3T, MRT-3020/A7, V5.0 MRI system.

It's important to note that this document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device. It does not present a detailed clinical study with specific acceptance criteria and outcome metrics for diagnostic performance in the way a novel AI-driven diagnostic device might. Instead, it focuses on safety and imaging performance parameters (e.g., field strength, SAR, dB/dt) and software functionalities.

The document states: "No change from the previous predicate submission, K173382." regarding imaging performance parameters. This implies that the device's fundamental imaging quality and diagnostic capability are considered equivalent to the predicate, and therefore, no new studies specifically on diagnostic accuracy for new software features are presented in this summary. The "acceptance criteria" discussed below are thus primarily related to safety, operational limits, and maintaining imaging capabilities.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not specify a distinct "test set" in the context of a clinical trial for diagnostic performance. The evaluation is primarily focused on engineering and safety verification, implying performance on various physical and phantom tests, alongside some human volunteer imaging.

• Sample Size for Test Set: Not explicitly stated. The document mentions "volunteer clinical imaging" but does not quantify the number of volunteers or cases used for demonstrating the performance of the new software features. It is likely that these were smaller-scale internal evaluations.
• Data Provenance: Not explicitly stated, but the manufacturing site is Canon Medical Systems Corporation, Japan (1385 Shimoishigami Otawara-Shi, Tochigi-ken, Japan 324-8550). The "volunteer clinical imaging" would likely have occurred within a research setting affiliated with the manufacturer or a collaborating institution. The data would be considered prospective for the purposes of these internal evaluations.

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

This information is not provided in the 510(k) summary. The evaluation focuses on technical performance and safety, rather than providing a detailed diagnostic accuracy study requiring a traditional "ground truth" derived from expert reads. For the "volunteer clinical imaging" and phantom studies, the "ground truth" would be established by technical specifications, established imaging principles, and potentially expert review of image quality by radiologists or MR physicists within the development team, but this is not explicitly documented.

4. Adjudication Method for the Test Set

This information is not provided. Given the nature of a 510(k) summary for a software update to an MRI system, a formal adjudication process involving multiple readers for diagnostic consensus is not a typical requirement unless there are specific claims about diagnostic accuracy changes which are not made here.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, If So, What Was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

No, an MRMC comparative effectiveness study was not done as described in this document. The device is a Magnetic Resonance Diagnostic Device (MRDD) for acquiring and displaying images. While it includes "AI-like" features (e.g., KneeLine+, SpineLine+ for automated plane detection or k-t SPEEDER for image acceleration), these are enhancements to image acquisition and processing, not a traditional "AI" intended for diagnostic interpretation that would involve comparative reader studies against human readers. There are no claims of diagnostic improvement for human readers using this updated software.

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

No, a standalone algorithm performance study as typically understood for an AI diagnostic device was not done or reported. The software features are integrated into the MRI system to improve scan workflow, image quality, and efficiency, not to provide standalone diagnostic interpretations.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The document does not explicitly state the type of "ground truth" used for clinical evaluation of the software enhancements. However, based on the context of a 510(k) for an MRI system software update, the "ground truth" for evaluating the performance of features like KneeLine+, SpineLine+, or k-t SPEEDER would likely involve:

• Technical Specifications / Phantoms: For assessing parameters such as image resolution, signal-to-noise ratio, artifact reduction, and acquisition speed.
• Established Anatomical Knowledge / Expert Review of Image Quality: For features like KneeLine+ and SpineLine+, the "ground truth" for auto-detection accuracy would be based on expertly determined anatomical landmarks and planes within the acquired images, reviewed by experienced MR technologists or radiologists for accuracy and consistency against manual methods.
• Physiological Measurements: For R-wave monitoring, the ground truth would be from a concurrently acquired ECG signal.

8. The Sample Size for the Training Set

This information is not provided. The document describes software updates and enhanced sequences. While these might employ machine learning algorithms for features like anatomical detection (KneeLine+, SpineLine+), the specific data used for training these algorithms and their sample sizes are not detailed in this 510(k) summary. Given the focus on substantial equivalence, this level of detail is typically not required unless the core diagnostic function is driven by novel AI-learning.

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

This information is not provided. Similar to the training set sample size, the specifics of how ground truth might have been established for any potential machine learning components within the new software features are not disclosed in this regulatory submission.

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