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The Dual Tuned Surface Coils 3T are surface type transmit/receive coils dual tuned on l H (proton) and either 31P (phosphorus), 23Na (sodium) or 13C (carbon) nuclei. The coils are indicated for use as a diagnostic imaging device accessory to Siemens MAGNETOM Trio, A Tim System 3T and MAGNETOM Verio 3T magnetic resonance diagnostic devices to produce transverse, sagittal, coronal and oblique images, spectroscopic images and/or spectra, and that display the internal structure and/or function of the head or extremities. These images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

The Dual Tuned Surface Coils 3 T are transmit/receive surface coils to detect radiofrequency (RF) signals of hydrogen (1H) nuclei in combination with either phosphorus (31P), carbon (13C) or sodium (23Na) nuclei. Each coil consists of two single concentric loops, one of which is always tuned to the proton frequency, the other being tuned to either phosphorus-31P, carbon-13C or sodium-23Na frequency. Additional components are housed in a separate interface housing box.

Here's a breakdown of the acceptance criteria and study information for the Dual Tuned Surface Coils 3T, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The submission acts as a 510(k) summary, aiming to demonstrate substantial equivalence to a predicate device rather than explicitly defining and meeting specific quantitative acceptance criteria in a pass/fail manner. Instead, the submission outlines parameters considered and states that the new device's performance results in these areas are "equivalent with the predicate devices."

Note: The document does not provide specific numerical values for the performance metrics, only stating equivalence to the predicate device or that tests were performed.

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

The provided text does not explicitly state a "test set" in the context of human subject data. The testing described appears to be primarily bench testing or phantom testing on the device itself and its interaction with MR systems. Therefore, there is no mention of human subject sample sizes or data provenance (country of origin, retrospective/prospective). The tests were performed on "the Siemens MAGNETOM Trio a Tim System" which is an MR scanner.

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

Not applicable. This device is an accessory (surface coil) for an MR diagnostic device. The ground truth for its performance tests (e.g., SNR, image uniformity, spectral resolution) would be derived from quantitative measurements based on standardized test methods (like NEMA standards), not expert interpretation of images/spectra for diagnostic accuracy.

4. Adjudication Method for the Test Set

Not applicable. As noted above, the performance is assessed through quantitative measurements, not expert adjudication of diagnostic outcomes.

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

Not applicable. This submission is for a hardware accessory (MR coil), not an AI-powered diagnostic software.

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

Not applicable. This is a hardware device. Its "performance" is inherently "standalone" in that it produces raw data/images/spectra, which are then interpreted by a human. The coil itself is not an algorithm.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

The "ground truth" for the device's performance tests would be established through quantitative measurements against recognized industry standards (e.g., NEMA standards) or engineering specifications. For example, SNR would be measured directly from phantom images, spectral resolution from known chemical phantoms, etc. This is not a diagnostic device where pathology or expert consensus would establish ground truth for clinical decision-making.

8. The Sample Size for the Training Set

Not applicable. This is a hardware device, not a machine learning algorithm that requires a "training set."

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

Not applicable. As above, this is a hardware device.

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