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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) 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.

Here's an analysis of the acceptance criteria and supporting study details based on the provided FDA 510(k) summary:

Analysis of Acceptance Criteria and Proving Study

The submission focuses on software changes and the substantial equivalence of the new device (Vantage Galan 3T, MRT-3020, V7.0 with AiCE Reconstruction Processing Unit for MR) to its predicate (Vantage Galan 3T, MRT-3020, V6.0 with AiCE Reconstruction Processing Unit for MR). The primary objective of the testing described is to demonstrate that the image quality and safety of the new software functionalities are maintained and are of diagnostic quality.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document explicitly states "No change from the previous predicate submission, K192574" for Imaging Performance Parameters on page 6. However, the subsequent "TESTING" section (Page 7) describes objective image quality assessment for the new software functionalities (3D FAST sequences and 3D Compressed SPEEDER acceleration sequences) using expert review to demonstrate that diagnostic quality is maintained. This implies that the standard for image quality (diagnostic utility) serves as the implicit acceptance criterion.

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

• Sample Size: The document mentions "volunteer images" and "representative images" were used. It does not explicitly state the numerical sample size (number of volunteers or images) for the image quality testing.
• Data Provenance: The document does not specify the country of origin. The study appears to be prospective in the sense that the images were acquired using the subject device specifically for this evaluation using volunteers.

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

• Number of Experts: The document states "American Board Certified Radiologists and American Board Certified Cardiologists with MR certification" were used. It does not specify the exact number of such experts.
• Qualifications of Experts:
  • American Board Certified Radiologists
  • American Board Certified Cardiologists with MR certification
  • No information on their years of experience is provided.

4. Adjudication Method for the Test Set

The document does not explicitly state an adjudication method (like 2+1, 3+1). It describes "Reviewers provided detailed assessments," suggesting individual reviews contributed to the confirmation of diagnostic quality. It does not mention whether multiple reviewers were involved in assessing the same images and how disagreements were resolved.

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

There is no indication that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was performed to assess how much human readers improve with AI vs. without AI assistance. The study described focuses on confirming the diagnostic quality of images produced by the device with new software features, not on the impact of AI assistance on human reader performance. The "AiCE Reconstruction Processing Unit" is part of the core device functionality being assessed, not an AI assist to human interpretation in this context.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

The study described is an assessment of the image quality produced by the device, which includes the AiCE Reconstruction Processing Unit. The expert review confirms the diagnostic quality of these images. This is an evaluation of the algorithm's output, but it's not a standalone diagnostic performance study in the sense of the algorithm making an independent diagnosis. The device generates images to be interpreted by a trained physician. The study confirms the output (images) is of diagnostic quality, which could be considered a form of standalone performance for the image reconstruction aspect.

7. Type of Ground Truth Used

The ground truth used for evaluating image quality appears to be expert consensus/opinion regarding "diagnostic quality," "overall image noise," "image sharpness," "image degradation," "image artifacts," "diagnostic contrast," and "lesion/pathology conspicuity." This is derived from the subjective and objective assessment of the images by the certified radiologists and cardiologists. There is no mention of pathology findings or long-term outcomes data being used as ground truth for this particular study.

8. Sample Size for the Training Set

The document does not provide any information regarding the sample size for the training set used for the AiCE (Artificial intelligence-enhanced Reconstruction) component of the device. This information is typically found in the development or validation sections of the submission, but not in this summary.

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 for the AiCE component was established.

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Vantage 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 Orian (Model MRT-1550) is a 1.5 Tesla Magnetic Resonance Imaging (MRI) System. The Vantage Orian uses 1.4 m short and 3.8 tons light weight magnet. It includes the Pianissimo™ technology (scan noise reduction technology). The design of the gradient coil and the WB coil of the Vantage Orian 1.5T provides the maximum field of view of 55 x 50 cm. The Model MRT-1550/AC, AD, AG, AH includes the standard gradient system and Model MRT-1550/AK, AL, AO, AP includes the XGO gradient system.

AiCE is an optional noise reduction algorithm that improves image quality and reduces thermal noise by employing Deep Convolutional Neural Network methods. AiCE is designed to remove Gaussian distributed noise in MR images for reducing contributions of thermal noise. In order to train a DCNN that can learn a model that represents thermal noise, the training datasets are created by adding Gaussian noise of different amplitudes to high-SNR images acquired with large number of averages. The device is targeted for Brain and knee regions. This software and its associated hardware are used on Canon MRI systems that are designed to communicate with the AiCE Reconstruction Processing Unit for MR.

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 Orian 1.5T, MRT-1550, V6.0 with AiCE Reconstruction Processing Unit for MR is comparable to the current 1.5T Vantage Orian MRI System (K193021), cleared June 3rd, 2020 with the following modifications.

The provided text describes the Canon Medical Systems Corporation's Vantage Orian 1.5T, MRT-1550, V6.0 with AiCE Reconstruction Processing Unit for MR. Here's a breakdown of the acceptance criteria and the study details:

1. Acceptance Criteria and Reported Device Performance

The document doesn't explicitly state "acceptance criteria" in a tabulated format with specific numerical targets. Instead, it describes performance goals and how the device performed against them. The key performance goals for AiCE are:

• Improved Image Quality and Reduced Thermal Noise: Achieved by employing Deep Convolutional Neural Network methods.
• Maintained or Improved Low Contrast Detectability: Verified through a model observer study.
• Increased Signal-to-Noise Ratio (SNR) and Maintained Contrast: Demonstrated through measurements on clinical brain and knee images.
• Performance at or Above Predicate Device: Indicated by the human observer study's finding of a statistical preference for AiCE.

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

• Sample Size for Test Set: 160 image data sets for the human observer study.
• Data Provenance: Not explicitly stated, but the mention of "volunteer clinical imaging" suggests it was likely prospective data collected from volunteers. The regions targeted were "Brain and knee regions." Given the manufacturer is based in Japan, and the U.S. agent is in California, the data could originate from various geographical locations. The document does not specify the country of origin or if it was retrospective.

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

• Number of Experts: 6 board certified radiologists.
• Qualifications of Experts: Board certified radiologists. The document does not specify their years of experience.

4. Adjudication Method for the Test Set

• The document does not specify an adjudication method like 2+1 or 3+1 for establishing ground truth from the expert readers. It states the human observer study "demonstrated a statistical preference of AiCE when compared to other performance filters," implying a comparative assessment rather than a consensus-driven ground truth establishment for a diagnostic task.

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

• Yes, a human observer study was done. The document states: "Additionally, a human observer study was conducted with 6 board certified radiologists and 160 image data sets that demonstrated a statistical preference of AiCE when compared to other performance filters."
• Effect Size: The document mentions "a statistical preference of AiCE" and that AiCE performed "either at the same level or above the performance of the commercially available predicate device." However, it does not provide a specific quantitative effect size (e.g., AUC difference, sensitivity/specificity improvement, or change in reader confidence scores) for how much human readers improved with AI vs. without AI assistance. The study seems to have focused on whether AiCE images were preferred or performed better, implying an improvement or at least equivalence.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Yes, a standalone study was done in the form of a "model observer study" and direct measurements.
  • "AiCE deep learning reconstruction underwent performance (bench testing) using a model observer study to determine that image low contrast detectability was maintained or improved."
  • "In order to quantify the increase in SNR with AiCE over standard protocols, SNR measurements of sample clinical brain and knee images were obtained. Additionally, contrast was measured using the absolute signal intensity differences between two tissues." These are measurements of the algorithm's direct output on image quality metrics.

7. Type of Ground Truth Used

• For the standalone tests (model observer, SNR/contrast measurements): The ground truth was based on objective image quality metrics (low contrast detectability, SNR, contrast). For training the DCNN, high-SNR images acquired with a large number of averages were considered the "high quality" reference from which noisy images were created.
• For the human observer study: The "ground truth" was the "statistical preference" of the 6 board-certified radiologists when comparing AiCE images to images from other performance filters. This isn't a traditional diagnostic ground truth (like a biopsy result) but rather a preference-based assessment of image quality and clinical utility from experienced readers.

8. Sample Size for the Training Set

• The document states: "In order to train a DCNN that can learn a model that represents thermal noise, the training datasets are created by adding Gaussian noise of different amplitudes to high-SNR images acquired with large number of averages."
• The specific sample size (number of images or cases) for the training set is not provided.

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

• The ground truth for the training set was established through a synthetic process:
  • "High-SNR images acquired with large number of averages" were used as the reference "clean" images.
  • "Gaussian noise of different amplitudes" was then "added" to these high-SNR images to create noisy counterparts.
  • The DCNN was trained to learn how to transform the noisy images back to the high-SNR (ground truth) images, effectively modeling and removing the noise.

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