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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 K241496. 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 document describes a 510(k) premarket notification for the Vantage Galan 3T, MRT-3020, V10.0 with AiCE Reconstruction Processing Unit for MR. This submission concerns a modification to an already cleared device, primarily involving the addition of a standard gradient system and the extension of the Precise IQ Engine (PIQE) to new scan families, weightings, and anatomical areas.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of quantitative acceptance criteria for PIQE performance. Instead, it describes acceptance in qualitative terms based on expert review.

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

• Test Set Sample Size: 106 unique subjects.
• Data Provenance: Two sites in the USA and one in Japan. This data is described as "separate from the training data sets." The document states that the multinational study population is expected to be representative of the intended US population for PIQE, as PIQE is an image post-processing algorithm not disease-specific or dependent on acquisition parameters that might be affected by population variation. Comparisons were internal (each subject as its own control).

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

• Number of Experts: 14 USA board-certified radiologists and cardiologists (3 reviewers per anatomy).
• Qualifications: "USA board-certified radiologists and cardiologists." Specific experience levels (e.g., years of experience) are not provided.

4. Adjudication Method for the Test Set

The document describes a scoring process by multiple reviewers but does not specify a formal adjudication method (e.g., 2+1, 3+1). It states: "scored by 3 reviewers per anatomy in various clinically-relevant categories... Reviewer scoring data was analyzed for reviewer agreement and differences between reconstruction techniques using Gwet's Agreement Coefficient and Generalized Estimating Equations, respectively." This suggests that the scores from the three reviewers were aggregated and analyzed statistically, rather than undergoing a consensus or tie-breaking adjudication process for each individual case.

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

• MRMC Study: Yes, a multi-site, randomized, blinded clinical image review study was conducted.
• Effect Size (AI-assisted vs. without AI assistance): This was not an AI-assisted reader study. The study compared images reconstructed with the conventional method (matrix expansion with Fine Reconstruction and typical clinical filter) against images reconstructed with PIQE. The purpose was to evaluate the image quality produced by PIQE, not to assess reader performance with or without AI assistance. Therefore, no effect size on human reader improvement with AI assistance is reported. The study aimed to demonstrate that PIQE-reconstructed images are clinically acceptable and offer benefits like sharpness and ringing reduction.

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

Yes, a standalone performance evaluation of the PIQE algorithm was conducted through "bench testing." This involved evaluating metrics like Edge Slope Width, Ringing Variable Mean, Signal-to-Noise ratio, and Contrast Change Ratio on typical clinical images from various anatomical regions. This bench testing demonstrated that PIQE "generates images with sharper edges while mitigating the smoothing and ringing effects and maintaining similar or better contrast and SNR."

7. The Type of Ground Truth Used

• For Bench Testing: The "ground truth" implicitly referred to established quantitative image quality metrics (Edge Slope Width, Ringing Variable Mean, Signal-to-Noise ratio, and Contrast Change Ratio) and comparisons against conventional reconstruction methods.
• For Clinical Image Review Study: The "ground truth" was established by expert consensus/evaluation, where 14 board-certified radiologists and cardiologists scored images on various clinically-relevant categories (ringing, sharpness, SNR, overall IQ, and feature conspicuity) using a modified 5-point Likert scale.

8. The Sample Size for the Training Set

The document explicitly states that the "106 unique subjects... from two sites in USA and one in Japan... were scanned... to provide the test data sets (separate from the training data sets)." The sample size for the training set is not provided in the document.

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

The document does not provide information on how the ground truth for the training set was established, as details about the training set itself are omitted.

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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) 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.

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.

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Vantage Fortian/Orian 1.5T 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 Fortian (Model MRT-1550/ WK, WM, WQ)/Vantage Orian (Model MRT-1550/ A3, A4, A7, A8) is a 1.5 Tesla Magnetic Resonance Imaging (MRI) System. These Vantage Fortian/Orian models use 1.4 m short and 4.1 tons light weight magnet. They include the Canon Pianissimo Zen technology (scan noise reduction technology). The design of the gradient coil and the whole-body coil of these Vantage Fortian/Orian models provide the maximum field of view of 55 x 55 x 50 cm and include the standard gradient system.

The Vantage Orian (Model MRT-1550/ UC, UG, UH, UK, UL, UO, UP, AK, AL, AO, AP, Upgrade only: A3, A4, A7, A8, AC, AD, AG, AH) is a 1.5 Tesla Magnetic Resonance Imaging (MRI) System. The Vantage Orian models MRT-1550/ UC, UG, UH, UK, UL, UO, UP, Upgrade only: A3, A4, A7, A8 use 1.4 m short and 4.1 tons light weight magnet while the Vantage Orian models MRT-1550/ AK, AL, AO, AP, Upgrade only: AC, AD, AG, AH use 1.4 m short and 3.8 tons light weight magnet. All of the aforementioned models include the Canon Pianissimo™ and Pianissimo Zen technology (scan noise reduction technology). The design of the gradient coil and the whole-body coil of these Vantage Orian models provide the maximum field of view of 55 x 55 x 50 cm. The Model MRT-1550/ UC, UD, UG, UH, UK, UL, UO, UP, AK, AL, AO, AP includes the XGO gradient system. The Model MRT-1550/ A3, A4, A7, A8, AC, AD, AG, AH include the standard gradient system.

This system is based upon the technology and materials of previously marketed Canon Medical Systems MRI 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 Vantage Fortian/Orian MRI System is comparable to the current 1.5T Vantage Fortian/Orian MRI System (K222968), cleared October 25, 2022, with the following modifications.

Here's a breakdown of the acceptance criteria and study information for the device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

This table focuses on the specific features detailed in the "Testing" section of the document, as these are the ones with explicit performance criteria and evaluation results.

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

• PIQE (Precise IQ Engine):

  • Sample Size: 36 unique subjects (patients).
  • Data Provenance: Two sites in France.
  • Nature of Data: Clinical cases, prospectively collected for the study (scanned to provide test data sets separately from training data).

• NeuroLine+:

  • Sample Size: 15 clinical cases (4 male, 11 female).
  • Data Provenance: France (implied from national identities, although not explicitly stated as collected in France).
  • Nature of Data: Clinical images, newly collected, and entirely separate from the training group.

• Iterative Motion Correction (IMC):

  • Bench Testing: 12 clinical datasets (without subject motion and with mathematically simulated motion added). Data provenance not specified.
  • Clinical Image Review (Phase 1): 18 volunteers. Data provenance not specified.
  • Clinical Image Review (Phase 2): 49 image volumes from 15 typical clinical patients with pathology and motion. Data provenance not specified.
  • Nature of Data:
    • Bench testing: Clinical datasets with simulated motion.
    • Clinical review (Phase 1): Volunteers imaged with and without motion.
    • Clinical review (Phase 2): Clinical patients with pathology and motion.
    • All testing data acquired separately and independently from training data.

• Free Breathing Dynamic DLR:

  • Arterial Phase Detection: 11 clinical cases (5 male, 5 female, 1 unknown).
  • Clinical Image Review: 29 contrast-enhanced Free Breathing Dynamic liver studies from 29 patients (14 male, 14 female, 1 unknown).
  • Data Provenance: France and USA for arterial phase detection; United States, France, and Japan for clinical image review.
  • Nature of Data:
    • Arterial phase detection: Clinical images from patients receiving clinically indicated contrast.
    • Clinical image review: Contrast-enhanced Free Breathing Dynamic liver studies.
    • All testing data acquired separately and independently from training data.

• Exsper 3D, Slice Shim, UTE CG Recon, Ringing Correction, Auto Scan Assist, Ceiling Camera:

  • The text states "phantom images" or "clinical images" were used, but specific sample sizes and data provenance for these components (beyond PIQE, NeuroLine+, IMC, and Free Breathing Dynamic DLR, which are highlighted deeper) are not detailed.

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

• PIQE (Precise IQ Engine):

  • Number of Experts: 6 radiologists (3 per anatomy - brain and knee).
  • Qualifications: USA board certified radiologists.

• NeuroLine+:

  • Number of Experts: 2.
  • Qualifications: Experienced ARRT licensed MR technologists (for manual annotation of target planes).

• Iterative Motion Correction (IMC):

  • Number of Experts: 3.
  • Qualifications: US board certified radiologists, specializing in neuro imaging.

• Free Breathing Dynamic DLR:

  • Arterial Phase Detection: "Experienced radiologists" (number not specified for ground truth selection).
  • Clinical Image Review: 2 US board certified radiologists.

4. Adjudication Method for the Test Set

• PIQE (Precise IQ Engine): Randomized, blinded to the reviewers. Scored by 3 reviewers per anatomy. The text implies a consensus or averaging approach for reaching conclusions, as it states "all scored at, or above, clinically acceptable by three board-certified Radiologists per anatomy" and "reviewers exhibited a strong agreement".
• NeuroLine+: Manual annotation by two experienced ARRT licensed MR technologists. The text implies their annotations served as the reference for accuracy, but does not detail an adjudication process if disagreements occurred.
• Iterative Motion Correction (IMC): Randomized and blinded review by 3 US board certified radiologists.
• Free Breathing Dynamic DLR:
  • Arterial Phase Detection: "Gold standard phase as manually selected by experienced radiologists." (Implies expert consensus or selection, but not specific adjudication).
  • Clinical Image Review: 2 US board certified radiologists read and scored the images. The conclusion is based on the "average of visual scores."

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.

• Yes, MRMC studies were done for PIQE, IMC, and Free Breathing Dynamic DLR.
  • These studies involved multiple readers assessing multiple cases, and comparing images processed with the new AI/DL features (PIQE, IMC, Free Breathing Dynamic DLR) against conventional methods or images without the feature applied.
  • **However, these studies were designed as a comparison of image quality from the new AI-enhanced reconstruction/correction methods versus conventional methods, not a comparative effectiveness study measuring human reader improvement with AI assistance vs. without AI assistance (i.e., human-in-the-loop performance). The radiologists were evaluating the reconstructed images themselves, not their diagnostic performance with and without AI tools integrated into their workflow.
  • Effect Size: The document does not provide a quantitative effect size in terms of how much human readers improve with AI assistance. Instead, it reports on the quality of the AI-processed images relative to conventional images, often concluding that the AI-processed images are "same or better," "meet acceptance criteria" for clinical acceptability (e.g., Likert scores), or provide "significant reduction" in artifacts.
    • PIQE: "Confirmed PIQE generates higher spatial in-plane resolution images...contributes to ringing artifact reduction, denoising and increased sharpness... is able to accelerate scanning by reducing the acquisition matrix only, while maintaining clinical matrix size and image quality."
    • IMC: "Significantly reducing motion artifacts, and improving overall image quality metrics... IMC corrected images are the same as, or better than, images without IMC applied."
    • Free Breathing Dynamic DLR: "Providing acceptable diagnostic confidence."

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

• Yes, standalone performance was evaluated for several features, primarily through bench testing and quantitative metrics.
  • Exsper 3D: Evaluated using phantom images, confirmed artifact reduction compared to conventional method.
  • Slice Shim: Evaluated using clinical images, confirmed image quality equal to or better than standard shim.
  • UTE CG Recon: Evaluated using phantom and clinical images, confirmed better maintenance of resolution and SNR.
  • Ringing Correction: Evaluated using phantom and clinical images, confirmed ringing reduction.
  • PIQE: Underwent performance (bench testing) using ACR phantom images and typical clinical images (brain and knee). Metrics included SNR, signal intensity profiles for ringing and sharpness, Edge Slope Width, Ringing Variable Mean, Signal-to-Noise ratio, and Contrast Change Ratio. This is a clear standalone evaluation of the algorithm's output metrics.
  • NeuroLine+: Underwent performance (bench) testing using clinical images. Autopositioning success rate was evaluated against manually annotated ground truth. This is a standalone evaluation of the algorithm's accuracy.
  • Iterative Motion Correction (IMC): Underwent performance (bench testing) using clinical datasets with simulated motion. Metrics of peak SNR and structural similarity (SSIM) were used. This is a standalone evaluation of the algorithm's effectiveness.
  • Free Breathing Dynamic DLR (Arterial Phase Detection): Underwent performance (bench) testing using clinical images, assessing the success rate of the automatic detection against manually selected gold standard phases. This is a standalone evaluation of the algorithm's accuracy.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• Expert Consensus/Annotation:

  • PIQE: Clinically relevant categories (ringing, sharpness, SNR, overall IQ, feature conspicuity) scored by board-certified radiologists on a Likert scale, with >=3 considered clinically acceptable.
  • NeuroLine+: Manual annotation of angle and position of target planes by experienced ARRT licensed MR technologists.
  • IMC: SNR, tissue contrast, image sharpness, and diagnostic confidence scored by board-certified radiologists on a Likert scale.
  • Free Breathing Dynamic DLR:
    • Arterial phase detection: "Gold standard phase as manually selected by experienced radiologists."
    • Clinical image review: Overall SNR, overall IQ, feature conspicuity, and diagnostic confidence scored by board-certified radiologists on a Likert scale.

• Quantitative/Objective Metrics:

  • Exsper 3D: Artifact reduction compared to conventional method.
  • Slice Shim: Image quality comparison to standard shim.
  • UTE CG Recon: Maintenance of image resolution and SNR.
  • Ringing Correction: Reduction of ringing.
  • PIQE (bench): SNR, signal intensity profiles for ringing and sharpness, Edge Slope Width, Ringing Variable Mean, Signal-to-Noise ratio, Contrast Change Ratio.
  • NeuroLine+ (bench): Angular error and autopositioning success rate (comparison to manual annotations).
  • IMC (bench): Peak SNR and structural similarity (SSIM).
  • Ceiling Camera: Percentage of successful patient orientation detection and cases requiring no correction.

• Pathology/Outcomes Data: Not explicitly mentioned as a direct ground truth for the performance evaluation of these specific software features. The IMC study did use clinical patients with pathology, but the evaluation was still based on image quality and diagnostic information as assessed by radiologists, not a direct comparison to pathology reports or long-term outcomes.

8. The sample size for the training set

• The document consistently states for PIQE, NeuroLine+, IMC, and Free Breathing Dynamic DLR that "All testing data were acquired separately and independently from the training data after the machine learning training was completed."
• However, the specific sample sizes for the training datasets themselves are not provided in this document.

9. How the ground truth for the training set was established

• Again, the document states that training data was used, particularly for features utilizing Deep Learning (e.g., PIQE, IMC, Free Breathing Dynamic DLR, NeuroLine+ via Machine Learning).
• However, the document does not detail how the ground truth for these training sets was established. It only clarifies that the test data was independent of the training data.

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