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    K Number
    K232322
    Device Name
    MAGNETOM Terra; MAGNETOM Terra.X
    Manufacturer
    Siemens Medical Solutions USA, Inc.
    Date Cleared
    2024-03-22

    (232 days)

    Product Code
    LNH,LNI,MOS
    Regulation Number
    892.1000
    Type
    Traditional
    Panel
    Radiology
    Reference & Predicate Devices
    K213693,K202014,K191040,K183222
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    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.

    Device Description

    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).

    AI/ML Overview

    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

    AI FeatureAcceptance CriteriaReported Device Performance
    Deep Resolve BoostCharacterization by several quality metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Visual inspection to ensure potential artifacts are detected. Successful passing of quality metrics tests. Work-in-progress packages delivered and evaluated in clinical settings. (Implicit: No misinterpretation, alteration, suppression, or introduction of anatomical information, and potential for faster image acquisition and significant time savings).The impact of the network has been characterized by several quality metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Additionally, images were inspected visually to ensure that potential artifacts are detected that are not well captured by the metrics listed above. After successful passing of the quality metrics tests, work-in-progress packages of the network were delivered and evaluated in clinical settings with cooperation partners. In a total of seven peer-reviewed publications, the investigations covered various body regions (prostate, abdomen, liver, knee, hip, ankle, shoulder, hand, and lumbar spine) on 1.5T and 3T systems. All publications concluded that the work-in-progress package and the reconstruction algorithm can be beneficially used for clinical routine imaging. No cases have been reported where the network led to a misinterpretation of the images or where anatomical information has been altered, suppressed, or introduced. Significant time savings are reported.
    Deep Resolve SharpCharacterization by several quality metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and perceptual loss. Verification and validation by in-house tests including visual rating and evaluation of image sharpness by intensity profile comparisons. (Implicit: Increased edge sharpness).The impact of the network has been characterized by several quality metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and perceptual loss. In addition, the feature has been verified and validated by in-house tests. These tests include visual rating and an evaluation of image sharpness by intensity profile comparisons of reconstruction with and without Deep Resolve Sharp. Both tests show increased edge sharpness.
    Deep RxE1. During training, the loss (difference to ground truth) is monitored, and the training step with the lowest test loss is taken as the final trained network.
    1. Automated unit-tests are set up to test the consistency of the generated output to a previously defined reference output.
    2. During verification, the performance of the network is tested on a phantom against the ground truth with a maximal allowed NRMSE of 11% (for 2D network) and 8.7% (for 3D network).
    3. The trained final network was used in the clinical study. (Implicit: Increases image homogeneity in a reproducible way on the receive profile, and images acquired with Deep RxE are rated better for image quality in the clinical study). | 1. During training, the loss, as the difference to a ground truth, is monitored and the training step with the lowest test loss is taken as the final trained network.
    4. Automated unit-tests are set up to test the consistency of the generated output to a previously defined reference output.
    5. During verification, the performance of the network is tested on a phantom against the ground truth with a maximal allowed NRMSE of 11% (11% for the 2D network and 8.7% for the 3D network were achieved).
    6. The trained final network was used in the clinical study.
      The tests show that Deep RxE increases image homogeneity in a reproducible way on the receive profile. Images acquired with Deep RxE (DL bias field correction) are rated better for image quality than the ones acquired without it in the clinical study that was conducted. |

    Note on Acceptance Criteria: The document directly states acceptance criteria for Deep RxE (e.g., NRMSE

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