(84 days)
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) is a 3 Tesla Magnetic Resonance Imaging (MRI) System, previously cleared under K230355. 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.
The provided document describes a 510(k) premarket notification for a modified MRI system (Vantage Galan 3T, MRT-3020, V10.0 with AiCE Reconstruction Processing Unit for MR) by Canon Medical Systems Corporation. The primary purpose of this submission is to demonstrate substantial equivalence to a previously cleared predicate device (Vantage Galan 3T, MRT-3020, V9.0 with AiCE Reconstruction Processing Unit for MR, K230355) despite hardware and software changes.
The document primarily focuses on verifying that the changes do not adversely affect the device's safety and effectiveness and that the modified device maintains performance comparable to the predicate. It does not describe a study proving the device meets specific acceptance criteria in the context of diagnostic accuracy, particularly for an AI-assisted diagnostic device, as the "AiCE Reconstruction Processing Unit" is for image reconstruction, not for AI-based diagnosis.
Therefore, many of the requested fields related to diagnostic performance studies (like multi-reader multi-case studies, expert consensus ground truth, effect size of AI assistance for human readers, or standalone AI performance) are not applicable or not provided in this regulatory submission, as this is a modification of an imaging device itself, not a new AI diagnostic algorithm.
Based on the provided text, here's a breakdown of the requested information:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not present a formal table of "acceptance criteria" for diagnostic accuracy or clinical utility that an AI diagnostic algorithm would typically have, nor does it report performance metrics against such criteria. Instead, the testing focuses on ensuring the new features and hardware maintain image quality, safety, and functionality comparable to the predicate device.
However, the document does list testing performed for new features. We can infer the "acceptance criteria" for these were successful confirmation of functionality and image quality.
| Feature Tested | Acceptance Criteria (Inferred) | Reported Device Performance |
|---|---|---|
| 4D Flow | Accurate visualization of blood flow conditions when combined with external analytical software, including quantitative analysis (streamline, path line, velocity). Proper functioning of Cine or Retro modes with PS3D for time-phase information. | Bench testing included velocity measurement in a phantom with known flow values. Images in volunteers demonstrated velocity streamlines. (Implied: The system successfully produced the intended flow visualizations and quantitative data.) |
| Zoom DWI | Effective suppression of wraparound artifacts, reduction of image distortion, and provision of accurate ADC values for smaller FOV diffusion sizes by selective excitation and outer volume suppression (OVS). | Evaluated utilizing phantom images and representative volunteer images. Confirmed that Zoom DWI is effective for suppressing wraparound artifacts, reducing image distortion, and providing accurate ADC values. (Implied: The system successfully met these image quality objectives.) |
| 3D-QALAS | Acquisition of signals with FFE3D using T2prep pulse and IR pulse in combination. Production of multiple weighted images suitable for quantitative analysis using external analytical software. Image quality metrics (overall contrast, signal strength) comparable to reference images in literature. | Bench testing included scanning multiple volunteers. Three experienced reviewers compared the resulting multiple weighted images on image quality metrics (overall contrast and signal strength) against reference images published in the literature. (Implied: The image quality was found to be comparable and suitable for its intended use with external analytical software.) |
| General System | Safety parameters (Static field strength, Operational Modes, Safety parameter display, Operating mode access requirements, Maximum SAR, Maximum dB/dt, Potential emergency conditions and shutdown means) remain identical to the predicate device and comply with relevant IEC standards. Image quality (overall diagnostic capability) is maintained from the predicate device despite hardware/software changes. | Static field strength: 3T (Same as predicate). Operational Modes: Normal and 1st Operating Mode (Same as predicate). Safety parameter display: SAR, dB/dt (Same as predicate). Operating mode access requirements: Allows screen access to 1st level operating mode (Same as predicate). Maximum SAR: 4W/kg for whole body (1st operating mode specified in IEC 60601-2-33) (Same as predicate). Maximum dB/dt: 1st operating mode specified in IEC 60601-2-33 (Same as predicate). Potential emergency condition and means provided for shutdown: Shutdown by Emergency Ramp Down Unit for collision hazard for ferromagnetic objects (Same as predicate). "No change from the previous predicate submission, K230355" for imaging performance parameters. Risk analysis, verification/validation testing through bench testing demonstrate system requirements met. Image quality testing confirmed acceptance criteria met. Conclusion: Modifications do not change indications for use or intended use. Subject device is safe and effective for its intended use. |
2. Sample Size Used for the Test Set and Data Provenance
- 4D Flow: "a phantom with known flow values" and "volunteers." Specific numbers are not provided.
- Zoom DWI: "phantom images" and "representative volunteer images." Specific numbers are not provided.
- 3D-QALAS: "multiple volunteers." Specific numbers are not provided.
- Data Provenance: Not explicitly stated, but given Canon Medical Systems Corporation is based in Japan (manufacturer) and the U.S. (agent), it's likely a mix or either. The studies are described as "bench testing" and using "volunteers," implying prospective data collection for these specific tests.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
- 3D-QALAS: "three experienced reviewers" compared images. Their specific qualifications (e.g., "radiologist with 10 years of experience") are not detailed, but their role as "reviewers" suggests they are professionals qualified to assess image quality.
- Other features (4D Flow, Zoom DWI): The ground truth appears to be established by comparison to known phantom values or visual confirmation of expected image quality improvements (e.g., artifact suppression for Zoom DWI). No external "experts" beyond the testing team are mentioned for establishing ground truth in these cases, which is typical for image quality and functional assessments.
4. Adjudication Method for the Test Set
- For 3D-QALAS, comparison was made by "three experienced reviewers." The document does not specify an adjudication method (e.g., 2+1, 3+1 consensus). It simply states they "compared" the images.
- For other features, adjudication methods are not applicable as the "ground truth" relies on phantom measurements or visual confirmation against expected technical performance.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done
No, a MRMC study comparing human readers with and without AI assistance was not reported. This submission concerns hardware and image reconstruction software changes for an MRI system, not an AI diagnostic algorithm intended for human reader assistance in interpretation. The "AiCE Reconstruction Processing Unit" processes raw MR data into images, it does not interpret those images for diagnostic findings. Therefore, the effect size of human readers improving with AI vs without AI assistance is not relevant or measured here.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done
This refers to the performance of the image reconstruction itself. The testing described (e.g., for 4D Flow, Zoom DWI, 3D-QALAS) demonstrates the standalone technical performance of these new imaging capabilities and the AiCE reconstruction unit in producing images with desired characteristics (e.g., flow visualization, artifact suppression, specific contrast weighting). The "performance" is that the images are generated accurately according to the algorithms' design and meet technical quality metrics.
7. The Type of Ground Truth Used
- 4D Flow: Phantom with "known flow values" (objective physical ground truth) and visual assessment from "volunteer images."
- Zoom DWI: Phantom images and visual assessment from "volunteer images" (technical image quality and accuracy of ADC values).
- 3D-QALAS: Comparison against "reference images published in the literature" (literature-based reference) and assessment by "three experienced reviewers" on image quality metrics (expert qualitative assessment against a standard).
- General System Performance: Compliance with recognized consensus standards (e.g., IEC, NEMA) and comparison to the characteristics of the predicate device (regulatory/technical ground truth).
8. The Sample Size for the Training Set
The document does not describe a "training set" in the context of supervised machine learning for diagnostic tasks. The AiCE (Artificial intelligence Clear Engine) is mentioned as a "Reconstruction Processing Unit," suggesting it's an AI reconstruction algorithm, not an AI diagnostic algorithm. Image reconstruction algorithms may use learned models, but the source document does not provide details on their training data.
9. How the Ground Truth for the Training Set was Established
Not applicable, as a "training set" in the context of a diagnostic AI algorithm is not described. If the AiCE reconstructor uses a deep learning approach, its "training" would likely involve large datasets of raw MR data and corresponding high-quality reference images (e.g., from conventional reconstruction or higher-resolution scans) to learn the mapping from raw data to reconstructed images; however, this level of detail is not provided in a 510(k) summary focused on substantial equivalence of an entire MRI system.
§ 892.1000 Magnetic resonance diagnostic device.
(a)
Identification. A magnetic resonance diagnostic device is intended for general diagnostic use to present images which reflect the spatial distribution and/or magnetic resonance spectra which reflect frequency and distribution of nuclei exhibiting nuclear magnetic resonance. Other physical parameters derived from the images and/or spectra may also be produced. The device includes hydrogen-1 (proton) imaging, sodium-23 imaging, hydrogen-1 spectroscopy, phosphorus-31 spectroscopy, and chemical shift imaging (preserving simultaneous frequency and spatial information).(b)
Classification. Class II (special controls). A magnetic resonance imaging disposable kit intended for use with a magnetic resonance diagnostic device only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.