(123 days)
Yes
The document explicitly mentions "AI Features/Applications training and validation" and describes the training and testing of "Deep Resolve Boost" and "Deep Resolve Sharp" using metrics like PSNR and SSIM, which are common in image processing and often associated with ML/AI techniques. The description of the training data creation also suggests data manipulation and augmentation, typical for training ML models.
No
The device is described as a "magnetic resonance diagnostic device" used to produce images and spectra that "may assist in diagnosis," indicating a diagnostic rather than therapeutic purpose.
Yes
The Intended Use / Indications for Use section explicitly states that the MAGNETOM system is indicated for use as a "magnetic resonance diagnostic device (MRDD)" and that the information derived from it "may assist in diagnosis."
No
The device description explicitly states that the subject devices consist of "new and modified software and hardware." It also lists specific hardware components that have been modified.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are medical devices used to examine specimens taken from the human body, such as blood, urine, or tissue, to provide information for diagnosis, monitoring, or screening.
- Device Function: The MAGNETOM system is a Magnetic Resonance Diagnostic Device (MRDD). It produces images and spectra of the internal structure and function of the body without taking specimens from the patient. It uses magnetic fields and radio waves to generate these images.
- Intended Use: The intended use clearly states it's a diagnostic device that produces images and spectra of the head, body, or extremities. While these images are used to assist in diagnosis, the process itself is not performed in vitro (outside the body).
- Device Description: The description focuses on hardware and software components related to image acquisition and processing, not on analyzing biological samples.
Therefore, the MAGNETOM system falls under the category of an in vivo diagnostic imaging device, not an in vitro diagnostic device.
No
The letter does not state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / 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, 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.
Product codes (comma separated list FDA assigned to the subject device)
LNH, LNI, MOS
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:
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.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Magnetic resonance diagnostic device (MRDD)
Anatomical Site
head, body, or extremities
Indicated Patient Age Range
Not Found
Intended User / Care Setting
trained physician, healthcare professionals familiar with and responsible for the acquisition and post processing of magnetic resonance images.
Description of the training set, sample size, data source, and annotation protocol
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 The data covered a broad range of body parts, contrasts, fat suppression techniques, orientations, and field strength.
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 process includes further under-sampling of the data by discarding k-space lines, lowering of the SNR level by addition Restricted of noise and mirroring of k-space data.
Deep Resolve Sharp:
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.
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.
Description of the test set, sample size, data source, and annotation protocol
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.
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.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Non-clinical tests were conducted:
- Software verification and validation
- Sample clinical images
- Image quality assessment by sample clinical images
- Physiologging Verification Report
- myExam 3D Camera Validation and Verification Report
- myExam Implant Suite Validation and Verification Report
The influence of the Deep Resolve Boost and Deep Resolve Sharp networks were characterized using PSNR and SSIM. Visual comparisons were also used to evaluate aliasing artifacts, image sharpness and denoising levels.
No clinical tests were conducted to support substantial equivalence for the subject devices; however, sample clinical images were provided.
Key results: 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.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Key metrics for Deep Resolve Boost and Deep Resolve Sharp: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Perceptual Loss (for Deep Resolve Sharp only).
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.
0
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: a symbol on the left and the text "FDA U.S. FOOD & DRUG ADMINISTRATION" on the right. The symbol on the left is a stylized representation of a human figure, while the text on the right is written in a bold, sans-serif font. The word "FDA" is in a blue box.
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).
1
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
2
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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3
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 Boulevard
Malvern, PA 19355, USA
Registration Number: 2240869 |
|----------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Date Prepared: | August 09, 2023 |
| Manufacturer: | Siemens Healthcare GmbH
Henkestr. 127
91052 Erlangen
Germany
Registration Number: 3002808157
Siemens Shenzhen Magnetic Resonance LTD
Siemens MRI Center
Hi-Tech Industrial Park (middle)
Gaoxin C. Ave., 2nd
Shenzhen 518057
P.R. CHINA
Registration 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
4
| 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: LNH |
| Secondary: 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: LNH |
| Secondary: 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:
5
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 and
Date | Product
Code | Manufacturer |
|----------------------------------------------|----------------------------------|------------------|-------------------------|
| MAGNETOM Sola with syngo
MR XA51A | K221733 on September 13,
2022 | LNH,
LNI, MOS | Siemens Healthcare GmbH |
| Reference Device | FDA Clearance Number and
Date | Product
Code | Manufacturer |
| MAGNETOM Altea with syngo
MR 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 |
|---|---|---|---|
| ---------- | ----------------- | ------------------ | ------------------- |
6
| | MAGNETOM Sola
MAGNETOM Altea
with software syngo MR
XA61A | MAGNETOM Sola with
syngo MR XA51A
(K221733) | MAGNETOM Altea with
syngo MR XA51A
(K221733) |
|------------------------------|-------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------|----------------------------------------------------|
| Magnet System | Yes | Yes | Yes |
| RF System | Yes | Yes | Yes |
| Transmission
technique | Yes | Yes | Yes |
| Gradient System | Yes | Yes | Yes |
| Patient Table | Yes | Yes | Yes |
| Multi-Nuclear | | | |
| Option - Supported
Nuclei | No | No | No |
| Computer | Yes
Modified compared to
predicate device:
- New MRAWP and
MRWP - New MaRS hardware
for MAGNETOM Sola | Yes | Yes |
| Coils | Yes | Yes | Yes |
| Other HW
components | Yes
Modified compared to
predicate device: - myExam 3D Camera
functionality extension | Yes | Yes |
Summary software comparison table for the subject and predicate devices
| Subject Devices | Predicate Device | |
|---|---|---|
| Software | MAGNETOM Sola | |
| MAGNETOM Altea | ||
| with software syngo MR XA61A | MAGNETOM Sola with syngo | |
| MR XA51A | ||
| (K221733) | ||
| Sequences | ||
| SE-based pulse sequence types | New feature |
- Deep Resolve Boost for
HASTE | Yes |
| GRE-based/Steady-State pulse
sequence types | New or modified pulse
sequences: - BEAT_NAV pulse sequence
re-naming - GRE_PC new pulse sequence | Yes |
| EPI-based pulse sequence types | New features - Physiologging for EP2D_BOLD
and EP2D_PACE - Deep Resolve Boost for
EP2D DIFF | Yes |
7
| | - Complex Averaging for
EP2D_DIFF | |
|-------------------------------------------------------------------------------------------------------------------------|--------------------------------------------|-----|
| Spectroscopy pulse sequence
types | Yes | Yes |
| Feature and Applications | | |
| Other features and
applications such as:
-Application Suites
-myExam Assists
-Other Imaging
Applications | New feature:
- myExam Implant Suite | 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 | 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 |
8
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 and | ||
| Validation 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 and | ||
| Test Results | ||
| Summary | 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 | ||
| reconstructions with and without Deep | ||
| Resolve Sharp. | ||
| Equipment | 1.5T and 3T MRI systems | |
| Clinical Subgroups | No clinical subgroups have been defined for the collected dataset. | |
| Demographic | ||
| Distribution | Due to reasons of data privacy, we did not record gender, age and ethnicity during data | |
| collection. | ||
| Reference Standard | 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 process includes further under- | ||
| sampling of the data by discarding k-space | ||
| lines, lowering of the SNR level by addition | ||
| Restricted 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. |
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
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 diffusion | |
| weighted imaging of the abdomen with deep learning | ||
| reconstruction: Comparison with conventional diffusion | ||
| weighted imaging, Eur J Radiol., 154 (2022) | ||
| [2] Lee EJ et al., Feasibility of deep learning k-space-to-image | ||
| reconstruction for diffusion weighted imaging in patients | ||
| with breast cancers: Focus on image quality and reduced scan | ||
| time, Eur J Radiol., 157 (2022) | ||
| [3] Afat S et al., Acquisition time reduction of diffusion- | ||
| weighted liver imaging using deep learning image | ||
| reconstruction. Diagn Interv Imaging, (2022). | ||
| [4] Benkert T et al., Improved Clinical Diffusion Weighted | ||
| Imaging by Combining Deep Learning Reconstruction, Partial | ||
| Fourier, and Super Resolution, ISMRM (2022) | ||
| [5] Kim et al., Deep Learning-Accelerated Liver Diffusion- | ||
| Weighted Imaging - Intraindividual Comparison and | ||
| Additional Phantom Study of Free-Breathing and Respiratory- | ||
| Triggering Acquisitions, Invest Radiol (2023) | ||
| Deep Resolve Boost HASTE | [1] Herrmann J et al., Diagnostic Confidence and | |
| Feasibility of a Deep Learning Accelerated HASTE Sequence of | ||
| the Abdomen in a Single Breath-Hold, Investigative | ||
| Radiology, Volume 56, Number 5, May 2021. | ||
| [2] Shanbhogue K et al. Accelerated single-shot T2- | ||
| weighted fat-suppressed (FS) MRI of the liver with deep | ||
| learning-based image reconstruction: qualitative and | ||
| quantitative comparison of image quality with conventional | ||
| T2-weighted FS sequence. Eur Radiol. 2021 May 7. | ||
| [3] Herrmann J et al., Development and Evaluation of | ||
| Deep Learning-Accelerated Single-Breath-Hold Abdominal | ||
| HASTE at 3 T Using Variable Refocusing Flip Angles. Invest | ||
| Radiol. 2021 Apr 22. | ||
| [4] Han S et al., Evaluation of HASTE T2 weighted image | ||
| with reduced echo time for detecting focal liver lesions in | ||
| patients at risk of developing hepatocellular carcinoma. Eur J | ||
| Radiol. 2022 Nov 1;157:110588. | ||
| [5] Mule S et al., Fast T2-weighted liver MRI: Image | ||
| quality and solid focal lesions conspicuity using a deep | ||
| learning accelerated single breath-hold HASTE fat- | ||
| suppressed sequence. Diagn Interv Imaging. 2022 | ||
| Oct;103(10):479-485. | ||
| [6] Ginocchio LA et al., Accelerated T2-weighted MRI of | ||
| the 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:
10
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:
| Recognitio
n Number | Product Area | Title of Standard | Reference Number
and date | Standards
Development
Organization |
|------------------------|--------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------|------------------------------------------|
| 19-4 | General | Medical electrical equipment - part 1:
general requirements for basic safety
and essential performance | ES60601-
1:2005/(R)2012 and
A1:2012
C1:2009/(R)2012 | AAMI / ANSI |
| 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 Edition
4.0:2014-02 | IEC |
11
| 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
b:2015 | IEC |
|--------|--------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------|---------------------|
| 5-125 | General | 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 | 62366-1:2015 | ANSI AAMI IEC |
| 13-79 | Software/
Informatics | Medical device software - Software life
cycle processes | 62304 Edition 1.1
2015-06
CONSOLIDATED
VERSION | IEC |
| 12-195 | Radiology | NEMA MS 6-2008 (R2014)
Determination of Signal-to-Noise Ratio
and Image Uniformity for Single-
Channel Non-Volume Coils in
Diagnostic MR Imaging | MS 6-2008 (R2014) | NEMA |
| 12-342 | Radiology | Digital Imaging and Communications in
Medicine (DICOM) Set | PS 3.1 - 3.20
(2021e) | NEMA |
| 2-258 | Biocompati
bility | Biological evaluation of medical devices
- part 1: evaluation and testing within a
risk management process.
(Biocompatibility) | 10993-1 Fifth
edition 2018-08 | AAMI
ANSI
ISO |
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).