(146 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 Aera (including MAGNETOM Aera Mobile), MAGNETOM Skyra, MAGNETOM Prisma, MAGNETOM Prisma™, MAGNETOM Vida, MAGNETOM Lumina with software syngo MR XA60A, consist of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Vida with syngo MR XA50A (K213693).
This FDA 510(k) summary describes several updates to existing Siemens Medical Solutions MRI systems (MAGNETOM Vida, Lumina, Aera, Skyra, Prisma, and Prisma fit), primarily focusing on software updates (syngo MR XA60A) and some modified/new hardware components. The document highlights the evaluation of new AI features, specifically "Deep Resolve Boost" and "Deep Resolve Sharp."
Here's an analysis of the acceptance criteria and the study details for the AI features:
1. Table of Acceptance Criteria and Reported Device Performance
The document provides a general overview of the evaluation metrics used but does not explicitly state acceptance criteria in a quantitative format (e.g., "Deep Resolve Boost must achieve a PSNR of X" or "Deep Resolve Sharp must achieve Y SSIM"). Instead, it describes the types of metrics used and qualitative assessments.
| AI Feature | Acceptance Criteria (Implicit from Evaluation) | Reported Device Performance (Summary) |
|---|---|---|
| Deep Resolve Boost | - Preservation of image quality (aliasing artifacts, image sharpness, denoising levels) compared to original.- Impact characterized by PSNR and SSIM. | 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. |
| Deep Resolve Sharp | - Preservation of image quality (image sharpness) compared to original.- Impact characterized by PSNR, SSIM, and perceptual loss.- Verification and validation by visual rating and evaluation of image sharpness by intensity profile comparisons. | 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. |
2. Sample Size Used for the Test Set and Data Provenance
- Deep Resolve Boost: The document doesn't explicitly state a separate "test set" size. It mentions the "Training and Validation data" which includes:
- TSE: more than 25,000 slices
- HASTE: pre-trained on the TSE dataset and refined with more than 10,000 HASTE slices
- EPI Diffusion: more than 1,000,000 slices
- Data Provenance: The data covered a broad range of body parts, contrasts, fat suppression techniques, orientations, and field strength. No specific country of origin is mentioned, but the manufacturer (Siemens Healthcare GmbH) is based in Germany, and Siemens Medical Solutions USA, Inc. is the submitter. The data was "retrospectively created from the ground truth by data manipulation and augmentation."
- Deep Resolve Sharp: The document doesn't explicitly state a separate "test set" size. It mentions "Training and Validation data" from "on more than 10,000 high resolution 2D images."
- Data Provenance: Similar to Deep Resolve Boost, the data covered a broad range of body parts, contrasts, fat suppression techniques, orientations, and field strength. Data was "retrospectively created from the ground truth by data manipulation." No specific country of origin is mentioned.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
Not specified. The document states that the acquired datasets "represent the ground truth." There is no mention of expert involvement in establishing ground truth for the test sets. The focus is on technical metrics (PSNR, SSIM) and "visual comparisons" or "visual rating" which implies expert review, but the number and qualifications are not provided.
4. Adjudication Method for the Test Set
Not explicitly stated. The document mentions "visual comparisons" for Deep Resolve Boost and "visual rating" for Deep Resolve Sharp. This suggests subjective human review, but no specific adjudication method (like 2+1 or 3+1 consensus) is 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 is described for the AI features. The studies mentioned (sections 8 and 9) focus on evaluating the technical performance and image quality of the AI algorithms themselves, not on their impact on human reader performance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
Yes, standalone performance evaluation of the algorithms was conducted. The "Test Statistics and Test Results Summary" for both Deep Resolve Boost and Deep Resolve Sharp detail the evaluation of the network's impact using quantitative metrics (PSNR, SSIM, perceptual loss) and qualitative assessments ("visual comparisons," "visual rating," "intensity profile comparisons"). This represents the algorithm's performance independent of a human reader's diagnostic accuracy.
7. The Type of Ground Truth Used
The ground truth used for both Deep Resolve Boost and Deep Resolve Sharp was the acquired datasets themselves, representing the original high-quality or reference images/slices.
- For 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 original acquired data serves as the target "ground truth" for the AI to reconstruct/denoise.
- For Deep Resolve Sharp, input data was "retrospectively created from the ground truth by data manipulation," specifically by cropping k-space data to create low-resolution input, with the original high-resolution data serving as the "output / ground truth" for training and validation.
8. The Sample Size for the Training Set
- Deep Resolve Boost:
- TSE: more than 25,000 slices
- HASTE: pre-trained on the TSE dataset and refined with more than 10,000 HASTE slices
- 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 as the acquired, unaltered (or minimally altered, e.g., removal of k-space lines to simulate lower quality input from high quality ground truth) raw imaging data.
- For Deep Resolve Boost: "The acquired datasets (as described above) represent the ground truth for the training and validation. Input data was retrospectively created from the ground truth by data manipulation and augmentation." This implies that the original, high-quality scans were considered the ground truth, and the AI was trained to restore manipulated, lower-quality versions to this original quality.
- For Deep Resolve Sharp: "The acquired datasets represent the ground truth for the training and validation. Input data was retrospectively created from the ground truth by data manipulation. 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." Similar to Boost, the original, higher-resolution scans served as the ground truth.
{0}------------------------------------------------
Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services seal on the left and the FDA acronym with the full name of the agency on the right. The FDA part of the logo is in blue, with the acronym in a square and the full name, "U.S. Food & Drug Administration," written out to the right of the square.
October 23, 2023
Siemens Medical Solutions USA, Inc. Alina Goodman Regulatory Affairs Professional 40 Liberty Boulevard Malvern, Pennsylvania 19355
Re: K231560
Trade/Device Name: MAGNETOM Vida; MAGNETOM Lumina; MAGNETOM Aera; MAGNETOM Skyra; MAGNETOM Prisma; MAGNETOM Prisma fit Regulation Number: 21 CFR 892.1000 Regulation Name: Magnetic Resonance Diagnostic Device Regulatory Class: Class II Product Code: LNH, LNI, MOS Dated: September 22, 2023 Received: September 22, 2023
Dear Alina Goodman:
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.
{1}------------------------------------------------
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).
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.
{2}------------------------------------------------
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,
D.G.K.
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
Enclosure
{3}------------------------------------------------
Indications for Use
Submission Number (if known)
Device Name MAGNETOM Vida: MAGNETOM Lumina; MAGNETOM Aera; MAGNETOM Skyra; MAGNETOM Prisma; MAGNETOM Prisma fit
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 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.
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)
CONTINUE ON A SEPARATE PAGE IF NEEDED.
This section applies only to requirements of the Paperwork Reduction Act of 1995.
DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.
The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:
Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov
"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."
{4}------------------------------------------------
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: | May 25, 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
Alina Goodman Regulatory Affairs Professional Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19355, USA Phone: +1(317)371-8593 E-mail: alina.goodman@siemens-healthineers.com
3. Device Name and Classification
| Device/ Trade name: | MAGNETOM Aera, MAGNETOM Skyra, MAGNETOM Prisma, |
|---|---|
| MAGNETOM Prismafit, MAGNETOM Vida, MAGNETOM Lumina | |
| Classification Name: | Magnetic Resonance Diagnostic Device (MRDD) |
| Classification Panel: | Radiology |
| CFR Code: | 21 CFR § 892.1000 |
| Classification: | II |
| Product Code: | Primary: LNH |
{5}------------------------------------------------
Secondary: LNI, MOS
4. Legally Marketed Predicate and Reference Device
| 4.1. Predicate Device | |
|---|---|
| Trade name: | MAGNETOM Vida |
| 510(k) Number: | K213693 |
| 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 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 |
| Trade name: | MAGNETOM Lumina |
| 510(k) Number: | K220939 |
| 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
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.
{6}------------------------------------------------
5. Device Description
The subject devices, MAGNETOM Aera (including MAGNETOM Aera Mobile), MAGNETOM Skyra, MAGNETOM Prisma, MAGNETOM Prisma™, MAGNETOM Vida, MAGNETOM Lumina with software syngo MR XA60A, consist of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Vida with syngo MR XA50A (K213693).
A high-level summary of the new and modified hardware and software is provided below:
For MAGNETOM Vida with syngo MR XA60:
Hardware
Modified Hardware:
-
Host computers ((syngo MR Acquisition Workplace (MRAWP) and syngo MR Workplace (MRWP)),
-
MaRS (Measurement and Reconstruction System).
Software
New Features and Applications:
- -> GRE PC
- → Physio logging
- → Deep Resolve Boost HASTE
-
Deep Resolve Boost EPI Diffusion
- → Open Recon
- → MR Fingerprinting (MRF)1
Modified Features and Applications:
- → BEAT_nav (re-naming only).
-
myExam Angio Advanced Assist (Test Bolus).
- -> Beat Sensor (all sequences).
For MAGNETOM Lumina with syngo MR XA60:
Hardware
Modified Hardware:
→ Host computers ((syngo MR Acquisition Workplace (MRAWP) and syngo MR Workplace (MRWP)),
Software
New Features and Applications:
- -> GRE PC
- -> Deep Resolve Boost HASTE
-
Deep Resolve Boost EPI Diffusion
- → Open Recon
Modified Features and Applications:
- → BEAT nav (re-naming only)
- → myExam Angio Advanced Assist (Test Bolus)
- → Beat Sensor (all sequences)
For MAGNETOM Skyra with syngo MR XA60:
Hardware
Modified Hardware:
-
Host computers ((syngo MR Acquisition Workplace (MRAWP) and syngo MR Workplace (MRWP))
-
MaRS (measurement and reconstruction system)
Software
New Features and Applications:
1 MR Fingerprinting (MRF) is cleared under K213805
{7}------------------------------------------------
- → GRE_PC
- → Physio logging
- → Open Recon
-
MR Fingerprinting (MRF)1
Modified Features and Applications:
-
BEAT_nav (re-naming only)
For MAGNETOM Prisma and MAGNETOM Prisma™ with syngo MR XA60:
Hardware
Modified Hardware:
-
Host computers ((syngo MR Acquisition Workplace (MRAWP) and syngo MR Workplace (MRWP))
- → MaRS (measurement and reconstruction system)
Software
New Features and Applications:
- -> GRE PC
- → Physio logging
- → Open Recon
-
MR Fingerprinting (MRF)1
Modified Features and Applications:
→ BEAT_nav (re-naming only)
For MAGNETOM Aera with syngo MR XA60:
Hardware
New Hardware:
-> Flex Loop Large coil
Modified Hardware:
→ Host computers ((syngo MR Acquisition Workplace (MRAWP) and syngo MR Workplace (MRWP))
-
MaRS (measurement and reconstruction system)
Software
New Features and Applications:
- -> GRE PC
- → Physio logging
- → Open Recon
Modified Features and Applications:
- → BEAT_nav (re-naming only)
6. Substantial Equivalence
MAGNETOM Aera, MAGNETOM Skyra, MAGNETOM Prisma, MAGNETOM Prisma™, MAGNETOM Vida, MAGNETOM Lumina with software syngo MR XA60A is substantially equivalent to the following predicate device:
| Predicate Device | FDA Clearance Number and Date | Product Code | Manufacturer |
|---|---|---|---|
| MAGNETOM Vida with syngo MR XA50A | K213693, cleared on February 25, 2022 | LNHLNI, MOS | Siemens Healthcare GmbH |
| Reference Device | FDA Clearance Number and Date | Product Code | Manufacturer |
| MAGNETOM Lumina with syngo MR XA50A | K220939, cleared April 29, 2022 | LNHLNI, MOS | Siemens Healthcare GmbH |
| MAGNETOM Sola with syngo MR XA51A | K221733, cleared September 14, 2022 | LNHLNI, MOS | Siemens Healthcare GmbH |
{8}------------------------------------------------
7. Technological Characteristics
The subject devices, MAGNETOM Aera, MAGNETOM Skyra, MAGNETOM Prisma, MAGNETOM Prisma®, MAGNETOM Vida, MAGNETOM Lumina 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.
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:
| Hardware | Subject Devices | Predicate Device | Reference Devices* |
|---|---|---|---|
| MAGNETOM Aera,MAGNETOM Skyra,MAGNETOM Prisma,MAGNETOM Prismafit,MAGNETOM Vida andMAGNETOM Luminawith software syngo MRXA60A | MAGNETOM Vida withsyngo MR XA50A(K213693) | MAGNETOM Sola withsyngo MR XA51A(K221733)MAGNETOM Luminawith syngo MR XA50A(K220939) | |
| 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-NuclearOption - SupportedNuclei | Yes | Yes | Yes |
| Computer | YesModified | Yes | Yes |
| Coils | YesNew Flex Loop Large coil forMAGNETOM Aera | Yes | Yes |
| Other HWcomponents | YesModified (Beat Sensor - forMAGNETOM Vida andMAGNETOM Lumina, Imagecalculation system - except forMAGNETOM Lumina) | Yes | Yes |
Summary hardware comparison table for the subject and predicate/reference device
Summary software comparison table for the subject and predicate devices
| Subject Device | Predicate Device | |
|---|---|---|
| Software | MAGNETOM Vida with softwaresyngo MR XA50A | MAGNETOM Vida with syngoMR XA31A(K203443) |
| Sequences | ||
| SE-based pulse sequence types | New feature as listed in the DeviceDescription above | Yes |
{9}------------------------------------------------
| GRE-based/Steady-State pulsesequence types | New or modified pulse sequencesas listed in the Device Descriptionabove | Yes |
|---|---|---|
| EPI-based pulse sequence types | New features as listed in the DeviceDescription above | Yes |
| Spectroscopy pulse sequence types | Yes | Yes |
| Feature and Applications | ||
| Other features andapplications such as:-Application Suites-myExam Assists-Other ImagingApplications | New and Modified features andapplications as listed in the DeviceDescription above | Yes |
| User interface and user interaction | 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 | New featureas listed in the Device Descriptionabove | 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.
*The reference devices within this submission are used to demonstration previously cleared features which have been migrated to this submission with or without modification, a summary of these features are below;
Flex Loop Large coil previsouly cleared under MAGNETOM Sola with syngo MR XA51A (K221733)
Modified beat sensor, within hardware components, previsouly cleared under MAGNETOM Sola with syngo MR XA51A (K221733)
Open Recon feature previously cleared under MAGNETOM Sola with syngo MR XA51A (K221733)
myExam Angio Assist features previously cleared under MAGNETOM Sola with syngo MR XA51A (K221733) as MyExam Angio Advance Assist (Test Bolus) and MAGNETOM Lumina with syngo MR XA50A (K220939) as myExam Angio Assist (Care Bolus)..
{10}------------------------------------------------
8. Nonclinical Tests
| Performance Test | Tested Hardware orSoftware | Source/Rationale for test |
|---|---|---|
| Software verificationand validation | New or modified softwarefeatures | Guidance for the Content of PremarketSubmissions for Software Contained inMedical Devices |
| Sample clinical images | New or modified softwarefeatures | Guidance for submission of PremarketNotifications for Magnetic ResonanceDiagnostic Devices |
| Image qualityassessment by sampleclinical images | - new / modified pulsesequence types.- comparison imagesbetween the new / modifiedfeatures and the predicatedevice features | |
| Physio loggingVerification Report | Physio logging | New Feature Introduction |
The following performance testing was conducted on the subject devices:
The following performance testing for local coils was conducted on the predicate and the reference devices and can be reused for the subject devices:
| Performance Test | Tested Hardware or Software | Source/Rationale for test |
|---|---|---|
| Performance bench test | - SNR and image uniformitymeasurements for coils- Heating measurements for coils | Guidance for Submission ofPremarket Notifications forMagnetic Resonance DiagnosticDevices |
AI Features/Applications training and validation:
The information below shows an executive summary of training and validation dataset of the Al features:
| Deep Resolve Boost: | Deep Resolve Sharp: | |
|---|---|---|
| Training andValidation data | TSE: more than 25,000 slices HASTE: pre-trained on the TSE dataset and refined with more than 10,000 HASTE slices EPI Diffusion: more than 1,000,000 slices The data covered a broad range of body parts, contrasts, fat suppression techniques, orientations, and field strength. | on more than 10,000 high resolution 2D images.The data covered a broad range of body parts, contrasts, fat suppression techniques, orientations, and field strength. |
| Test Statistics andTest ResultsSummary | 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. | 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 |
{11}------------------------------------------------
| reconstructions 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.
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.
Furthermore, additional clinical publications were referenced to provide information on the use of the following features and functions:
| 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) |
{12}------------------------------------------------
| 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 deeplearning-based image reconstruction: impact on the imagequality and lesion detection. Abdom Radiol (NY). 2022 Sep 28. | |
| [7] Herrmann J et al., Comprehensive clinical evaluationof a deep learning-accelerated, single-breath-hold abdominalHASTE 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-BasedReconstruction of the Liver, Invest Radiol., 2022 | |
| GRE_PC | [1] Guenthner C. et al. Ristretto MRE: A generalized multi-shot GRE-MRE sequence. NMR Biomed 2019; 32:e4049. |
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 Aera, MAGNETOM Skyra, MAGNETOM Prisma, MAGNETOM Prisma®*, MAGNETOM Vida, MAGNETOM Lumina with software syngo MR XA60A conforms to the following FDA recognized and international IEC, ISO and NEMA standards:
{13}------------------------------------------------
| Recognition Number | ProductArea | 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 |
| 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 Aera, MAGNETOM Skyra, MAGNETOM Prisma, MAGNETOM Prisma®, MAGNETOM Vida, MAGNETOM Lumina with software syngo MR XA60A has the same intended use and same basic technological characteristics than the predicate device system, MAGNETOM Vida with syngo MR XA50A, 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
{14}------------------------------------------------
tested and the conclusions from all verification and validation 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 Aera, MAGNETOM Skyra, MAGNETOM Prisma, MAGNETOM Prisma™, MAGNETOM Vida, MAGNETOM Lumina with software syngo MR XA60A is substantially equivalent to the currently marketed device MAGNETOM Vida with software syngo MR XA50A (K213693, cleared on February 25, 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.