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.

Device Description

MAGNETOM Vida with software syngo MR XA50A includes new and modified software compared to the predicate device, MAGNETOM Vida with software syngo MR XA31A. A high-level summary of the new and modified hardware and software is provided below:

Software
New Features and Applications

Deep Resolve Swift Brain is a protocol for fast routine brain imaging primarily based on echo planar imaging (EPI) pulse sequences. Its main enablers are multi-shot (ms) EPI pulse sequence types and a deep learning-based image reconstruction.
Deep Resolve Boost is a novel deep learning-based image reconstruction algorithm for 2D TSE data, which reconstructs images from k-space raw-data.
BLADE diffusion is a multi-shot imaging method based on TSE or TGSE (when EPI factor > 1) readout and a BLADE trajectory with diffusion preparation to enable diffusion weighted imaging with reduced sensitivity to B0 inhomogeneity and reduced T2 decay caused image blurring.
HASTE diffusion (HASTE DIFF) is a single-shot imaging method based on TSE readout with diffusion preparation to enable diffusion weighted imaging with reduced sensitivity to B0 inhomogeneity.

Modified Features and Applications

Fast GRE RefScan: A speed-optimized reference scan for GRAPPA and SMS kernel calibration for echo planar imaging pulse sequence types.
Static Field Correction is a reconstruction option reducing susceptibilityinduced distortions and intensity variations.
Deep Resolve Gain is a reconstruction option which improves the SNR of the scanned images. The functionality has been extended to pulse sequence types SE and TSE DIXON.
Deep Resolve Sharp is an interpolation algorithm which increases the perceived sharpness of the interpolated images. The functionality has been extended to pulse sequence types SE and TSE DIXON.
The myExam Angio Advanced Assist provides an assisted and quided workflow for peripheral angiography examination using care bolus. The main advantage of this new workflow is a simplified and improved planning procedure of multi-station peripherical angiography measurements.

Other Modifications and / or Minor Changes

TSE MoCo is an image-based motion correction in the average-dimension for the TSE pulse sequence type.
MR Breast Biopsy is improved with an automatic fiducial detection.

AI/ML Overview

This document describes regulatory approval for an MRI system and its software updates, rather than a device with specific performance criteria evaluated against a ground truth in the context of AI or advanced image analysis. Therefore, much of the requested information cannot be extracted from the provided text.

Here's why and what can be extracted:

Why most of the requested information cannot be provided:

No specific "device" for performance claims: The submission is for a Magnetic Resonance Diagnostic Device (MRDD) system (MAGNETOM Vida) and its software updates (syngo MR XA50A). It's essentially an upgrade to an existing MRI machine, not a new AI/CADx device making specific diagnostic claims that would require detailed performance metrics like sensitivity, specificity, AUC, etc.
Focus on Substantial Equivalence: The primary objective of this 510(k) summary is to demonstrate that the upgraded MRI system is "substantially equivalent" to a legally marketed predicate device (MAGNETOM Vida with syngo MR XA31A). This demonstration typically involves showing that the new features do not raise new questions of safety or effectiveness and perform as intended, rather than proving a superior or specific diagnostic accuracy against a clinical ground truth.
"Acceptance Criteria" are not clinical performance metrics: The "acceptance criteria" discussed in the document are related to the successful completion of performance tests (e.g., image quality assessments, software verification and validation) to ensure the device performs as intended and conforms to relevant standards, not clinical performance acceptance thresholds for a diagnostic task.
No detailed clinical study for performance claims: The document explicitly states: "No additional clinical tests were conducted to support substantial equivalence for the subject devices; however, as stated above, sample clinical images were provided." This confirms that a dedicated clinical study to prove diagnostic performance metrics (e.g., of an AI algorithm) was not performed or presented here. The "clinical publications" referenced are primarily previous research papers related to the underlying technologies (e.g., deep learning for reconstruction, new pulse sequences), not a study directly validating the clinical performance of this specific device's new features against a ground truth.

Information that can be extracted:

Here's what can be provided based on the text:

1. A table of (apparent) acceptance criteria and the reported device performance

Based on the nature of this 510(k) for an MRI system upgrade, the "acceptance criteria" are related to the successful completion of engineering and non-clinical performance evaluations, demonstrating the device performs as intended and is safe and effective when compared to the predicate. No specific numerical performance metrics (e.g., sensitivity, specificity for a diagnostic task) are provided as acceptance criteria or reported performance for a "device" in the context of AI.

Acceptance Criteria (Implied from Nonclinical Tests)	Reported Device Performance
Successfully generate sample clinical images	Performed as intended
Meet image quality assessment requirements	Performed as intended
Software verification and validation successful	Performed as intended
Conformity to relevant standards (e.g., IEC 62304, ISO 14971, IEC 60601-1, NEMA standards)	Met standards
No new questions of safety or effectiveness raised compared to predicate	Substantially equivalent to predicate

2. Sample sized used for the test set and the data provenance

Sample Size: Not specified for any clinical performance evaluation, as no dedicated clinical study was performed. The document mentions "sample clinical images" were provided, but the quantity or characteristics of these images are not detailed for a statistical test set.
Data Provenance: Not specified, as no formal clinical test set with defined provenance was used to demonstrate performance against acceptance criteria in the manner requested.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. No formal expert-adjudicated ground truth for a test set was established for a clinical performance study of this upgraded MRI system. The interpretation of images is generally described as being by a "trained physician" as part of the Indications for Use.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable. No formal adjudication methods were used for a clinical performance test set.

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. A multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not conducted or reported in this 510(k) summary. The new features like "Deep Resolve Swift Brain" and "Deep Resolve Boost" are described as deep learning-based reconstruction algorithms or methods for faster imaging, not diagnostic AI assistants for human readers.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done

No. This submission is for an MRI system and its software, which includes deep learning for image reconstruction. It is not a standalone diagnostic algorithm that operates without human-in-the-loop for interpretation and performance evaluation. The "nonclinical tests" included "Image quality assessments by sample clinical images" and "Software verification and validation," which are typical for imaging device changes.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

Not applicable for performance claims of an AI algorithm against a diagnostic ground truth. The "ground truth" in the context of MRI system performance typically refers to physical phantom measurements, simulated data, or established image quality metrics, not clinical diagnostic outcomes adjudicated by experts or pathology for AI algorithm evaluation.

8. The sample size for the training set

Not specified. While the document mentions "deep learning-based image reconstruction," it does not provide details about the training set size for these deep learning components. The referenced clinical publications (e.g., [2], [3], [4], [5], [6], [8], [9]) discuss deep learning for reconstruction and accelerated MRI but are not specific to the training data used for this particular device's integrated deep learning features.

9. How the ground truth for the training set was established

Not specified. For deep learning-based image reconstruction, the "ground truth" typically involves high-quality, fully sampled MRI data used to train models to reconstruct images from undersampled or noisy data. However, the exact methodology for establishing this ground truth for the incorporated deep learning models is not detailed in this regulatory summary.

Summary

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February 25, 2022

Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health and Human Services logo on the left, and the FDA logo on the right. The FDA logo is a blue square with the letters "FDA" in white, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue.

Siemens Medical Solutions USA, Inc. % Karthik Pillai, Ph.D. Senior Regulatory Affairs Professional 40 Liberty Boulevard Mail Code 65-1A MALVERN PA 19355

Re: K213693

Trade/Device Name: MAGNETOM Vida with syngo MR XA50A Regulation Number: 21 CFR 892.1000 Regulation Name: Magnetic resonance diagnostic device Regulatory Class: Class II Product Code: LNH, LNI and MOS Dated: February 1, 2022 Received: February 2, 2022

Dear Karthik Pillai:

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 (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 located 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.

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 803) for

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devices or postmarketing safety reporting (21 CFR 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 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 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.

For

Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

Submission Number (if known)

K213693

Device Name

MAGNETOM Vida with syngo MR XA50A

Indications for Use (Describe)

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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K213693 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 ട്ട 807.92.

1. General Information

Establishment:	Siemens Medical Solutions USA, Inc.
	40 Liberty Boulevard
	Mail Code 65-1A
	Malvern, PA 19355, USA
	Registration Number: 2240869

Date Prepared: November 22, 2021
Siemens Healthcare GmbH Manufacturer: 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

Karthik Pillai, PhD Senior Regulatory Affairs Professional Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Mail Code 65-1A Malvern, PA 19355, USA +1(317)371-8593 Phone: Fax: (610) 448-1787 E-mail: karthik.pillai@siemens-healthineers.com

3. Device Name and Classification

Device/ Trade name:	MAGNETOM Vida with syngo MR XA50A
Classification Name:	Magnetic Resonance Diagnostic Device (MRDD)
Classification Panel:	Radiology
CFR Code:	21 CFR § 892.1000
Classification:	II

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Image /page/4/Picture/0 description: The image shows the Siemens Healthineers logo. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. To the right of the words is a graphic of orange dots arranged in a circular pattern.

Product Code:	Primary: LNH
	Secondary: LNI. MOS

4. Legally Marketed Predicate Device

Trade name:	MAGNETOM Vida
510(k) Number:	K203443
Classification Name:	Magnetic Resonance Diagnostic Device (MRDD)
Classification Panel:	Radiology
CFR Code:	21 CFR § 892.1000
Classification:	II
Product Code:	Primary: LNHSecondary: LNI, MOS

5. Intended Use

The indications for use for the subject devices are the same as the predicate device:

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. Device Description

Software

New Features and Applications

Deep Resolve Swift Brain is a protocol for fast routine brain imaging primarily based on echo planar imaging (EPI) pulse sequences. Its main enablers are multi-shot (ms) EPI pulse sequence types and a deep learning-based image reconstruction.
Deep Resolve Boost is a novel deep learning-based image reconstruction algorithm for 2D TSE data, which reconstructs images from k-space raw-data.
BLADE diffusion is a multi-shot imaging method based on TSE or TGSE (when EPI factor > 1) readout and a BLADE trajectory with diffusion preparation to enable diffusion weighted imaging with reduced sensitivity to B0 inhomogeneity and reduced T2 decay caused image blurring.

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Image /page/5/Picture/0 description: The image shows the logo for Siemens Healthineers. The word "SIEMENS" is written in teal, and the word "Healthineers" is written in orange below it. To the right of the words is a graphic of orange dots.

HASTE diffusion (HASTE DIFF) is a single-shot imaging method based on TSE readout with diffusion preparation to enable diffusion weighted imaging with reduced sensitivity to B0 inhomogeneity.

Modified Features and Applications

Fast GRE RefScan: A speed-optimized reference scan for GRAPPA and SMS kernel calibration for echo planar imaging pulse sequence types.
Static Field Correction is a reconstruction option reducing susceptibilityinduced distortions and intensity variations.
Deep Resolve Gain is a reconstruction option which improves the SNR of the scanned images. The functionality has been extended to pulse sequence types SE and TSE DIXON.
Deep Resolve Sharp is an interpolation algorithm which increases the perceived sharpness of the interpolated images. The functionality has been extended to pulse sequence types SE and TSE DIXON.
The myExam Angio Advanced Assist provides an assisted and quided workflow for peripheral angiography examination using care bolus. The main advantage of this new workflow is a simplified and improved planning procedure of multi-station peripherical angiography measurements.

Other Modifications and / or Minor Changes

TSE MoCo is an image-based motion correction in the average-dimension for the TSE pulse sequence type.
MR Breast Biopsy is improved with an automatic fiducial detection.

7. Substantial Equivalence

MAGNETOM Vida with software syngo MR XA50A is substantially equivalent to the following predicate device:

Predicate Device	FDA Clearance Numberand Date	ProductCode	Manufacturer
MAGNETOM Vida withsyngo MR XA31A	K203443, cleared March31, 2021	LNHLNI, MOS	Siemens HealthcareGmbH

8. Comparison of technological characteristics with the predicate device

The subject device, MAGNETOM Vida with software syngo MR XA50A, is 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 modified software. These differences have been tested and the conclusions from the non-clinical data suggests that the features bear an equivalent safety and performance profile to that of the predicate device.

9. Nonclinical Tests

The following performance testing was conducted on the subject devices.

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Image /page/6/Picture/0 description: The image contains the logo for Siemens Healthineers. The word "SIEMENS" is written in teal, and the word "Healthineers" is written in orange below it. To the right of the words is a graphic of orange dots arranged in a circular pattern.

Performance Test	Tested Hardware or Software	Source/Rationale for test
Sample clinical images	new and modified software features	Guidance for Submission ofPremarket Notifications for
Image quality assessments bysample clinical images. Insome cases a comparison ofthe image quality / quantitativedata was made.	- new / modified pulse sequencetypes and algorithms.- comparison images betweenthe new / modified featuresand the predicate devicefeatures	Magnetic ResonanceDiagnostic Devices
Software verification andvalidation	mainly new and modifiedsoftware features	Guidance for the Content ofPremarket Submissions forSoftware Contained in MedicalDevices

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.

10.Clinical Tests / Publications

No additional clinical tests were conducted to support substantial equivalence for the subject devices; however, as stated above, sample clinical images were provided. Clinical publications were referenced to provide information on the use of the following features and functions.

Feature / Function	Clinical Publication
Deep Resolve SwiftBrain	[1] Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE:Sensitivity encoding for fast MRI. Magn Reson Med. 1999;42:952-962.
	[2] Hyun CM, Kim HP, Lee SM, Lee S, Seo JK. Deep learning forundersampled MRI reconstruction. Phys Med Biol. 2018;63(13):135007.doi:10.1088/1361-6560/aac71a
	[3] Wang S, Su Z, Ying L, et al. Accelerating magnetic resonance imagingvia deep learning. In: 2016 IEEE 13th International Symposium onBiomedical Imaging (ISBI). Prague, Czech Republic: IEEE; 2016:514-517.doi:10.1109/ISBI.2016.7493320
	[4] Yu S, Park B, Jeong J. Deep iterative down-up CNN for imagedenoising. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit.; 2019:9.
	[5] Hammernik K, Schlemper J, Qin C, Duan J, Summers RM, Rueckert D.Σ-net: Systematic evaluation of iterative deep neural networks for fastparallel MR image reconstruction. ArXiv191209278 Cs Eess. December2019. http://arxiv.org/abs/1912.09278. Accessed January 9, 2020.
	[6] Hammernik K, Schlemper J, Qin C, Duan J, Summers RM, Rueckert D.Systematic evaluation of iterative deep neural networks for fast parallelMRI reconstruction with sensitivity-weighted coil combination. Magn.Reson. Med. 2021;86(4):1859-1872. doi:10.1002/mrm.28827
[7] Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image qualityassessment: From error visibility to structural similarity. IEEE Trans ImageProcess. 2004;13(4):600-612. doi:10.1109/TIP.2003.819861
[8] Schlemper J, Caballero J, Hajnal JV, Price AN, Rueckert D. A deepcascade of convolutional neural networks for dynamic MR imagereconstruction," IEEE Trans. Med. Imag. 2018;37:491-503
[9] Zbontar J, Knoll F, Sriram A, et al. fastMRI: An open dataset andbenchmarks for accelerated MRI. arXiv:181108839 [physics, stat].December 2019. http://arxiv.org/abs/1811.08839. Accessed March 5,2020.
[10] Demir et al., Optimization of Magnetization Transfer Contrast for EPIFLAIR Brain Imaging, Proceedings of the ISMRM 2021, abstract 4179
[11] Clifford et al., Clinical evaluation of an Al-accelerated two-minutemulti-shot EPI protocol for comprehensive high-quality brain imaging,Proceedings of the ISMRM 2021, abstract 300
[12] Filho et al., Clinical Evaluation of An Al-Accelerated Two-MinuteMulti-Shot EPI Protocol For Comprehensive High-Quality Brain MRI In AnEmergency And Inpatient Setting, Proceedings of the RSNA 2021,accepted, abstract 16890
[13] Pistocchi et al., Al-enhanced multi-shot multi-contrast EPI protocol:Preliminary clinical experience, Proceedings of the ECR 2022, submitted
[14] Gassenmaier S et al., Deep learning-accelerated T2-weightedimaging of the prostate: Reduction of acquisition time and improvementof image quality, European Journal of Radiology, 137 (2021)
[15] Herrmann J et al., Diagnostic Confidence and Feasibility of a DeepLearning Accelerated HASTE Sequence of the Abdomen in a SingleBreath-Hold, Investigative Radiology, Volume 56, Number 5, May 2021
[16] Shanbhogue K et al. Accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based imagereconstruction: qualitative and quantitative comparison of image qualitywith conventional T2-weighted FS sequence. Eur Radiol. 2021 May 7.
Deep Resolve Boost
[17] Herrmann J et al., Development and Evaluation of Deep Learning-Accelerated Single-Breath-Hold Abdominal HASTE at 3 T Using VariableRefocusing Flip Angles. Invest Radiol. 2021 Apr 22.
[18] Gassenmaier S et al., Accelerated T2-Weighted TSE Imaging of theProstate Using Deep Learning Image Reconstruction: A ProspectiveComparison with Standard T2-Weighted TSE Imaging, Cancers, 13,3593 (2021)

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	[19] Herrmann J et al., Feasibility and implementation of a Deep LearningMR reconstruction for TSE sequences in musculoskeletal imaging,Diagnostics, 11, 1484 (2021)
	Publications under review:
	[20] Judith Herrmann et al., Feasibility and diagnostic confidence of deeplearning reconstructed TSE imaging of the knee at 1.5 and 3 T. Aprospective study.
	[21] Alsop, D. C. (1997). Phase insensitive preparation of singleshot RARE: application to diffusion imaging in humans. Magneticresonance in medicine, 527-533.
BLADE_Diffusion	[22] Fu, Q., Kong, X.-c., Liu, D.-x., Guo, Y.-h., Zhou, K., Lei, Z.-q., &Zheng, C.-s. (2021). Clinical utility of turbo gradient and spin echoBLADE-DWI (TGSE-BLADE-DWI) for orbital tumors comparedwith readout-segmented echo-planar DWI. Proc. Intl. Soc. Mag.Reson. Med., (p. 3933).
	[23] Fu, Q., Kong, X.-c., Liu, D.-x., Guo, Y.-h., Zhou, K., Lei, Z.-q., &Zheng, C.-s. (2021). The efficacy 2D turbo gradient- and spin-echodiffusion-weighted imaging for cerebellopontine angle tumors.Proc. Intl. Soc. Mag. Reson. Med., (p. 3934).
	[24] Hu, H. H., McAllister, A. S., Jin, N., Lubeley, L. J., Selvaraj, B., Smith,M., ... Zhou, K. (2019). Comparison of 2D BLADE turbo gradient-andspin-Echo and 2D spin-Echo Echo-planar diffusion-weighted brain MRI at3 T: preliminary experience in children. Academic radiology, 1597-1604.
	[25] Pipe, J. G., Farthing, V. G., & Forbes, K. P. (2002). MultishotDiffusion-Weighted FSE UsingPROPELLER MRI. Magnetic Resonancein Medicine, 42-52.
	[26] Sheng, Y., Hong, R., Sha, Y., Zhang, Z., Zhou, K., & Fu, C. (2020).Performance of TGSE BLADE DWI compared with RESOLVE DWI in thediagnosis of cholesteatoma. BMC medical imaging, 1-9.
	[27] Srinivasan, G., Rangwala, N., & Zhou, X. J. (2018). Steer-PROP: aGRASE-PROPELLER sequence with interecho steering gradient pulses.Magnetic resonance in medicine, 2533-2541.
	[28] Yuan, T., Sha, Y., Zhang, Z., Liu, X., Ye, X., Sheng, Y., ... Fu, C.(2020). TGSE diffusion-weighted pulse sequence in the evaluation ofoptic neuritis: Acomprehensive comparison of image quality withRESOLVE DWI. Proc. Intl. Soc. Mag. Reson. Med., (p. 4137).
	[29] Zhou, K., Liu, W., & Cheng, S. (2018). Non-CPMG PROPELLERdiffusion imaging: comparison of phase insensitive preparation with splitacquisition. Proc. Intl. Soc. Mag. Reson. Med., (p. 5320).

11.Safety and Effectiveness

The device labeling contains instructions for use and any necessary cautions and warnings to ensure safe and effective use of the device.

Risk Management is ensured via a risk analysis in compliance with ISO 14971, to identify and provide mitigation of potential hazards early in the design cycle

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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 Vida with software syngo MR XA50A conforms to the following FDA recognized and international IEC, ISO and NEMA standards:

RecognitionNumber	ProductArea	Title of Standard	ReferenceNumber and date	StandardsDevelopmentOrganization
19-4	General II(ES/EMC)	C1:2009/(R)2012 andA2:2010/(R)2012 (ConsolidatedText) Medical electricalequipment - Part 1: Generalrequirements for basic safetyand essential performance (IEC60601-1:2005, MOD)	ES60601-1:2005/(R)2012and A1:2012	ANSI AAMI
12-295	Radiology	Medical electrical equipment -Part 2-33: Particularrequirements for the basicsafety and essentialperformance of magneticresonance equipment formedical diagnosis	60601-2-33 Ed. 3.2 IECb:2015	IEC
5-40	General I(QS/RM)	Medical devices - Application ofrisk management to medicaldevices	14971 Secondedition 2007-03-01	ISO
5-114	General I(QS/RM)	Medical devices - Part 1:Application of usabilityengineering to medical devices	62366-1:2015	ANSI AAMIIEC
13-79	Software/Informatics	Medical device software -Software life cycle processes[Including Amendment 1 (2016)]	62304:2006/A1:2016	ANSI AAMIIEC
12-232	Radiology	Acoustic Noise MeasurementProcedure for DiagnosingMagnetic Resonance ImagingDevices	MS 4-2010	NEMA
12-288	Radiology	Standards PublicationCharacterization of PhasedArray Coils for DiagnosticMagnetic Resonance Images	MS 9-2008 (R2014)	NEMA
12-300	Radiology	Digital Imaging andCommunications in Medicine(DICOM) Set 03/16/2012Radiology	PS 3.1 - 3.20(2016)	NEMA
12-331	Radiology	Characterization ofRadiofrequency (RF) CoilHeating in Magnetic ResonanceImaging Systems	StandardsPublication MS 14-2019	NEMA

12.Conclusion as to Substantial Equivalence

MAGNETOM Vida with software syngo MR XA50A has the same intended use and same basic technological characteristics than the predicate device system,

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Image /page/10/Picture/0 description: The image contains the logo for Siemens Healthineers. The word "SIEMENS" is in teal, and the word "Healthineers" is in orange. To the right of the words is a graphic of orange dots.

MAGNETOM Vida with syngo MR XA31A, with respect to the magnetic resonance features and functionalities. While there are some differences in technical features compared to the predicate device, the differences have been tested and the conclusions from all verification and validation data suggest that the features bear an equivalent safety and performance profile to that of the predicate device.

Siemens believes that MAGNETOM Vida with software syngo MR XA50A is substantially equivalent to the currently marketed device MAGNETOM Vida with software syngo MR XA31A (K203443, cleared on March 31, 2021).

Regulation Number and Section

§ 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.