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The MAGNETOM system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross-sectional images, spectroscopic images and/or spectra, and that displays, depending on optional local coils that have been configured with the system, the internal structure and/or function of the head, body, or extremities. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used. These images and/or spectra and the physical parameters derived from the images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

The MAGNETOM system may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room displays and MR Safe biopsy needles.

MAGNETOM Flow.Ace and MAGNETOM Flow.Plus are 60cm-bore MRI systems with quench pipe-free, sealed magnets utilizing DryCool technology. They are equipped with BioMatrix technology and run on Siemens' syngo MR XA70A software platform. The systems include Eco Power Mode for reduced energy and helium consumption. They have different gradient configurations suitable for all body regions, with stronger configurations supporting advanced cardiac imaging. Compared to the predicate device, new hardware includes a new magnet, gradient coil, RF system, local coils, patient tables, and computer systems. New software features include AutoMate Cardiac, Quick Protocols, BLADE with SMS acceleration for non-diffusion imaging, Deep Resolve Swift Brain, Fast GRE Reference Scan, Ghost reduction, Fleet Reference Scan, SMS Averaging, Select&GO extension, myExam Spine Autopilot, and New Startup-Timer. Modified features include improvements for Pulse Sequence Type SPACE, improved Gradient ECO Mode Settings, and Inline Image Filter switchable for users.

The provided 510(k) clearance letter and summary describe the acceptance criteria and supporting studies for the MAGNETOM Flow.Ace and MAGNETOM Flow.Plus devices, particularly focusing on their AI features: Deep Resolve Boost, Deep Resolve Sharp, and Deep Resolve Swift Brain.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document uses quality metrics like PSNR, SSIM, and NMSE as indicators of performance and implicitly as acceptance criteria. Visual inspection and clinical evaluations are also mentioned.

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

The document uses "Training and Validation data" and often refers to the datasets used for both. It is not explicitly stated what percentage or how many cases from these datasets were strictly reserved for a separate "test set" and what came from the "validation sets." However, given the separation in slice count, the "Validation" slices for Deep Resolve Swift Brain might be considered the test set.

• Deep Resolve Boost:
  • TSE: >25,000 slices
  • HASTE: >10,000 HASTE slices (refined)
  • EPI Diffusion: >1,000,000 slices
  • Data Provenance: Retrospectively created from acquired datasets. Data covered a broad range of body parts, contrasts, fat suppression techniques, orientations, and field strength.
• Deep Resolve Sharp:
  • Data: >10,000 high resolution 2D images
  • Data Provenance: Retrospectively created from acquired datasets. Data covered a broad range of body parts, contrasts, fat suppression techniques, orientations, and field strength.
• Deep Resolve Swift Brain:
  • 1.5T Validation: 3,616 slices (This functions as a test set for 1.5T)
  • 3T Validation: 6,048 slices (This functions as a test set for 3T)
  • Data Provenance: Retrospectively created from acquired datasets.

The document does not explicitly state the country of origin for the data.

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

The document does not explicitly state the number or qualifications of experts used to establish the ground truth for the test sets. For Deep Resolve Swift Brain, it mentions "evaluated in clinical settings with collaboration partners," implying clinical experts were involved in the evaluation, but details are not provided. For Boost and Sharp, the "acquired datasets...represent the ground truth," suggesting the raw imaging data itself, rather than expert annotations on that data, served as ground truth.

4. Adjudication Method for the Test Set

The document does not describe a formal adjudication method (e.g., 2+1, 3+1). For Deep Resolve Swift Brain, it mentions "visually inspected" and "evaluated in clinical settings with collaboration partners," suggesting a qualitative assessment, but details on consensus or adjudication are missing.

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

A formal MRMC comparative effectiveness study demonstrating human reader improvement with AI vs. without AI assistance is not described in the provided text. The studies focus on the AI's standalone performance in terms of image quality metrics and internal validation.

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

Yes, standalone performance was done for the AI features. The "Test Statistics and Test Results Summary" for Deep Resolve Boost, Deep Resolve Sharp, and Deep Resolve Swift Brain describe the evaluation of the algorithm's output using quantitative metrics (PSNR, SSIM, NMSE) and visual inspection against reference standards, which is characteristic of standalone performance evaluation.

7. The Type of Ground Truth Used

For Deep Resolve Boost, Deep Resolve Sharp, and Deep Resolve Swift Brain, the ground truth used was the acquired high-quality datasets themselves. The input data for training and validation was then retrospectively created from this ground truth by manipulating or augmenting it (e.g., undersampling k-space, adding noise, cropping, using only the center part of k-space). This means the original, higher-quality MR images or k-space data served as the reference for what the AI models should reconstruct or improve upon.

8. The Sample Size for the Training Set

• Deep Resolve Boost:
  • TSE: >25,000 slices
  • HASTE: pre-trained on the TSE dataset and refined with >10,000 HASTE slices
  • EPI Diffusion: >1,000,000 slices
• Deep Resolve Sharp: >10,000 high resolution 2D images
• Deep Resolve Swift Brain: 20,076 slices

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

For Deep Resolve Boost, Deep Resolve Sharp, and Deep Resolve Swift Brain, the "acquired datasets (as described above) represent the ground truth for the training and validation." This implies that high-quality, fully acquired MRI data was considered the ground truth. The input data used during training (e.g., undersampled, noisy, or lower-resolution versions) was then derived or manipulated from this original ground truth. Essentially, the "ground truth" was the optimal, full-data acquisition before any degradation was simulated for the AI's input.

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