(123 days)
The MAGNETOM system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross sectional images, spectroscopic images and/or spectra, and that displays the internal structure and/or function of the head, body, or extremities. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used. 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 MAGNETOM system may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room displays and MR Safe biopsy needles.
The subject devices, MAGNETOM Sola and MAGNETOM Altea with software syngo MR XA61A, consist of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Sola with syngo MR XA51A (K221733).
A high-level summary of the new and modified hardware and software is provided below:
Hardware
Modified Hardware:
- Host computers ((syngo MR Acquisition Workplace (MRAWP) and syngo MR Workplace (MRWP))
- MaRS (Measurement and Reconstruction System) computer – for MAGNETOM Sola only
- myExam 3D Camera
Software
New Features and Applications:
- GRE_PC
- Physiologging
- Deep Resolve Boost HASTE
- Deep Resolve Boost EPI Diffusion
- Complex Averaging
- myExam Implant Suite
Modified Features and Applications:
- OpenRecon Framework.
- BEAT_nav (re-naming only).
- Low SAR Protocols – SAR adoptive MR protocols to perform knee, spine, heart and brain examinations with 50% of the max allowed SAR values in normal mode for head and whole-body SAR.
The provided text describes the Siemens Medical Solutions USA, Inc. MAGNETOM Sola and MAGNETOM Altea with software syngo MR XA61A, which are Magnetic Resonance Diagnostic Devices (MRDD). The submission (K232535) claims substantial equivalence to a predicate device (MAGNETOM Sola with syngo MR XA51A, K221733).
Based on the provided information, the acceptance criteria and study details for the AI features (Deep Resolve Boost and Deep Resolve Sharp) are as follows:
1. Table of Acceptance Criteria and Reported Device Performance
| Feature | Acceptance Criteria (Stated) | Reported Device Performance and Metrics |
|---|---|---|
| Deep Resolve Boost | 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). Most importantly, the performance was evaluated by visual comparisons to evaluate e.g., aliasing artifacts, image sharpness and denoising levels. | Performance was evaluated by visual comparisons to evaluate aliasing artifacts, image sharpness, and denoising levels, in addition to quantitative metrics like PSNR and SSIM. The document states, "The results from each set of tests demonstrate that the devices perform as intended and are thus substantially equivalent to the predicate device to which it has been compared," implying these metrics met the internal acceptance criteria for substantial equivalence. No specific numerical thresholds are provided. |
| Deep Resolve Sharp | 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 reconstructions with and without Deep Resolve Sharp. | Performance was evaluated by visual rating and intensity profile comparisons for image sharpness, along with quantitative metrics like PSNR, SSIM, and perceptual loss. The document states, "The results from each set of tests demonstrate that the devices perform as intended and are thus substantially equivalent to the predicate device to which it has been compared," implying these metrics met the internal acceptance criteria for substantial equivalence. No specific numerical thresholds are provided. |
2. Sample Size Used for the Test Set and Data Provenance
- Deep Resolve Boost:
- TSE: more than 25,000 slices (implicitly for both training/validation and testing, as no separate test set is explicitly mentioned).
- HASTE: more than 10,000 HASTE slices (refined from TSE dataset).
- EPI Diffusion: more than 1,000,000 slices.
- Data Provenance: Retrospective creation from acquired datasets. The data "covered a broad range of body parts, contrasts, fat suppression techniques, orientations, and field strength." Country of origin is not specified but given the manufacturer (Siemens Healthcare GmbH, Germany, and Siemens Shenzhen Magnetic Resonance LTD, China) and typical medical device development, it likely includes international data.
- Deep Resolve Sharp:
- 2D images: more than 10,000 high resolution 2D images.
- Data Provenance: Retrospective creation from acquired datasets. The data "covered a broad range of body parts, contrasts, fat suppression techniques, orientations, and field strength." Country of origin is not specified.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document does not specify the number of experts or their qualifications for establishing ground truth for the test set specifically. It mentions that "visual comparisons" and "visual rating" were part of the evaluation for both Deep Resolve Boost and Deep Resolve Sharp, which implies human expert review. However, details about these experts are not provided.
4. Adjudication Method for the Test Set
The document does not explicitly state an adjudication method (e.g., 2+1, 3+1). It refers to "visual comparisons" and "visual rating" as part of the evaluation, which suggests expert review, but the process for resolving disagreements or reaching consensus is not detailed.
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 involving human readers with and without AI assistance is reported for the substantial equivalence submission. The non-clinical tests focus on performance metrics and visual comparisons of image quality produced by the AI feature versus predicate device features. The "Publications" section lists several clinical feasibility studies, but these are noted as "referenced to provide information" and are not direct evidence of human reader improvement with AI for this specific submission's evaluation. The submission states, "No clinical tests were conducted to support substantial equivalence for the subject devices."
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
Yes, standalone performance was evaluated through quantitative image quality metrics (PSNR, SSIM, perceptual loss) and direct comparison of images produced by the AI-enhanced sequences against the predicate device's features. The "Test Statistics and Test Results Summary" for both Deep Resolve Boost and Deep Resolve Sharp detail these algorithm-only evaluations.
7. The Type of Ground Truth Used
The ground truth for both Deep Resolve Boost and Deep Resolve Sharp was established from acquired datasets (raw MRI data). This data was then retrospectively manipulated to create input data for the models:
- Deep Resolve Boost: Input data was "retrospectively created from the ground truth by data manipulation and augmentation," including undersampling k-space lines, lowering SNR, and mirroring k-space data. The acquired datasets themselves "represent the ground truth for the training and validation."
- Deep Resolve Sharp: Input data was "retrospectively created from the ground truth by data manipulation," specifically by cropping k-space data to use only the center part, which created corresponding low-resolution input data and high-resolution output/ground truth data. The acquired datasets "represent the ground truth for the training and validation."
Essentially, the "ground truth" refers to the high-quality, fully sampled/non-accelerated raw or reconstructed MRI data from which the training and validation inputs were derived.
8. The Sample Size for the Training Set
The sample sizes mentioned under "Training and Validation data" are implicitly for training, as they refer to the datasets from which both training and validation data were derived:
- Deep Resolve Boost:
- TSE: more than 25,000 slices
- HASTE: more than 10,000 HASTE slices (refined)
- EPI Diffusion: more than 1,000,000 slices
- Deep Resolve Sharp:
- more than 10,000 high resolution 2D images
9. How the Ground Truth for the Training Set Was Established
The ground truth for the training set was established from acquired datasets (raw MRI data). As explained in point 7, this involved:
- Deep Resolve Boost: Using the acquired datasets as the "ground truth." Input data for training was then generated by manipulating this ground truth (e.g., undersampling, adding noise).
- Deep Resolve Sharp: Using the acquired datasets as the "ground truth." Input data was then generated by manipulating the k-space data of the ground truth to create corresponding low-resolution inputs and high-resolution ground truth outputs for the model.
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December 22, 2023
Siemens Medical Solutions USA, Inc. Milind Dhamankar Clinical Affairs Professional 40 Liberty Boulevard Malvern, PA 19355
Re: K232535
Trade/Device Name: MAGNETOM Sola: MAGNETOM Altea Regulation Number: 21 CFR 892.1000 Regulation Name: Magnetic resonance diagnostic device Regulatory Class: Class II Product Code: LNH, LNI, MOS Dated: December 6, 2023 Received: December 6, 2023
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,
signature
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
Submission Number (if known)
K232535
Device Name
MAGNETOM Sola: MAGNETOM Altea
Indications for Use (Describe)
The MAGNETOM system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross sectional images, spectroscopic images and/or spectra, and that displays the internal structure and/or function of the head, body, or extremities. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used. These images and/or spectra and the physical parameters derived from the images and/or spectra when interpreted by a trained physician vield information that may assist in diagnosis.
The MAGNETOM system may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room displays and MR Safe biopsy needles.
Type of Use (Select one or both, as applicable)
| Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 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.
1. General Information
| Establishment: | Siemens Medical Solutions USA, Inc.40 Liberty BoulevardMalvern, PA 19355, USARegistration Number: 2240869 |
|---|---|
| Date Prepared: | August 09, 2023 |
| Manufacturer: | Siemens Healthcare GmbHHenkestr. 12791052 ErlangenGermanyRegistration Number: 3002808157Siemens Shenzhen Magnetic Resonance LTDSiemens MRI CenterHi-Tech Industrial Park (middle)Gaoxin C. Ave., 2ndShenzhen 518057P.R. CHINARegistration Number: 3004754211 |
2. Contact Information
Milind Dhamankar Clinical Affairs Professional Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19355, USA Phone: +1(610) 517-9484 Fax: (610) 448-6547 E-mail: milind.dhamankar@siemens-healthineers.com
-
- Device Name and Classification
- Device/ Trade name: MAGNETOM Sola
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| MAGNETOM Altea | |
|---|---|
| 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 |
4. Legally Marketed Predicate and Reference Device
4.1. Predicate Device
| Trade name: | MAGNETOM Sola |
|---|---|
| 510(k) Number: | K221733 |
| Classification Name: | Magnetic Resonance Diagnostic Device (MRDD |
| Classification Panel: | Radiology |
| CFR Code: | 21 CFR § 892.1000 |
| Classification: | II |
| Product Code: | Primary: LNHSecondary: LNI, MOS |
4.2. Reference Device
| Trade name: | MAGNETOM Altea |
|---|---|
| 510(k) Number: | K221733 |
| Classification Name: | Magnetic Resonance Diagnostic Device (MRDD) |
| Classification Panel: | Radiology |
| CFR Code: | 21 CFR § 892.1000 |
| Classification: | II |
| Product Code: | Primary: LNHSecondary: LNI, MOS |
4. Intended Use / Indications for Use
The indications for use for the subject devices are the same as the predicate device:
The MAGNETOM system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross sectional images, spectroscopic images and/or spectra, and that displays the internal structure and/or function of the head, body, or extremities. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used. 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 MAGNETOM system may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room displays and MR Safe biopsy needles.
5. Device Description
The subject devices, MAGNETOM Sola and MAGNETOM Altea with software syngo MR XA61A, consist of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Sola with syngo MR XA51A (K221733).
A high-level summary of the new and modified hardware and software is provided below:
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Hardware
Modified Hardware:
- -Host computers ((syngo MR Acquisition Workplace (MRAWP) and syngo MR Workplace (MRWP))
- MaRS (Measurement and Reconstruction System) computer – for MAGNETOM Sola only
- । myExam 3D Camera
Software
New Features and Applications:
- -GRE_PC
- Physiologging ।
- Deep Resolve Boost HASTE l
- -Deep Resolve Boost EPI Diffusion
- -Complex Averaging
- myExam Implant Suite
Modified Features and Applications:
- OpenRecon Framework. -
- -BEAT_nav (re-naming only).
- -Low SAR Protocols – SAR adoptive MR protocols to perform knee, spine, heart and brain examinations with 50% of the max allowed SAR values in normal mode for head and whole-body SAR.
6. Substantial Equivalence
MAGNETOM Sola and MAGNETOM Altea with software syngo MR XA61A are substantially equivalent to the following predicate device:
| Predicate Device | FDA Clearance Number andDate | ProductCode | Manufacturer |
|---|---|---|---|
| MAGNETOM Sola with syngoMR XA51A | K221733 on September 13,2022 | LNH,LNI, MOS | Siemens Healthcare GmbH |
| Reference Device | FDA Clearance Number andDate | ProductCode | Manufacturer |
| MAGNETOM Altea with syngoMR XA51A | K221733 on September 13,2022 | LNH,LNI, MOS | Siemens Healthcare GmbH |
7. Technological Characteristics
The subject devices, MAGNETOM Sola and MAGNETOM Altea with software syngo MR XA61A, 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.
There are some differences in technological characteristics between the subject device and predicate device, including new and modified hardware/software. Here is summary of differences:
Summary hardware comparison table for the subject and predicate/reference device
| Hardware | Subject Devices | Predicate Device | Reference Devices |
|---|---|---|---|
| ---------- | ----------------- | ------------------ | ------------------- |
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| MAGNETOM SolaMAGNETOM Alteawith software syngo MRXA61A | MAGNETOM Sola withsyngo MR XA51A(K221733) | MAGNETOM Altea withsyngo MR XA51A(K221733) | |
|---|---|---|---|
| Magnet System | Yes | Yes | Yes |
| RF System | Yes | Yes | Yes |
| Transmissiontechnique | Yes | Yes | Yes |
| Gradient System | Yes | Yes | Yes |
| Patient Table | Yes | Yes | Yes |
| Multi-Nuclear | |||
| Option - SupportedNuclei | No | No | No |
| Computer | YesModified compared topredicate device:- New MRAWP andMRWP- New MaRS hardwarefor MAGNETOM Sola | Yes | Yes |
| Coils | Yes | Yes | Yes |
| Other HWcomponents | YesModified compared topredicate device:- myExam 3D Camerafunctionality extension | Yes | Yes |
Summary software comparison table for the subject and predicate devices
| Subject Devices | Predicate Device | |
|---|---|---|
| Software | MAGNETOM SolaMAGNETOM Alteawith software syngo MR XA61A | MAGNETOM Sola with syngoMR XA51A(K221733) |
| Sequences | ||
| SE-based pulse sequence types | New feature- Deep Resolve Boost forHASTE | Yes |
| GRE-based/Steady-State pulsesequence types | New or modified pulsesequences:- BEAT_NAV pulse sequencere-naming- GRE_PC new pulse sequence | Yes |
| EPI-based pulse sequence types | New features- Physiologging for EP2D_BOLDand EP2D_PACE- Deep Resolve Boost forEP2D DIFF | Yes |
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| - Complex Averaging forEP2D_DIFF | ||
|---|---|---|
| Spectroscopy pulse sequencetypes | Yes | Yes |
| Feature and Applications | ||
| Other features andapplications such as:-Application Suites-myExam Assists-Other ImagingApplications | New feature:- myExam Implant Suite | Yes |
| User interface and userinteraction | Yes | Yes |
| Viewing and post-processing | Yes | Yes |
| Workflow and software utilization | Yes | Yes |
| Patient Management | Yes | Yes |
| Scan Modes and Pulse Sequences | Yes | Yes |
| Scanning | Yes | Yes |
| Reconstruction | Modified feature:- OpenRecon Framework | Yes |
| Image Display | Yes | Yes |
| File/Data Management | Yes | Yes |
The differences have been tested and the conclusion from the non-clinical data suggests that the features bear an equivalent safety and performance profile to that of the predicate device.
8. Nonclinical Tests
The following performance testing was conducted on the subject devices:
| Performance Test | Tested Hardware or Software | Source/Rationale for test |
|---|---|---|
| Software verification and validation | New or modified software features | Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices |
| Sample clinical images | New or modified software features | Guidance for submission of Premarket Notifications for Magnetic Resonance Diagnostic Devices |
| Image quality assessment by sample clinical images | - new / modified pulse sequence types.- comparison images between the new / modified features and the predicate device features | |
| Physiologging Verification Report | Physiologging | New Feature Introduction |
| myExam 3D Camera Validation and Verification Report | myExam 3D Camera | Modified hardware |
| myExam Implant Suite Validation and Verification Report | myExam Implant Suite | New feature Introduction |
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AI Features/Applications training and validation:
The information below shows an executive summary of training and validation dataset of the AI features:
| Deep Resolve Boost: | Deep Resolve Sharp: | |
|---|---|---|
| Training andValidation data | TSE: more than 25,000 slices HASTE: pre-trained on the TSE datasetand refined with more than 10,000HASTE slices EPI Diffusion: more than 1,000,000slices The data covered a broad range of bodyparts, contrasts, fat suppression techniques,orientations, and field strength. | on more than 10,000 high resolution 2Dimages.The data covered a broad range of bodyparts, contrasts, fat suppression techniques,orientations, and field strength. |
| Test Statistics andTest ResultsSummary | The impact of the network has beencharacterized by several quality metricssuch as peak signal-to-noise ratio (PSNR)and structural similarity index (SSIM). Mostimportantly, the performance wasevaluated by visual comparisons toevaluate e.g., aliasing artifacts, imagesharpness and denoising levels. | The impact of the network has beencharacterized by several quality metricssuch as peak signal-to-noise ratio (PSNR),structural similarity index (SSIM), andperceptual loss. In addition, the feature hasbeen verified and validated by inhousetests. These tests include visual rating andan evaluation of image sharpness byintensity profile comparisons ofreconstructions with and without DeepResolve Sharp. |
| Equipment | 1.5T and 3T MRI systems | |
| Clinical Subgroups | No clinical subgroups have been defined for the collected dataset. | |
| DemographicDistribution | Due to reasons of data privacy, we did not record gender, age and ethnicity during datacollection. | |
| Reference Standard | The acquired datasets (as described above)represent the ground truth for the trainingand validation. Input data wasretrospectively created from the groundtruth by data manipulation andaugmentation.This process includes further under-sampling of the data by discarding k-spacelines, lowering of the SNR level by additionRestricted of noise and mirroring of k-spacedata. | The acquired datasets representthe ground truth for the trainingand validation. Input data wasretrospectively created from theground truth by datamanipulation. k-space data hasbeen cropped such that only thecenter part of the data was usedas input. With this methodcorresponding low-resolutiondata as input and high-resolutiondata as output / ground truthwere created for training andvalidation. |
The results from each set of tests demonstrate that the devices perform as intended and are thus substantially equivalent to the predicate device to which it has been compared.
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9. Clinical Tests / Publications
No clinical tests were conducted to support substantial equivalence for the subject devices; however, as stated above, sample clinical images were provided.
| Feature | Publications | |
|---|---|---|
| Deep Resolve Boost EPI Diffusion | [1] Bae SH et al., Clinical feasibility of accelerated diffusionweighted imaging of the abdomen with deep learningreconstruction: Comparison with conventional diffusionweighted imaging, Eur J Radiol., 154 (2022) | |
| [2] Lee EJ et al., Feasibility of deep learning k-space-to-imagereconstruction for diffusion weighted imaging in patientswith breast cancers: Focus on image quality and reduced scantime, Eur J Radiol., 157 (2022) | ||
| [3] Afat S et al., Acquisition time reduction of diffusion-weighted liver imaging using deep learning imagereconstruction. Diagn Interv Imaging, (2022). | ||
| [4] Benkert T et al., Improved Clinical Diffusion WeightedImaging by Combining Deep Learning Reconstruction, PartialFourier, and Super Resolution, ISMRM (2022) | ||
| [5] Kim et al., Deep Learning-Accelerated Liver Diffusion-Weighted Imaging - Intraindividual Comparison andAdditional Phantom Study of Free-Breathing and Respiratory-Triggering Acquisitions, Invest Radiol (2023) | ||
| Deep Resolve Boost HASTE | [1] Herrmann J et al., Diagnostic Confidence andFeasibility of a Deep Learning Accelerated HASTE Sequence ofthe Abdomen in a Single Breath-Hold, InvestigativeRadiology, Volume 56, Number 5, May 2021. | |
| [2] Shanbhogue K et al. Accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deeplearning-based image reconstruction: qualitative andquantitative comparison of image quality with conventionalT2-weighted FS sequence. Eur Radiol. 2021 May 7. | ||
| [3] Herrmann J et al., Development and Evaluation ofDeep Learning-Accelerated Single-Breath-Hold AbdominalHASTE at 3 T Using Variable Refocusing Flip Angles. InvestRadiol. 2021 Apr 22. | ||
| [4] Han S et al., Evaluation of HASTE T2 weighted imagewith reduced echo time for detecting focal liver lesions inpatients at risk of developing hepatocellular carcinoma. Eur JRadiol. 2022 Nov 1;157:110588. | ||
| [5] Mule S et al., Fast T2-weighted liver MRI: Imagequality and solid focal lesions conspicuity using a deeplearning accelerated single breath-hold HASTE fat-suppressed sequence. Diagn Interv Imaging. 2022Oct;103(10):479-485. | ||
| [6] Ginocchio LA et al., Accelerated T2-weighted MRI ofthe liver at 3 T using a single-shot technique with deep | ||
| GRE_PC | [1] Guenthner C. et al. Ristretto MRE: A generalized multi-shot GRE-MRE sequence. NMR Biomed 2019; 32:e4049. | learning-based image reconstruction: impact on the image quality and lesion detection. Abdom Radiol (NY). 2022 Sep 28. [7] Herrmann J et al., Comprehensive clinical evaluation of a deep learning-accelerated, single-breath-hold abdominal HASTE at 1.5 T and 3 T. Acad Radiol. 2022 Apr 22:S1076-6332(22)00195-7. [8] Ichinohe F. et al., Usefulness of Breath-Hold Fat-Suppressed T2-Weighted Images With Deep Learning-Based Reconstruction of the Liver, Invest Radiol., 2022 |
| Complex Averaging | [1] Walsh DO, Gmitro AF, Marcellin MW. Adaptive reconstruction of phased array MR imagery. Magn Reson Med. 2000 May 1;43(5):682–90. [2] Kordbacheh H, Seethamraju RT, Weiland E, Kiefer B, Nickel MD, Chulroek T, et al. Image quality and diagnostic accuracy of complex-averaged high b value images in diffusion-weighted MRI of prostate cancer. Abdom Radiol (NY). 2019;44(6):2244–53 |
Furthermore, additional clinical publications were referenced to provide information on the use of the following features and functions:
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10. 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 cycle and continuously throughout the development of the product. Siemens Healthcare GmbH adheres to recognized and established industry standards, 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 Sola and MAGNETOM Altea with software syngo MR XA61A conforms to the following FDA recognized and international IEC, ISO and NEMA standards:
| Recognition Number | Product Area | Title of Standard | Reference Numberand date | StandardsDevelopmentOrganization |
|---|---|---|---|---|
| 19-4 | General | Medical electrical equipment - part 1:general requirements for basic safetyand essential performance | ES60601-1:2005/(R)2012 andA1:2012C1:2009/(R)2012 | AAMI / ANSI |
| 19-8 | General | Medical electrical equipment - Part 1-2:General requirements for basic safetyand essential performance - CollateralStandard: Electromagnetic disturbances- Requirements and tests | 60601-1-2 Edition4.0:2014-02 | IEC |
{11}------------------------------------------------
| 12-295 | Radiology | Medical electrical equipment - Part 2-33: Particular requirements for thebasic safety and essential performanceof magnetic resonance equipment formedical diagnosis | 60601-2-33 Ed. 3.2b:2015 | IEC |
|---|---|---|---|---|
| 5-125 | General | Medical devices - Application of riskmanagement to medical devices | 14971 Third Edition2019-12 | ISO |
| 5-114 | General I(QS/RM) | Medical devices - Part 1: Application ofusability engineering to medical devices | 62366-1:2015 | ANSI AAMI IEC |
| 13-79 | Software/Informatics | Medical device software - Software lifecycle processes | 62304 Edition 1.12015-06CONSOLIDATEDVERSION | IEC |
| 12-195 | Radiology | NEMA MS 6-2008 (R2014)Determination of Signal-to-Noise Ratioand Image Uniformity for Single-Channel Non-Volume Coils inDiagnostic MR Imaging | MS 6-2008 (R2014) | NEMA |
| 12-342 | Radiology | Digital Imaging and Communications inMedicine (DICOM) Set | PS 3.1 - 3.20(2021e) | NEMA |
| 2-258 | Biocompatibility | Biological evaluation of medical devices- part 1: evaluation and testing within arisk management process.(Biocompatibility) | 10993-1 Fifthedition 2018-08 | AAMIANSIISO |
11. Conclusion as to Substantial Equivalence
MAGNETOM Sola and MAGNETOM Altea with software syngo MR XA61A has the same intended use and same basic technological characteristics than the predicate device system, MAGNETOM Sola with syngo MR XA51A, 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 data suggest that the features bear an equivalent safety and performance profile to that of the predicate device and reference devices.
Siemens believes that MAGNETOM Sola and MAGNETOM Altea with software syngo MR XA61A are substantially equivalent to the currently marketed device MAGNETOM Sola with software syngo MR XA51A (K221733 on September 13, 2022).
§ 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.