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MAGNETOM Free.Max:
The MAGNETOM MR system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross-sectional images that display, 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 may also be produced. Depending on the region of interest, contrast agents may be used. These images and the physical parameters derived from the images when interpreted by a trained physician or dentist trained in MRI yield information that may assist in diagnosis.

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

When operated by dentists and dental assistants trained in MRI, the MAGNETOM MR system must only be used for scanning the dentomaxillofacial region.

MAGNETOM Free.Star:
The MAGNETOM MR system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross-sectional images that display, 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 may also be produced. Depending on the region of interest, contrast agents may be used. These images and the physical parameters derived from the images when interpreted by a trained physician yield information that may assist in diagnosis.

The subject devices MAGNETOM Free.Max and MAGNETOM Free.Star with software version syngo MR XA80A, consists of new and modified hardware and software features comparing to the predicate device MAGNETOM Free.Max and MAGNETOM Free.Star with software version syngo MR XA60A (K231617).

New hardware features (Only for MAGNETOM Free.Max):

• Dental coil
• High-end host
• syngo Workplace

Modified hardware features:

• MaRS
• Select&GO Display (TPAN_3G)

New Pulse Sequences/ Software Features / Applications:

Only for MAGNETOM Free.Max:

• EP_SEG_FID_PHS
• EP2D_FID_PHS
• EP_SEG_PHS
• GRE_Proj
• GRE_PHS
• myExam Dental Assist
• Select&GO Dental
• Slice Overlapping

For both MAGNETOM Free.Max and MAGNETOM Free.Star:

• Eco Power Mode
• Extended Gradient Eco Mode
• System Startup Timer

Modified Features and Applications:

• myExam RT Assist (only for MAGNETOM Free.Max)
• Deep Resolve for HASTE
• Deep Resolve for EPI Diffusion
• Select&GO for dental (only for MAGNETOM Free.Max)
• Select&GO extension: Patient Registration and Start Scan
• SPACE improvement: MTC prep module

Other Modifications and Minor Changes:

• MAGNETOM Free.Max Dental Edition marketing bundle (only for MAGNETOM Free.Max)
• MAGNETOM Free.Max RT Pro Edition marketing bundle (only for MAGNETOM Free.Max)
• Off-Center Planning Support
• ID Gain

The provided FDA 510(k) clearance letter and summary for MAGNETOM Free.Max and MAGNETOM Free.Star (K251822) offer high-level information regarding the devices and their comparison to predicate devices. However, it does not explicitly detail acceptance criteria (performance metrics with pass/fail thresholds) or a specific study proving the device meets those criteria for the overall device clearance.

The document primarily focuses on demonstrating substantial equivalence to predicate devices for general MR diagnostic imaging. The most detailed performance evaluation mentioned is for the AI feature "Deep Resolve Boost." Therefore, the response will focus on the information provided regarding Deep Resolve Boost, and address other points based on what is stated and what is not.

Acceptance Criteria and Device Performance (Focusing on Deep Resolve Boost)

Table 1. Deep Resolve Boost Performance Summary

Note: The document does not provide numerical thresholds or specific statistical methods used to define "significantly better" for SSIM and PSNR. The acceptance criteria are implicitly derived from the reported positive performance relative to conventional reconstruction.

Study Details for Deep Resolve Boost

1. Sample Size used for the test set and the data provenance:

   • Test Data: A "set of test data" was used for quantitative metrics (SSIM, PSNR) and visual evaluation. This test data was a "retrospectively undersampled copy of the test data" which was also used for conventional reconstruction.
   • Provenance: "In-house measurements and collaboration partners."
   • Retrospective/Prospective: The process of creating the test data by manipulating (undersampling) retrospectively acquired data indicates a retrospective approach.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Number of Experts: Not specified. The document states, "Visual evaluation was performed by qualified readers."
   • Qualifications of Experts: "Qualified readers." No further specific qualifications (e.g., years of experience, specialty) are provided.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • Not specified. The document states, "Visual evaluation was performed by qualified readers." It does not mention whether multiple readers were used per case or how discrepancies were resolved.

4. 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:

   • No, an MRMC comparative effectiveness study involving human readers with vs. without AI assistance was not explicitly described for the Deep Resolve Boost feature. The visual evaluation was focused on comparing images reconstructed with conventional methods versus Deep Resolve Boost, primarily to assess image quality attributes without explicit human performance metrics (e.g., diagnostic accuracy, reading time).

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

   • Yes, a standalone performance evaluation was done. The quantitative metrics (SSIM, PSNR) and the visual assessment of images reconstructed solely by the algorithm (Deep Resolve Boost) were performed to characterize the network's impact independently.

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

   • The "acquired datasets represent the ground truth for the training and validation." Input data for testing was "retrospectively created from the ground truth by data manipulation and augmentation." This implies that the raw, fully sampled, and high-quality MRI acquisitions are considered the ground truth against which the reconstructed images (conventional and Deep Resolve Boost) are compared. This is a technical ground truth rather than a clinical ground truth like pathology.

7. The sample size for the training set:

   • TSE: More than 25,000 slices.
   • HASTE: Pretrained on the TSE dataset and refined with more than 10,000 HASTE slices.
   • EPI Diffusion: More than 1,000,000 slices.

8. How the ground truth for the training set was established:

   • "The acquired datasets represent the ground truth for the training and validation."
   • Input data for training was "retrospectively created from the ground truth by data manipulation and augmentation." This included "further under-sampling of the data by discarding k-space lines, lowering of the SNR level by addition of noise and mirroring of k-space data."
   • This indicates that the ground truth for training was derived from high-quality, fully sampled MRI acquisitions, which were then manipulated to simulate lower quality inputs for the AI to learn from.

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