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The MAGNETOM Terra system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross sectional images, and that displays the internal structure and/or function of the head or extremities. Other physical parameters derived from the images may also be produced.

Additionally the MAGNETOM Terra is intended to produce Sodium images for the head and Phosphorus spectroscopic images and/or spectra for whole body, excluding the head.

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 device is intended for patients > 30 kg/66 lbs.

MAGNETOM Terra is a 60 cm bore Magnetic Resonance Imaging system with an actively shielded 7T superconducting magnet. With the interplay of the magnetic field, gradients, radio frequency (RF) transmitter and receiver coil and software this magnetic resonance scanner produces transverse, sagittal, coronal and oblique cross sectional images that represent the spatial distribution of protons with spin.

Additionally the MAGNETOM Terra produce Sodium images for the head and Phosphorus spectroscopic images and/or spectra for the whole body, excluding the head.

For MAGNETOM Terra four local transmit/receive coils for the specific applications are available, these are:

1Tx32Rx Head Coil 7T Clinic; 1Tx28Rx Knee Coil 7T Clinic;

31P/1H TxRx Flex Loop 7T; 23Na 1Tx32Rx Head 7T

This document is a 510(k) summary for the Siemens MAGNETOM Terra medical device, a Magnetic Resonance (MR) system. It describes modifications to an existing device (MAGNETOM Terra with syngo MR E11K) to add new capabilities: Sodium (23Na) imaging of the head and Phosphorus (31P) spectroscopic imaging/spectra for the whole body, excluding the head.

The document does not contain acceptance criteria in the form of quantitative performance metrics for device output, nor does it describe a comparative study that proves the device meets specific acceptance criteria. Instead, the submission argues for substantial equivalence to a predicate device and reference devices based on non-clinical testing, software validation, and existing clinical literature.

Here's a breakdown of the requested information based on the provided text, highlighting what is present and what is missing/not applicable for this type of submission:

1. A table of acceptance criteria and the reported device performance:

• Missing. This submission does not provide a table of acceptance criteria with corresponding performance results. The focus is on demonstrating that the new functionalities (23Na imaging and 31P spectroscopy) are safe and effective, and do not raise new questions of safety or effectiveness compared to predicate and reference devices. The "performance" mentioned refers broadly to the device performing as intended through non-clinical tests (image quality assessments, surface heating, software V&V).

2. Sample sized used for the test set and the data provenance:

• Test Set (Non-clinical Data):
  • "Sample clinical images or Phosphorus spectra were acquired for all modified / new pulse sequences and local coils."
  • "reports from one U.S. board-certified radiologist have been provided. The radiologist reviewed Sodium head images of healthy volunteers and patient with respect to their diagnostic quality."
  • Sample Size: Not explicitly stated as a numerical count for healthy volunteers or patients beyond "sample clinical images" and "patient." Only "one U.S. board-certified radiologist" is mentioned as reviewing the images.
  • Data Provenance: Implied to be prospective, collected for the purpose of this submission (e.g., healthy volunteers suggests prospective collection). The location is "U.S." for the radiologist, but not explicitly stated for where the images were acquired, though likely in Europe where manufacturing site is, or US where Siemens Medical Solutions USA is.
• Training Set:
  • Not Applicable. This submission is for modifications to an existing MR system and relies on demonstrating substantial equivalence, not the development and validation of an AI/ML algorithm that would typically require a training set. The new functionalities are extensions to existing MR capabilities, not an AI output.

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

• Number of Experts: One. "reports from one U.S. board-certified radiologist have been provided."
• Qualifications: "U.S. board-certified radiologist." No specific experience (e.g., "10 years of experience") is mentioned.

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

• None stated. Only one radiologist reviewed the images. There is no mention of a consensus or adjudication process given only one reader.

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:

• No. An MRMC study was not conducted. This is not an AI-assisted device, but rather an MR imaging system with extended capabilities. The submission focuses on the safety and effectiveness of the device itself for acquiring images/spectra, not how it impacts human interpretation via assistance.

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

• Not Applicable. This is an MR acquisition device, not a standalone diagnostic algorithm. Its output (images and spectra) is intended to be interpreted by a trained physician. No such "algorithm only" performance would be relevant here.

7. The type of ground truth used:

• For the non-clinical images reviewed by the radiologist: The "ground truth" seems to be effectively the expert opinion/qualitative assessment of the single U.S. board-certified radiologist regarding diagnostic quality, artifacts, and concerns. There's no mention of pathology, clinical outcomes, or a multi-expert consensus serving as a formal ground truth.

8. The sample size for the training set:

• Not Applicable. As noted above, this is not an AI/ML device that requires a training set.

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

• Not Applicable. No training set was used.

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