(232 days)
Yes
The device description explicitly lists "Deep Resolve Boost", "Deep Resolve Gain", "Deep Resolve Sharp", and "Bias field correction (marketing name: Deep RxE)" as new software features, and the performance studies section details the training and testing data and results for "Deep Resolve Boost", "Deep Resolve Sharp", and "Deep RxE", all of which are described as utilizing deep learning approaches.
No
The intended use states that the device is a magnetic device (MRDD) used to produce images and physical parameters to assist in diagnosis. This describes a diagnostic device, not a therapeutic device.
Yes
The "Intended Use / Indications for Use" section explicitly states that the images, spectra, and physical parameters derived from the system, when interpreted by a trained physician, "yield information that may assist in diagnosis." This directly indicates that the device is intended to aid in the diagnostic process.
No
The device description explicitly states that the submission includes "new and modified hardware and software" and lists several specific hardware components that are new or modified. This indicates it is not a software-only device.
Based on the provided information, the MAGNETOM system is not an In Vitro Diagnostic (IVD) device.
Here's why:
- Intended Use: The intended use clearly states that the system is a "magnetic device (MRDD)" that produces images and physical parameters of the internal structure and/or function of the head or extremities. These images and parameters are then "interpreted by a trained physician [to] yield information that may assist in diagnosis." This describes an in vivo imaging system used for diagnostic purposes, not a device that performs tests on biological samples outside of the body.
- Device Description: The description details hardware and software components related to magnetic resonance imaging, such as coils, gradient systems, RF amplifiers, and image processing software. These are characteristic of an MR scanner, not an IVD device.
- Input Imaging Modality: The input modality is Magnetic Resonance (MR), which is an in vivo imaging technique. IVD devices typically use modalities like spectroscopy, chromatography, or immunoassay to analyze biological samples.
- Anatomical Site: The device is used to image specific anatomical sites within the body (head, extremities, whole body). IVD devices analyze samples taken from the body (blood, urine, tissue, etc.).
- Performance Studies: The performance studies described focus on image quality metrics (PSNR, SSIM, NRMSE), system safety, and clinical evaluations comparing images from the device to predicate/reference devices. These are typical performance evaluations for medical imaging devices, not IVD devices which would focus on analytical and clinical performance metrics related to the analysis of biological samples (sensitivity, specificity, accuracy, etc.).
In summary, the MAGNETOM system is a medical imaging device used for in vivo diagnosis, not a device that performs tests on biological samples outside the body. Therefore, it does not fit the definition of an In Vitro Diagnostic device.
No
The letter does not state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for 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.
Product codes (comma separated list FDA assigned to the subject device)
LNH, LNI, MOS
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.
Hardware:
- New Hardware:
- Combiner (pTx to sTx) (8Tx-1Tx Combiner Interface)
- MC-PALI
- GSSU control unit
- New Coil:
- The 8Tx32Rx Head coil
- Modified Hardware:
- Upgrade of GPA
- New Host computer hardware
- New MaRS computer hardware
- Upgrade the SEP to the newest cooling cabinet series
- The new shim cabinet ASC5 replaces two ACS4 shim cabinets
- RFPA
- Use of a common MR component which provides basic functionality that is required for all MAGNETOM system types. RFCEL 2G light houses common MR components compatible with the MR environment but no specific 7T functionality is implemented and it is reduced in its functionality.
- The multi-nuclear (MNO) option has been modified to be used in combination with the parallel transmit (pTx) technology.
- 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 (marked as new device)
- Provide secure MR scanner setup for DoD (Department of Defense) -Information Assurance compliance.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Magnetic Resonance Diagnostic Device (MRDD)
Anatomical Site
Head or extremities, whole body (excluding the head for Phosphorus spectroscopic images)
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Trained physician / Clinical routine
Description of the training set, sample size, data source, and annotation protocol
Deep Resolve Boost:
- Test setup: Equipment: 7T MRI scanners (from the predicate device); Protocols: Representative protocols (T1, T2 and PD with and without fat saturation), which have been altered (e.g., to increase SNR, increase resolution or reduced acceleration); Body regions: head and knee.
- Sample size: 26,473 2D slices (6206 2D slices acquired at 7T).
- Dataset split: Training: 24,599 slices; Validation: 1,874 slices. Note: Data split maintained similar data distribution (e.g., contrast, orientation, field strength, ...) in both training and validation datasets.
- Sample source: in-house measurements and collaboration partners.
- Patient Characteristics: Clinical subgroups: No clinical subgroups have been defined for the datasets. Please note: due to reasons of data privacy, we did not record how many individuals the datasets belong to. Gender, age, and ethnicity distribution were also not recorded during data collection. Due to the network architecture, attributes like gender, age and ethnicity are not relevant to the training data.
- Confounder: The input and output variables of the network have been derived from the same dataset so that no confounders exist for the training methodology.
- Reference standard: 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. This process includes 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.
Deep Resolve Sharp:
- General / additional information: The same function as on reference device MAGNETOM Vida which was ported to the subject devices MAGNETOM Terra and MAGNETOM Terra.X without significant modifications. The training and testing from the reference devices still fits.
- Test setup: Equipment: 1.5T and 3T MRI scanners; Protocols: Representative protocols (T1, T2 and PD with and without fat saturation) which have been altered (e.g. to increase SNR, increase resolution or reduced acceleration); Body regions: a broad range of different body regions.
- Sample size: 13,977 2D slices.
- Dataset split: Training: 11,920 slices; Validation: 2,057 slices.
- Sample source: in-house measurements.
- Patient Characteristics: Not specified on patient characteristics for training.
- Reference standard: The acquired datasets represent the ground truth for the training and validation. Input data was retrospectively created from the ground truth by data manipulation. k-space data has been cropped such that only the center part of the data was used as input. With this method corresponding low-resolution data as input and high-resolution data as output / ground truth were created for training and validation.
Deep RxE:
- General / additional information: A 4-step approach was performed: 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. 2. Automated unit-tests are set-up to test the consistency of the generated output to a previously defined reference output 3. 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) 4. The trained final network was used in the clinical study.
- Test setup: Equipment: 7T MRI MAGNETOM Terra and MAGNETOM Terra.X scanners; Protocols: Representative protocols (T1, T2 and PD with and without fat saturation) which have been altered (e.g., to increase SNR, increase resolution or using acceleration techniques or without acceleration); Body regions: head (44%) and knee (56%); Used coils: - 1Tx32Rx Head Coil 7T Clinic / per system - research 8Tx32Rx Head / per system - 1Tx28Rx Knee Coil 7T Clinic / per system - 23Na 1Tx32Rx Head 7T.
- Sample size: 143,947/2410 (2D slices / 3D volumes).
- Dataset split: Training: 119,955/2007 (2D/3D); Validation and test: 23,992/404 (2D/3D). All data from the two MR systems were separated into independent training, validation and test datasets.
- Patient Characteristics: Gender distribution: female: 56%; male: 41%; phantom: 3%. Age: group ranges from 20 - 80 years. Clinical subgroups: No have been defined for the datasets. Please note: Due to the network architecture, attributes like gender, age and ethnicity are not relevant to the training data.
- Reference standard: Applying three different methods for bias field correction to the data, homodyne filtering, N4 and UNICORN.
Description of the test set, sample size, data source, and annotation protocol
Deep Resolve Boost:
- Test setup: Equipment: 7T MRI scanners (from the predicate device); Protocols: Representative protocols (T1, T2 and PD with and without fat saturation), which have been altered (e.g., to increase SNR, increase resolution or reduced acceleration); Body regions: head and knee.
- Sample size: Included in training/validation splits. "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."
- Dataset split: "Validation: 1,874 slices" (used for evaluation during training but not a completely separate test set as described for Deep RxE).
- Sample source: in-house measurements and collaboration partners.
- Patient Characteristics: Not captured.
- Confounder: None.
- Reference standard: 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. This process includes 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.
Deep Resolve Sharp:
- General / additional information: The same function as on reference device MAGNETOM Vida which was ported to the subject devices MAGNETOM Terra and MAGNETOM Terra.X without significant modifications. The training and testing from the reference devices still fits.
- Test setup: Equipment: 1.5T and 3T MRI scanners; Protocols: Representative protocols (T1, T2 and PD with and without fat saturation) which have been altered (e.g. to increase SNR, increase resolution or reduced acceleration); Body regions: a broad range of different body regions.
- Sample size: Included in training/validation splits. "The impact of the network has been characterized by several quality metrics... In addition, the feature has been verified and validated by inhouse tests."
- Dataset split: "Validation: 2,057 slices" (used for evaluation during training).
- Sample source: in-house measurements.
- Patient Characteristics: Not specified.
- Reference standard: The acquired datasets represent the ground truth for the training and validation. Input data was retrospectively created from the ground truth by data manipulation. k-space data has been cropped such that only the center part of the data was used as input. With this method corresponding low-resolution data as input and high-resolution data as output / ground truth were created for training and validation.
Deep RxE:
- Test setup: Equipment: 7T MRI MAGNETOM Terra and MAGNETOM Terra.X scanners; Protocols: Representative protocols (T1, T2 and PD with and without fat saturation) which have been altered (e.g., to increase SNR, increase resolution or using acceleration techniques or without acceleration); Body regions: head (44%) and knee (56%); Used coils: - 1Tx32Rx Head Coil 7T Clinic / per system - research 8Tx32Rx Head / per system - 1Tx28Rx Knee Coil 7T Clinic / per system - 23Na 1Tx32Rx Head 7T.
- Sample size: 143,947/2410 (2D slices / 3D volumes).
- Dataset split: Validation and test: 23,992/404 (2D/3D). All data from the two MR systems were separated into independent training, validation and test datasets.
- Patient Characteristics: Gender distribution: female: 56%; male: 41%; phantom: 3%. Age: group ranges from 20 - 80 years. Clinical subgroups: No have been defined for the datasets. Please note: Due to the network architecture, attributes like gender, age and ethnicity are not relevant to the training data.
- Reference standard: Applying three different methods for bias field correction to the data, homodyne filtering, N4 and UNICORN.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Deep Resolve Boost:
- Study type: Performance characterization using quality metrics and visual inspection, followed by clinical evaluation with cooperation partners.
- Sample size: 26,473 2D slices (6206 2D slices acquired at 7T).
- Key results: 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 artefacts 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 co-operation partners. In a total of seven peer-reviewed publications, the images were successfully scanned on 1.5T and 3T. The investigations covered following body regions: prostate, abdomen, liver, knee, hip, ankle, shoulder, hand, and lumbar spine. All publications have 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. In most cases the new algorithm has been used to acquire images faster and significant time savings are reported.
Deep Resolve Sharp:
- Study type: Performance characterization using quality metrics, visual rating, and intensity profile comparisons.
- Sample size: 13,977 2D slices.
- Key results: 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 inhouse 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 RxE:
- Study type: Two-step test procedure including validation during training and system tests. Clinical study.
- Sample size: 143,947/2410 (2D slices / 3D volumes).
- Key results:
-
- During training, the test data set was used to validate how the network performed on unseen data.
-
- During system tests, the standard deviation was determined, and the RMS error was calculated (against the ground truth). 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.
-
General Clinical Studies:
- On the predicate device MAGNETOM Terra, a clinical study of 35 individuals was conducted to determine the nerve stimulation thresholds used to limit the gradient system output. This study is still valid for the subject devices.
- Radiologist's evaluation reports from two U.S. board-certified radiologists have been provided, comparing subject and predicate/reference device images.
- A clinical investigation was conducted that covered parallel transmission technique, applications with faster image acquisition, Deep learning approaches and additional methods to enhance the clinical application range. The conclusion was that the subject device can be used in the clinical routine with all applications and protocols examined.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Deep Resolve Boost: Peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).
Deep Resolve Sharp: Peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and perceptual loss.
Deep RxE: NRMSE (achieved 11% for 2D network and 8.7% for 3D network against ground truth), standard deviation, RMS error.
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 892.1000 Magnetic resonance diagnostic device.
(a)
Identification. A magnetic resonance diagnostic device is intended for general diagnostic use to present images which reflect the spatial distribution and/or magnetic resonance spectra which reflect frequency and distribution of nuclei exhibiting nuclear magnetic resonance. Other physical parameters derived from the images and/or spectra may also be produced. The device includes hydrogen-1 (proton) imaging, sodium-23 imaging, hydrogen-1 spectroscopy, phosphorus-31 spectroscopy, and chemical shift imaging (preserving simultaneous frequency and spatial information).(b)
Classification. Class II (special controls). A magnetic resonance imaging disposable kit intended for use with a magnetic resonance diagnostic device only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.
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March 22, 2024
Siemens Medical Solutions USA, Inc. Milind Dhamankar Clinical Affairs and Regulatory Professional 40 Liberty Boulevard Malvern, Pennsylvania 19355
Re: K232322
Trade/Device Name: MAGNETOM Terra; MAGNETOM Terra.X Regulation Number: 21 CFR 892.1000 Regulation Name: Magnetic Resonance Diagnostic Device Regulatory Class: Class II Product Code: LNH, LNI, MOS Dated: March 1, 2024 Received: March 1, 2024
Dear Milind Dhamankar:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you. however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
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Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Daniel M. Krainak, Ph.D, Assistant Director DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
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Indications for Use
510(k) Number (if known) K232322
Device Name MAGNETOM Terra and MAGNETOM Terra.X
Indications for Use (Describe)
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.
Type of Use (Select one or both, as applicable)
| ☑ Prescription Use (Part 21 CER 801 Subpart D) |
|---|
| ☐ Over The Counter Use (21 CER 801 Subpart C) |
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510(k) Summary
This summary of 510(k) safety and effectiveness information is being submitted in accordance with the requirements of the Safe Medical Devices Act 1990 and 21 CFR § 807.92.
General Information 1.
| Establishment: | Siemens Medical Solutions USA, Inc.
40 Liberty Boulevard
Malvern, PA 19355, USA
Registration Number: 2240869 |
|----------------|-----------------------------------------------------------------------------------------------------------------------|
| Date Prepared: | March 1, 2024 |
- Manufacturer: Siemens Healthcare GmbH Henkestrasse 127 91052 Erlangen Germany Registration Number: 3002808157
2. Contact Information
Milind Dhamankar, M.D. Clinical Affairs and Regulatory Professional Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19355, USA Cell: +1 (610) 517-9484 Phone: +1 (610) 448-6467
Device Name and Classification 3.
| Device/ Trade name: | MAGNETOM Terra |
|---|---|
| Classification Name: | Magnetic Resonance Diagnostic Device (MRDD) |
| Classification Panel: | Radiology |
| CFR Code: | 21 CFR § 892.1000 |
| Classification: | II |
| Product Code: | Primary: LNH |
| Secondary: LNI, MOS |
Device/ Trade name: MAGNETOM Terra.X Classification Name: Magnetic Resonance Diagnostic Device (MRDD)
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| Classification Panel: | Radiology |
|---|---|
| CFR Code: | 21 CFR § 892.1000 |
| Classification: | II |
| Product Code: | Primary: LNH |
| Secondary: LNI, MOS |
Legally Marketed Predicate Device ব
| Trade name: | MAGNETOM Terra |
|---|---|
| 510(k) Number: | K183222 |
| Clearance Date: | February 15, 2019 |
| Classification Name: | Magnetic Resonance Diagnostic Device (MRDD) |
| Classification Panel: | Radiology |
| CFR Code: | 21 CFR § 892.1000 |
| Classification: | II |
| Product Code: | Primary: LNH |
| Secondary: LNI, MOS |
5. Intended Use / Indications for Use
The indications for use for the subject devices is modified compared to the predicate device to accurately represent the weight limits in individual coils' intended population compared to the predicate device:
Indications for Use:
The MAGNETOM 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 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.
6. Device Description
MAGNETOM Terra and MAGNETOM Terra.X with software syngo MR XA60A include new and modified hardware and software compared to the predicate
5
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) (8Tx-1Tx Combiner Interface):
- Hardware component adaptor to connect single Tx local RF coils to the pTx system, combines the 8Tx channels to single channel mode.
- MC-PALI: New component for monitoring RF power on all transmit channels.
- GSSU control unit: The new GSSU including the new cardiac stimulation monitor board supports and enables the cardiac simulation monitoring separately and nearly independently from the OPS.
New Coil
-
The 8Tx32Rx Head coil is a 1H (proton) RF-coil with 8 transmit and 32 receive channels used for head applications usable in the 8ch pTx Mode. It features a 8TX transmit shell. There are enhancements to the software that controls SAR. By enabling B1 shimming or full 8ch pTx operation, the new 8TX array allows improved B1 transmit characteristics which increase both contrast and homogeneity in the brain.
Modified Hardware -
Main components such as: ।
- Upgrade of GPA to increase the gradient performance which leads to an improvement of the imaging.
- New Host computer hardware with increased performance and "dynamic i research labeling" in the GUI
- New MaRS computer hardware as successor of previous MaRS computer
- Upgrade the SEP to the newest cooling cabinet series
- The new shim cabinet ASC5 replaces two ACS4 shim cabinets. -
-
। Other components such as:
- RFPA: Modified to be used for 8ch pTx. Therefore, components for 1ch Tx i are obsolete and were removed and control- and power modules are combined in one module instead.
- Use of a common MR component which provides basic functionality that is required for all MAGNETOM system types. RFCEL 2G light houses common MR components compatible with the MR environment but no specific 7T functionality is implemented and it is reduced in its functionality.
- The multi-nuclear (MNO) option has been modified to be used in combination with the parallel transmit (pTx) technology. Therefore, components used for the SAR supervision are changed. Other components such as the coils are unchanged.
- OPS module: The OPS implements the SAFE model using digital filters now. The parametrization of the filters and the processes are independent and separate for the peripheral and cardiac stimulation supervision.
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- Cover with UI update on PDD: The cover has been modified to bring the system up to the Siemens Healthineers Design incl. the Numaris/X platform on the patent data display interface.
Software
New Features and Applications
- Static B1 shimming: B1 Shimming is available for nearby all the sequences i and can be used offering an improved B1 homogeneity especially in the brain.
- TrueForm (1ch compatibility mode): Software function to run a multichannel pTx coil in a virtual single channel mode.
- Deep Resolve Boost is a novel deep learning-based image reconstruction algorithm for 2D TSE data, which reconstructs images from k-space raw-data.
- Deep Resolve Gain is a reconstruction option which enables targeted denoising, resulting in improved SNR of the scanned images. The functionality is available for specific pulse sequence types now.
- Deep Resolve Sharp is a deep learning-based interpolation algorithm which । increases the perceived sharpness of the interpolated images. The functionality has been ported from the reference device MAGNETOM Vida to the subject devices MAGNETOM Terra and MAGNETOM Terra.X.
- Bias field correction (marketing name: Deep RxE) is a deep learning image । filter. The intention is to correct images by reducing residual B1 inhomogeneities (similar to a pre-scan normalize) to improve image quality for head and extremity imaging.
- The new BEAT pulse sequence type provides a combination of Time-Of-Flight -(TOF) MR angiography and Compressed Sensing (CS) to reduce measurement time.
- BLADE diffusion is a multi-shot imaging method based on TSE or TGSE । (when EPI factor > 1) readout and a BLADE trajectory with diffusion preparation to enable diffusion weighted imaging with reduced sensitivity to B0 inhomogeneity and reduced T2 decay caused image blurring.
- -The PETRA pulse sequence type generates no additional perceivable noise above the general noise in the background.
- TSE DIXON is a modified TSE (turbo spin echo) pulse sequence type for Dixon imaqinq.
- The Compressed Sensing (CS) functionality is now available for the SPACE pulse sequence type. Scan time can be reduced by the incoherent undersampling of the k-space data. The usage of CS as well as the acceleration factor and other options can be freely selected by the user.
- The Compressed Sensing (CS) functionality is now available for the TFL pulse sequence type. Scan time can be reduced by the incoherent undersampling of the k-space data. The usage of CS as well as the acceleration factor and other options can be freely selected by the user.
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- । IDEA which is a set of tools for developing sequences, image reconstruction programs, etc. is now available as well.
- The Scientific Suite supports scientific users by providing easy access to application-specific data for further processing and advanced image calculus.
Modified Features and Applications
- EP2D DIFF and TSE with SliceAdjust: SliceAdjust is a framework which । allows applying adjustment settings dynamically for individual slice measures during the acquisition.
- The Turbo Flash (TFL) is a GRE-based pulse sequence type which generates T1w, and FWS images. With dynamic pTx more coverage and a homogenous contrast is possible.
Modified Software / Platform
- Stimulation monitoring: Peripheral nerve stimulation and cardiac stimulation limits are supervised via the SAFE model, but with separate parameterizations now. Both SAFE models run independently.
- "dynamic research labeling": new Host computer hardware with increased performance and "dynamic research labeling" in the GUI
Other Modifications and / or Minor Changes
- Intended use, SAR Calculation and Weight limit reduction for 31P/1H । TxRx Flex Loop Coil: Adaptation of the system intended use, by moving the weight limit from the system intended use to the RF coil intended use and adapting of SAR calculation. In addition, change of the intended population for the 31P/1H TxRx Flex Loop 7T coil.
- X-upgrade for MAGNETOM Terra to MAGNETOM Terra.X (marked as new device): The MAGNETOM Terra.X is a new 7T MRI System which is the result of an improvement of the MAGNETOM Terra - either ex-factory or by an upgrade on-site.
- Provide secure MR scanner setup for DoD (Department of Defense) -Information Assurance compliance.
7. Substantial Equivalence
MAGNETOM Terra and MAGNETOM Terra.X with software syngo MR XA60A are substantially equivalent to the following predicate device:
| Predicate Device | FDA Clearance Number
and Date | Product
Code | Manufacturer |
|--------------------------------------|---------------------------------------|-----------------|-----------------------------------------|
| MAGNETOM Terra with
syngo MR E12U | K183222,
cleared February 15, 2019 | LNH | Siemens AG / Siemens
Healthcare GmbH |
| | | LNI, MOS | |
MAGNETOM Terra and MAGNETOM Terra.X with software syngo MR XA60A include hardware and software already cleared on the following reference devices:
Traditional Premarket Notification 510(k)
8
| Reference Devices | FDA Clearance Number
and Date | Product
Code | Manufacturer |
|-------------------------------------------------|---------------------------------------|-----------------|----------------------------|
| MAGNETOM Vida with
software syngo MR XA50A | K213693,
cleared February 25, 2022 | LNH
LNI, MOS | Siemens Healthcare
GmbH |
| MAGNETOM Prisma with
software syngo MR XA30A | K202014,
cleared September 8, 2020 | LNH
LNI, MOS | Siemens Healthcare
GmbH |
| syngo.via VB40A | K191040,
cleared May 16, 2019 | LLZ | Siemens Healthcare
GmbH |
Comparison of technological Characteristics with the Predicate Device 8.
The subject devices, MAGNETOM Terra and MAGNETOM Terra.X with software syngo MR XA60A, are substantially equivalent to the predicate device with regard to the operational environment, programming language, operating system and performance.
The subject devices conform to the standard for medical device software (IEC 62304) and other relevant IEC and NEMA standards.
While there are some differences in technological characteristics between the subject devices and predicate device, including new and modified hardware and software, these differences have been tested and the conclusions from the nonclinical data suggest that the features bear an equivalent safety and performance profile to that of the predicate device.
9. Nonclinical Tests
The following performance testing was conducted on the subject devices.
| Performance Test | Tested Hardware or Software | Source/Rationale for test |
|---|---|---|
| Sample clinical images | coils, new and modified | |
| software features | Guidance for Submission of | |
| Premarket Notifications for | ||
| Magnetic Resonance | ||
| Diagnostic Devices | ||
| Image quality assessments by | ||
| sample clinical images. In | ||
| some cases a comparison of | ||
| the image quality was made. | - new / modified pulse | |
| sequence types and | ||
| algorithms. |
- comparison images between
the new / modified features
and the predicate device
features | Guidance for the Content of
Premarket Submissions for
Software Contained in Medical
Devices |
| Performance bench test
Software verification and
validation | new and modified hardware and
software features | |
| Electrical, mechanical,
structural, and related system
safety test | complete system MAGNETOM
Terra and MAGNETOM Terra.X | - AAMI / ANSI ES60601-1 - IEC 60601-2-33 |
| Electrical safety and
electromagnetic compatibility
(EMC) | complete system MAGNETOM
Terra.X | IEC 60601-1-2 |
Traditional Premarket Notification 510(k)
9
The results from each set of tests demonstrate that the subject devices perform as intended and are thus substantially equivalent to the predicate device to which it has been compared.
Below table shows an executive summary of training and validation dataset of Al features (Deep Resolve Boost, Deep Resolve Sharp and Deep RxE) in the subject devices.
| Deep Resolve Boost | Deep Resolve Sharp | Deep RxE | |
|---|---|---|---|
| General / additional information | n.a. | The same function as on reference device | |
| MAGNETOM Vida which was ported to the subject devices | |||
| MAGNETOM Terra and MAGNETOM Terra.X | |||
| without significant modifications. The training and testing from the reference devices still fits. | A 4-step approach was performed: |
- 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.
- Automated unit-tests are set-up to test the consistency of the generated output to a previously defined reference output
- 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)
- The trained final network was used in the clinical study. |
| Test setup | Equipment: 7T MRI scanners (from the predicate device)
Protocols: Representative protocols (T1, T2 and PD | Equipment: 1.5T and 3T MRI scanners
Protocols: Representative protocols (T1, T2 and PD with and | Equipment: 7T MRI MAGNETOM Terra and MAGNETOM Terra.X scanners |
| | | | |
| | with and without fat
saturation), which have
been altered (e.g., to
increase SNR, increase
resolution or reduced
acceleration).
Body regions: head and
knee | without fat saturation)
which have been altered
(e.g. to increase SNR,
increase resolution or
reduced acceleration).
Body regions: a broad
range of different body
regions | Protocols:
Representative
protocols (T1, T2 and
PD with and without fat
saturation) which have
been altered (e.g., to
increase SNR,
increase resolution or
using acceleration
techniques or without
acceleration).
Body regions: head
(44%) and knee (56%)
Used coils:
- 1Tx32Rx Head Coil
7T Clinic / per system |
| | Sample size: 26,473 2D
slices (6206 2D slices
acquired at 7T) | | - research 8Tx32Rx
Head / per system - 1Tx28Rx Knee Coil
7T Clinic / per system - 23Na 1Tx32Rx Head
7T
Sample size: |
| | | Sample size: 13,977 2D
slices | 143,947/2410 (2D
slices / 3D volumes) |
| | Dataset split: - Training: 24,599 slices
- Validation: 1,874 slices | Dataset split:
- Training: 11,920 slices
- Validation: 2,057 slices | Dataset split:
- Training:
119,955/2007 (2D/3D) - Validation and test:
23,992/404 (2D/3D) |
| | Note: Data split maintained similar data distribution
(e.g., contrast, orientation, field strength, ...) in both
training and validation datasets. | | All data from the two
MR systems were
separated into
independent training,
validation and test
datasets |
| | Sample source: in-house
measurements and
collaboration partners | Sample source: in-house measurements | |
| Patient
Characteristics | Clinical subgroups: No clinical subgroups have
been defined for the datasets. | | Gender distribution: - female: 56%
- male: 41%
- phantom: 3% |
| | | | Age: group ranges
from 20 - 80 years.
Clinical subgroups: No |
| | | | have been defined for
the datasets. |
| | Please note: due to reasons of data privacy, we did
not record how many individuals the datasets
belong to. Gender, age, and ethnicity distribution
were also not recorded during data collection. Due
to the network architecture, attributes like gender,
age and ethnicity are not relevant to the training
data. | | Please note: Due to
the network
architecture, attributes
like gender, age and
ethnicity are not
relevant to the training
data. |
| Confounder | The input and output variables of the network have been derived from the
same dataset so that no confounders exist for the training methodology. | | |
| Reference standard | 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. This
process includes 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. | The acquired datasets
represent the ground
truth for the training and
validation. Input data
was retrospectively
created from the ground
truth by data
manipulation. k-space
data has been cropped
such that only the center
part of the data was
used as input. With this
method corresponding
low-resolution data as
input and high-resolution
data as output / ground
truth were created for
training and validation. | Applying three different
methods for bias field
correction to the data,
homodyne filtering, N4
and UNICORN. |
| Test statistics and
test results | 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 artefacts 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 | 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 inhouse
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. | Two step test
procedure.
- During training, the
test data set was
used to validate
how the network
performed on
unseen data. - During system tests,
the standard
deviation was
determined, and the
RMS error was
calculated (against
the ground truth).
The tests show that
Deep RxE increases
image homogeneity in
a reproduceable way
on the receive profile.
Images acquired with
Deep RxE (DL bias |
Training and validation dataset of Al features
Traditional Premarket Notification 510(k)
10
Traditional Premarket Notification 510(k)
11
Traditional Premarket Notification 510(k)
12
| were successfully
scanned on 1.5T and 3T.
The investigations
covered following body
regions: prostate,
abdomen, liver, knee, hip,
ankle, shoulder, hand,
and lumbar spine. All
publications have
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. In most cases
the new algorithm has
been used to acquire
images faster and
significant time savings
are reported. | field correction) are
rated better for image
quality than the ones
acquired without it in
the clinical study that
was conducted. |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
|---|
10. Clinical Tests / Publications
On the predicate device MAGNETOM Terra, a clinical study of 35 individuals was conducted to determine the nerve stimulation thresholds used to limit the gradient system output. The observed parameters were used to set the PNS (Peripheral Nerve Stimulation) threshold level which is required in IEC 60601-2-33. This study is still valid for the subject devices MAGNETOM Terra and MAGNETOM Terra.X as the same gradient coil is used.
In addition to providing clinical sample images for some software modifications and the new 8Tx32Rx Head of the subject device, radiologist's evaluation reports from two U.S. board-certified radiologists have been provided. Where necessary the radiologists compared the subject and the predicate / reference device images. The radiologist's evaluation reports have comments on any observed artifacts and concerns those have been communicated with the user via labeling material.
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A clinical investigation was conducted that covered parallel transmission technique, applications with faster image acquisition, Deep learning approaches and additional methods to enhance the clinical application range and a report summarizing the results was provided. The conclusion of the test was that the subject device can be used in the clinical routine with all applications and protocols examined in the clinical investigation according to the investigation plan.
Clinical publications are referenced to provide information on the use of the following features and functions.
| Feature / Function | Clinical Publication |
|---|---|
| Cloos MA, Boulant N, Luong M, Ferrand G, Giacomini E, Le Bihan D, | |
| Amadon A. kT -points: short three-dimensional tailored RF pulses for flip- | |
| angle homogenization over an extended volume. Magn Reson Med. 2012 | |
| Jan;67(1):72-80. doi: 10.1002/mrm.22978. Epub 2011 May 16. PMID: |
- |
| dynamic pTx for TFL
(for MAGNETOM
Terra.X only) | Majewski K. Simultaneous optimization of radio frequency and gradient
waveforms with exact Hessians and slew rate constraints applied to kT-
points excitation. Journal of Magnetic Resonance. 2021 May 1;
326:106941. |
| | Herrler, J, Liebig, P, Gumbrecht, R, et al. Fast online-customized
(FOCUS) parallel transmission pulses: A combination of universal pulses
and individual optimization. Magn Reson Med. 2021; 85: 3140-3153.
https://doi.org/10.1002/mrm.28643 |
| | Tanner, Mark, Giulio Gambarota, Tobias Kober, Gunnar Krueger, David
Erritzoe, José P Marques, and Rexford Newbould. 2012. 'Fluid and White
Matter Suppression with the MP2RAGE Sequence.' Journal of Magnetic
Resonance Imaging: JMRI 35 (5): 1063-70.
https://doi.org/10.1002/jmri.23532. |
| Weight limit
reduction for 31P/1H | Parasoglou, P, et al., 3D-Mapping of Phosphocreatine Concentration in
the Human Calf Muscle at 7T: Comparison to 3T, Magn Reson Med.
Author manuscript; available in PMC 2014 December 01, 2013
December, 70(6) |
| TxRx Flex Loop Coil | Hooijmans M. T., et al., Spatially localized phosphorous metabolism of
skeletal muscle in Duchenne muscular dystrophy patients: 24±month
follow-up, https://doi.org/10.1371/journal.pone.0182086, August 1, 2017 |
11. Safety and Effectiveness
The device labeling contains instructions for use and any necessary cautions and warnings to ensure safe and effective use of the device.
Risk Management is ensured via a risk analysis in compliance with ISO 14971. to identify and provide mitigation of potential hazards early in the design cvcle and continuously throughout the development of the product. Siemens Healthcare GmbH adheres to recognized and established industry standards,
14
such as the IEC 60601-1 series, to minimize electrical and mechanical hazards. Furthermore, the device is intended for healthcare professionals familiar with and responsible for the acquisition and post processing of magnetic resonance images.
MAGNETOM Terra and MAGNETOM Terra.X with software syngo MR XA60A conform to the following FDA recognized and international IEC, ISO and NEMA standards:
| Recognition
Number | Product
Area | Title of Standard | Reference
Number and date | Standards
Development
Organization |
|-----------------------|--------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|------------------------------------------|
| 19-4 | General II
(ES/EMC) | Medical electrical equipment -
Part 1: General requirements for
basic safety and essential
performance (IEC 60601-
1:2005, MOD) | ES60601-
1:2005/(R)2012
and A1:2012,
C1:2009/(R)2012
and
A2:2010/(R)2012
(Consolidated Text) | ANSI AAMI |
| 19-8 | General | Medical electrical equipment -
Part 1-2: General requirements
for basic safety and essential
performance - Collateral
Standard: Electromagnetic
disturbances - Requirements
and tests | 60601-1-2, Ed.
4.0:2014 | IEC |
| 12-295 | Radiology | Medical electrical equipment -
Part 2-33: Particular
requirements for the basic
safety and essential
performance of magnetic
resonance equipment for
medical diagnosis | 60601-2-33, Ed.
3.2:2015 | IEC |
| 5-125 | General I
(QS/RM) | Medical devices - Application of
risk management to medical
devices | 14971 Third edition
2019-12 | ISO |
| 5-114 | General I
(QS/RM) | Medical devices - Part 1:
Application of usability
engineering to medical devices
[Including CORRIGENDUM 1
(2016)] | 62366-1 Edition 1.0
2015-02 | IEC |
| 13-79 | Software/
Informatics | Medical device software -
Software life cycle processes | 62304 Edition 1.1
2015-06
CONSOLIDATED
VERSION | IEC |
| 2-258 | Biocompati
bility | Biological evaluation of medical
devices - part 1: evaluation and
testing within a risk
management process | 10993-1 Fifth
edition 2018-08 | ISO |
Traditional Premarket Notification 510(k)
15
Image /page/15/Figure/0 description: The image shows the Siemens Healthineers logo. The word "SIEMENS" is written in teal, and the word "Healthineers" is written in orange below it. To the right of the word "Healthineers" is a graphic of orange dots arranged in a circular pattern.
| 12-342 | Radiology | Digital Imaging and
Communications in Medicine
(DICOM) Set | PS 3.1 - 3.20
2021e | NEMA |
|--------|-----------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------|------|
| 12-188 | Radiology | Determination of Signal-to-
Noise Ratio (SNR) in Diagnostic
Magnetic Resonance Images | MS 1:2008 (R2020) | NEMA |
| 12-196 | Radiology | Determination of Two-
dimensional Geometric
Distortion in Diagnostic
Magnetic Resonance Images | MS 2:2008 (R2020) | NEMA |
| 12-187 | Radiology | Determination of Image
Uniformity in Diagnostic
Magnetic Resonance Images | MS 3:2008 (R2020) | NEMA |
| 12-232 | Radiology | Acoustic Noise Measurement
Procedure for Diagnosing
Magnetic Resonance Imaging
Devices | MS 4:2010 | NEMA |
| 12-322 | Radiology | Determination of Slice
Thickness in Diagnostic
Magnetic Resonance Imaging | MS 5:2018 | NEMA |
| 12-195 | Radiology | Determination of Signal-to-
Noise Ratio and Image
Uniformity for Single-Channel,
Non-Volume Coils in Diagnostic
Magnetic Resonance Imaging
(MRI) | MS 6:2008 (R2014) | NEMA |
| 12-315 | Radiology | Characterization of the Specific
Absorption Rate for Magnetic
Resonance Imaging Systems | MS 8:2016 | NEMA |
| 12-288 | Radiology | Standards Publication
Characterization of Phased
Array Coils for Diagnostic
Magnetic Resonance Images | MS 9-2008 (R2020) | NEMA |
| 12-298 | Radiology | Determination of Local Specific
Absorption Rate (SAR) in
Diagnostic Magnetic Resonance
Imaging Systems | MS 10 - 2010 | NEMA |
| 12-306 | Radiology | Quantification and Mapping of
Geometric Distortion for Special
Applications | MS 12 - 2016 | NEMA |
12. Conclusion as to Substantial Equivalence
MAGNETOM Terra and MAGNETOM Terra.X with software syngo MR XA60A have the same intended use (although it is corrected) and same basic technological characteristics than the predicate device system, MAGNETOM Terra with syngo MR E12U, with respect to the magnetic resonance features and functionalities. While there are some differences in technical features compared to the predicate device, the differences have been tested and the conclusions from all verification and validation data suggest that the features bear an
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equivalent safety and performance profile to that of the predicate device and reference devices.
Siemens believes that MAGNETOM Terra and MAGNETOM Terra.X with software syngo MR XA60A are substantially equivalent to the currently marketed device MAGNETOM Terra with software syngo MR E12U (K183222, cleared on February 15, 2019).