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The MAGNETOM system is indicated for use as a magnetic 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 system 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.

MAGNETOM Terra and MAGNETOM Terra.X with software syngo MR XA60A include new and modified hardware and software compared to the predicate device, MAGNETOM Terra with software syngo MR E12U. A high level summary of the new and modified hardware and software is provided below: Hardware: New Hardware (Combiner (pTx to sTx), MC-PALI, GSSU control unit, 8Tx32Rx Head coil), Modified Hardware (Main components such as: Upgrade of GPA, New Host computer hardware, New MaRS computer hardware, Upgrade the SEP, The new shim cabinet ASC5 replaces two ACS4 shim cabinets; Other components such as: RFPA, Use of a common MR component which provides basic functionality that is required for all MAGNETOM system types, The multi-nuclear (MNO) option has been modified, OPS module, Cover with UI update on PDD). Software: New Features and Applications (Static B1 shimming, TrueForm (1ch compatibility mode), Deep Resolve Boost, Deep Resolve Gain, Deep Resolve Sharp, Bias field correction (marketing name: Deep RxE), The new BEAT pulse sequence type, BLADE diffusion, The PETRA pulse sequence type, TSE DIXON, The Compressed Sensing (CS) functionality is now available for the SPACE pulse sequence type, The Compressed Sensing (CS) functionality is now available for the TFL pulse sequence type, IDEA, The Scientific Suite), Modified Features and Applications (EP2D DIFF and TSE with SliceAdjust, The Turbo Flash (TFL)), Modified Software / Platform (Stimulation monitoring, "dynamic research labeling"), Other Modifications and / or Minor Changes (Intended use, SAR Calculation and Weight limit reduction for 31P/1H TxRx Flex Loop Coil, X-upgrade for MAGNETOM Terra to MAGNETOM Terra.X, Provide secure MR scanner setup for DoD (Department of Defense) -Information Assurance compliance).

The provided text describes the acceptance criteria and supporting study for the AI features (Deep Resolve Boost, Deep Resolve Sharp, and Deep RxE) within the MAGNETOM Terra and MAGNETOM Terra.X devices.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

Note on Acceptance Criteria: The document directly states acceptance criteria for Deep RxE (e.g., NRMSE < 11%). For Deep Resolve Boost and Deep Resolve Sharp, the "acceptance criteria" are more implicitly derived from the described validation and evaluation metrics and outcomes (e.g., "successful passing of quality metrics tests," "increased edge sharpness," "no misinterpretation").

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

Patient characteristics (Gender/Age) were recorded for Deep RxE: female: 56%, male: 41%, phantom: 3%. Age range 20-80 years. Not recorded for other features.
Ethnicity was not recorded for any feature. The document states that due to network architecture, attributes like gender, age, and ethnicity are not relevant to training data.

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

• Deep Resolve Boost & Deep Resolve Sharp: The document does not mention the use of experts for ground truth establishment for the test set regarding these features. Images were visually inspected and quality metrics were used.
• Deep RxE: The document mentions that images acquired with Deep RxE were "rated better for image quality than the ones acquired without it in the clinical study that was conducted." This implies expert evaluation, but the number of experts or their qualifications for the test set ground truth for Deep RxE is not explicitly stated.
  • Separately, for the overall device clearance, "radiologist's evaluation reports from two U.S. board-certified radiologists have been provided" for software modifications and new hardware. This is a general statement for the device and not specifically linked to the ground truth of the AI features' test sets.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method (like 2+1, 3+1) for establishing ground truth for the test sets of these AI features. For Deep Resolve Boost and Sharp, the ground truth was derived from the acquired datasets themselves, which were then manipulated to create input data. For Deep RxE, the ground truth for phantom testing was a "previously defined reference output" or the acquired data, and for the clinical study, images were "rated better" but the adjudication process for this rating isn't detailed.

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

No MRMC comparative effectiveness study is directly mentioned specifically for the AI features (Deep Resolve Boost, Deep Resolve Sharp, or Deep RxE) that compares human readers with vs. without AI assistance. The document alludes to radiologists evaluating images with new software features or comparing images from subject/predicate devices, and for Deep Resolve Boost, it mentions clinical settings and "seven peer-reviewed publications" concluding beneficial use for clinical routine, with reports of "significant time savings." For Deep RxE, images were "rated better for image quality," which implies a reader study, but no details on methodology, number of readers, or specific effect size are provided to quantify human reader improvement with AI assistance.

6. Standalone (Algorithm Only) Performance

Yes, standalone performance was conducted for all three AI features:

• Deep Resolve Boost: Characterized by PSNR and SSIM, and visual inspection.
• Deep Resolve Sharp: Characterized by PSNR, SSIM, perceptual loss, visual rating, and intensity profile comparisons.
• Deep RxE: Performance of the network tested on a phantom against ground truth (maximal allowed NRMSE of 11% for 2D, 8.7% for 3D achieved). Unit-tests and a two-step test procedure involving validation on unseen data and RMS error calculation against ground truth.

These indicate evaluation of the algorithm's performance without a human in the loop, beyond initial visual inspections by evaluators.

7. Type of Ground Truth Used

• Deep Resolve Boost: The acquired datasets themselves, retrospectively manipulated through data manipulation and augmentation (under-sampling, lowering SNR, mirroring k-space data) to create input data.
• Deep Resolve Sharp: The acquired datasets themselves, retrospectively manipulated through data manipulation (cropping k-space data) to create corresponding low-resolution input and high-resolution output/ground truth.
• Deep RxE: For phantom testing, a "previously defined reference output" was used, and for other evaluations, the acquired datasets themselves were used for comparison against bias field correction methods (homodyne filtering, N4, UNICORN).

8. Sample Size for the Training Set

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

• Deep Resolve Boost: The "acquired datasets represent the ground truth for the training and validation." Input data was "retrospectively created from the ground truth by data manipulation and augmentation."
• Deep Resolve Sharp: The "acquired datasets represent the ground truth for the training and validation." Input data was "retrospectively created from the ground truth by data manipulation."
• Deep RxE: The document states that "During training the loss, as the difference to a ground truth, is monitored." The method of establishing this initial ground truth is implicitly the raw acquired data from the 7T MRI scanners, as the network aims to correct for B1 inhomogeneities. It also states "All data from the two MR systems were separated into independent training, validation and test datasets," implying the raw or processed raw data served as reference for training.

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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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The MAGNETOM Terra system is indicated for use as a magnetic 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. These images and the physical parameters derived from the interpreted by a trained physician vield 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. The MAGNETOM Terra uses two local coils 1Tx32Rx Head Coil 7T Clinic and 1Tx28Rx Knee Coil 7T Clinic for head and knee imaging.

The provided text describes the Siemens MAGNETOM Terra, a 7T Magnetic Resonance Imaging (MRI) system. However, it focuses on demonstrating substantial equivalence to a predicate device (MAGNETOM Trio A Tim System with syngo MR B19A) rather than establishing novel safety and effectiveness through specific acceptance criteria and a dedicated study demonstrating the device meets those criteria for a new clinical indication or outcome.

The text outlines various non-clinical tests and a clinical study primarily to ensure the device's fundamental safety and performance within the established framework for MRI devices, especially given the increased magnetic field strength (7T). It does not present a study designed to prove the device meets specific acceptance criteria related to a new clinical performance claim or diagnostic accuracy.

Therefore, many of the requested sections (Table of acceptance criteria, device performance, sample size for test set, data provenance, number of experts for ground truth, adjudication method, MRMC study, standalone performance, type of ground truth used for test set, training set details) are not applicable or extractable from this document as the submission does not detail a study aimed at proving a specific clinical performance criterion for this device as a new clinical claim.

Below is a summary of the information that can be extracted or inferred based on the document's content:

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

No explicit "acceptance criteria" table for a specific clinical performance claim is provided. The submission focuses on demonstrating compliance with recognized standards and substantial equivalence to a predicate device. Performance is generally assessed via image quality and safety parameters.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample size for nerve stimulation threshold study: 35 individuals.
• Data provenance: Not explicitly stated whether retrospective or prospective, or country of origin. It is a "clinical study" performed to set PNS thresholds.
• Sample images for image quality assessment: Not specified beyond "sample clinical images were acquired for all available clinical pulse sequences and local coils."

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• For nerve stimulation threshold study: Not applicable, as this study determines physiological thresholds, not ground truth for diagnostic imaging interpretation.
• For image quality assessment: "reports from two U.S. board-certified radiologists have been provided after the radiologists reviewed image pairs comparing the subject and the predicate device." Their specific experience level is not mentioned beyond "board-certified." This implies a qualitative assessment, not a formal ground truth establishment for a diagnostic study.

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

• For image quality review: Not explicitly detailed beyond "two U.S. board-certified radiologists... reviewed image pairs comparing the subject and the predicate device [and their] comments on any observed artifacts and concerns have also been included." This suggests a qualitative comparison rather than a formal adjudication process for diagnostic accuracy.

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 MRMC comparative effectiveness study is mentioned, nor is there any AI component described in the device. This device is an MRI scanner, not an AI-powered diagnostic tool.

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

• Not applicable, as this is an MRI scanner, not an algorithm being evaluated for standalone diagnostic performance.

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

• For nerve stimulation threshold study: The "ground truth" is the empirically observed nerve stimulation thresholds in the 35 individuals, which defines the physiological limits for setting the PNS threshold level.
• For image quality assessment: The "ground truth" or reference is implied to be the qualitative assessment and comparison by board-certified radiologists against the predicate device, focusing on image characteristics and artifacts. No objective ground truth (e.g., pathology, clinical outcomes) is stated as being used to assess diagnostic accuracy.

8. The sample size for the training set

• Not applicable/provided. This submission does not describe a machine learning algorithm that requires a training set. The software development is based on an existing software line and adapted for 7T parameters. The SAR control software enhancements are based on simulations with human models.

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

• Not applicable, as no training set for a machine learning algorithm is described. The SAR control software relies on computational modeling and simulation data using established human models (Virtual Population, MIDA Model) to estimate local SAR.

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