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Vantage Galan 3T systems are indicated for use as a diagnostic imaging modality that produces crosssectional 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 K192574. 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 AiCE Reconstruction Processing Unit for MR is included with this system for the processing of images for various anatomical regions.

1. Acceptance Criteria and Reported Device Performance

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

The human observer study included 60 subjects and a total of 348 scans. The provenance of this data (e.g., country of origin, retrospective or prospective) is not specified in the provided document.

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

The human observer study involved 15 board-certified radiologists/cardiologists. The document does not explicitly state that these experts established "ground truth" for the test set, but rather that they evaluated the images and demonstrated a statistical preference for AiCE. Their qualifications are listed as "board certified radiologists / cardiologists." No further details on their experience (e.g., 10 years of experience) are provided.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (such as 2+1 or 3+1) for the human observer study. It only states that 15 radiologists/cardiologists provided evaluations leading to a statistical preference.

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

Yes, a multi-reader multi-case (MRMC) comparative effectiveness study was conducted. The study involved 15 board-certified radiologists/cardiologists evaluating images from 60 subjects and 348 scans.

The effect size of how much human readers improve with AI vs. without AI assistance is not explicitly quantified in terms of a specific numerical improvement in accuracy or efficiency. Instead, the study "demonstrated a statistical preference of AiCE when compared to other performance filters," indicating improved perception or diagnostic confidence with AiCE, but without detailing the magnitude of this improvement or the specific metrics used for "preference."

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone performance study was done. The document states that "AiCE deep learning reconstruction underwent performance (bench testing) using a model observer study to determine that image low contrast detectability was maintained or improved, accompanied with other bench testing of SNR and contrast performance." This indicates an assessment of the algorithm's intrinsic image quality improvement without direct human interaction at that stage.

7. Type of Ground Truth Used

For the model observer study and bench testing, the ground truth appears to be based on objective image quality metrics such as "low contrast detectability," "SNR," and "contrast performance." These are inherent properties of the reconstructed images.

For the human observer study, the "ground truth" or reference for comparison was the performance of "other performance filters" (implicitly, the images reconstructed with these filters). The observers then indicated a "statistical preference" for AiCE, which served as the outcome measure. It isn't explicitly stated that the cases had a confirmatory diagnostic "ground truth" (e.g., pathology or outcomes data) that the radiologists were evaluating for accuracy. Rather, it focuses on the radiologists' perception and preference for the AiCE reconstructed images.

8. Sample Size for the Training Set

The document does not provide any information about the sample size used for the training set of the AiCE deep learning model.

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

The document does not provide any information on how the ground truth for the training set was established for the AiCE deep learning model.

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