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

The subject device, MAGNETOM Cima.X Fit with software syngo MR XA61A, consists 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 Cima.X Fit with syngo MR XA61:

Hardware

New Hardware:
→ 3D Camera

Modified Hardware:

→ Host computers ((syngo MR Acquisition Workplace (MRAWP) and syngo MR Workplace (MRWP)).
MaRS (Measurement and Reconstruction System).
→ Gradient Coil
→ Cover
→ Cooling/ACSC
→ SEP
→ GPA
→ RFCEL Temp
→ Body Coil
→ Tunnel light

Software

New Features and Applications:

-> GRE_PC
→ Physio logging
-> Deep Resolve Boost HASTE
Deep Resolve Boost EPI Diffusion
→ Open Recon
-> Ghost reduction (DPG)
-> Fleet Ref Scan
→ Manual Mode
→ SAMER
→ MR Fingerprinting (MRF)1

Modified Features and Applications:

→ BEAT nav (re-naming only).
myExam Angio Advanced Assist (Test Bolus).
→ Beat Sensor (all sequences).
Stimulation monitoring
-> Complex Averaging

AI/ML Overview

I am sorry, but the provided text does not contain the acceptance criteria and the comprehensive study details you requested for the "MAGNETOM Cima.X Fit" device, particularly point-by-point information on a multi-reader multi-case (MRMC) comparative effectiveness study or specific quantitative acceptance criteria for its AI features like Deep Resolve Boost or Deep Resolve Sharp.

The document is a 510(k) summary for a Magnetic Resonance Diagnostic Device (MRDD), highlighting its substantial equivalence to a predicate device. While it mentions AI features and their training/validation, it does not provide the detailed performance metrics or study design to fully answer your request.

Here's what can be extracted based on the provided text, and where information is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

The document mentions that the impact of the AI networks (Deep Resolve Boost and Deep Resolve Sharp) has been characterized by "several quality metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)," and evaluated by "visual comparisons to evaluate e.g., aliasing artifacts, image sharpness and denoising levels" and "perceptual loss." For Deep Resolve Sharp, "an evaluation of image sharpness by intensity profile comparisons of reconstructions with and without Deep Resolve Sharp" was also conducted.

However, specific numerical acceptance criteria (e.g., PSNR > X, SSIM > Y), or the actual reported performance values against these criteria are not provided in the text. The document states that the conclusions from the non-clinical data suggest that the features bear an equivalent safety and performance profile to that of the predicate device, but no quantitative data to support this for the AI features is included in this summary.

AI Feature	Acceptance Criteria (Not explicitly stated with numerical values in the text)	Reported Device Performance (No quantitative results provided in the text)
Deep Resolve Boost	- PSNR (implied to be high) - SSIM (implied to be high) - Visual comparisons (e.g., absence of aliasing artifacts, good image sharpness, effective denoising levels)	Impact characterized by these metrics and visual comparisons. Claims of equivalent safety and performance profile to predicate device. No specific quantitative performance values (e.g., actual PSNR/SSIM scores) are reported in this document.
Deep Resolve Sharp	- PSNR (implied to be high) - SSIM (implied to be high) - Perceptual loss - Visual rating - Image sharpness by intensity profile comparisons (reconstructions with and without Deep Resolve Sharp)	Impact characterized by these metrics, verified and validated by in-house tests. Claims of equivalent safety and performance profile to predicate device. No specific quantitative performance values are reported in this document.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):

Deep Resolve Boost:
- Test Set Description: The text mentions that "the performance was evaluated by visual comparisons." It does not explicitly state a separate test set size beyond the validation data used during development. It implies the performance evaluation was based on the broad range of data covered during training and validation.
- Data Provenance: Not specified (country of origin or retrospective/prospective). The data was "retrospectively created from the ground truth by data manipulation and augmentation."
Deep Resolve Sharp:
- Test Set Description: The text mentions "in-house tests. These tests include visual rating and an evaluation of image sharpness by intensity profile comparisons of reconstructions with and without Deep Resolve Sharp." Similar to Deep Resolve Boost, a separate test set size is not explicitly stated. It implies these tests were performed on data from the more than 10,000 high-resolution 2D images used for training and validation.
- Data Provenance: Not specified (country of origin or retrospective/prospective). The data was "retrospectively created from the ground truth by data manipulation."

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

Not specified. The document mentions "visual comparisons" and "visual rating" as part of the evaluation but does not detail how many experts were involved or their qualifications.

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

Not specified.

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, an MRMC comparative effectiveness study is not mentioned in this document as being performed to establish substantial equivalence for the AI features. The document relies on technical metrics and visual comparisons of image quality to demonstrate equivalence.

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

The evaluation mentioned, using metrics like PSNR, SSIM, perceptual loss, and intensity profile comparisons, are indicative of standalone algorithm performance in terms of image quality. Visual comparisons and ratings would involve human observers, but the primary focus described is on the image output quality itself from the algorithm. However, no specific "standalone" study design with comparative performance metrics (e.g., standalone diagnostic accuracy) is detailed.

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

Deep Resolve Boost: "The acquired datasets (as described above) represent the ground truth for the training and validation." This implies the high-quality, full-data MRI scans before artificial undersampling or noise addition served as the ground truth. This is a technical ground truth based on the original acquired MRI data, not a clinical ground truth like pathology or expert consensus on a diagnosis.
Deep Resolve Sharp: "The acquired datasets represent the ground truth for the training and validation." Similar to Deep Resolve Boost, this refers to technical ground truth from high-resolution 2D images before manipulation.

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: on more than 10,000 high resolution 2D images.

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

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 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: "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."

In summary, the document focuses on the technical aspects of the AI features and their development, demonstrating substantial equivalence through non-clinical performance tests and image quality assessments, rather than clinical efficacy studies with specific diagnostic accuracy endpoints or human-AI interaction evaluations.

Summary

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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.

February 29, 2024

Siemens Medical Solutions USA, Inc. Alina Goodman Regulatory Affairs Professional 40 Liberty Boulevard Malvern, Pennsylvania 19355

Re: K232765

Trade/Device Name: MAGNETOM Cima.X Fit Regulation Number: 21 CFR 892.1000 Regulation Name: Magnetic Resonance Diagnostic Device Regulatory Class: Class II Product Code: LNH, LNI, MOS Dated: February 5, 2024 Received: February 5, 2024

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.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

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Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely,

D.H.

Daniel M. Krainak, Ph.D. Assistant Director DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

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Indications for Use

Submission Number (if known)

K232765

Device Name

MAGNETOM Cima.X Fit

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)

e-Counter Use (21 CFR 801 Subpart C)

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K232765

510(k) Summary

This summary of 510(k) safety and effectiveness information is being submitted in accordance with the requirements of the Safe Medical Devices Act 1990 and 21 CFR § 807.92.

1. General Information

Establishment:	Siemens Medical Solutions USA, Inc.40 Liberty BoulevardMalvern, PA 19355, USARegistration Number: 2240869
Date Prepared:	August 03rd, 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 GoodmanRegulatory 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 Cima.X Fit
Classification Name:	Magnetic Resonance Diagnostic Device (MRDD)
Classification Panel:	Radiology
CFR Code:	21 CFR § 892.1000
Classification:	II
Product Code:	Primary: LNH
	Secondary: LNL MOS

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4. Legally Marketed Predicate and Reference Device

Predicate Device 4.1.

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: LNH
	Secondary: LNI, MOS

4.2.Reference Device
Trade name:	MAGNETOM Prisma
510(k) Number:	K202014
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 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 Free.Max
510(k) Number:	K220575
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 device are the same as the predicate device:

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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

A high-level summary of the new and modified hardware and software is provided below:

For MAGNETOM Cima.X Fit with syngo MR XA61:

Hardware

New Hardware:

→ 3D Camera

Modified Hardware:

→ Host computers ((syngo MR Acquisition Workplace (MRAWP) and syngo MR Workplace (MRWP)).
MaRS (Measurement and Reconstruction System).
→ Gradient Coil
→ Cover
→ Cooling/ACSC
→ SEP
→ GPA
→ RFCEL Temp
→ Body Coil
→ Tunnel light

Software

New Features and Applications:

-> GRE_PC
→ Physio logging
-> Deep Resolve Boost HASTE
Deep Resolve Boost EPI Diffusion
→ Open Recon
-> Ghost reduction (DPG)
-> Fleet Ref Scan
→ Manual Mode
→ SAMER
→ MR Fingerprinting (MRF)1

Modified Features and Applications:

→ BEAT nav (re-naming only).
myExam Angio Advanced Assist (Test Bolus).
→ Beat Sensor (all sequences).
Stimulation monitoring
-> Complex Averaging

1 MR Fingerprinting (MRF) is cleared under K213805

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6. Substantial Equivalence

MAGNETOM Cima.X Fit with software syngo MR XA60A is substantially equivalent to the following predicate device:

Predicate Device	FDA Clearance Number andDate	ProductCode	Manufacturer
MAGNETOM Vida with syngoMR XA50A	K213693, cleared on February25, 2022	LNHLNI, MOS	Siemens Healthcare GmbH
Reference Device	FDA Clearance Number andDate	ProductCode	Manufacturer
MAGNETOM Prisma withsyngo MR XA30A	K202014 cleared,September 08, 2020	LNH,LNI, MOS	Siemens Healthcare GmbH
MAGNETOM Sola with syngoMR XA51A	K221733 cleared, September13, 2022	LNH,LNI, MOS	Siemens Healthcare GmbH
MAGNETOM Free.Max withsyngo MR XA50A	K220575 cleared, June 22,2022	LNH,LNI, MOS	Siemens ShenzhenMagnetic Resonance Ltd.

7. Technological Characteristics

The subject device, MAGNETOM Cima.X Fit with software syngo MR XA61A, is substantially equivalent to the predicate device with regard to the operational environment, programming language, operating system and performance.

The subject device conforms 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:

	Subject Devices	Predicate Device	Reference Devices
Hardware	MAGNETOM Cima.X Fitwith software syngo MRXA61A	MAGNETOM Vida withsyngo MR XA50A(K213693)	MAGNETOM Sola withsyngo MR XA51A(K221733)MAGNETOM Prisma withsyngo MR XA30A(K202014)MAGNETOM Free.Maxwith syngo MR XA50A(K220575)
Magnet System	Yes	Yes	Yes
RF System	Yes	Yes	Yes
Transmissiontechnique	Yes	Yes	Yes
Gradient System	New or modified features aslisted in the DeviceDescription above	Yes	Yes
Patient Table	Yes	Yes	Yes

Summary hardware comparison table for the subject and predicate device

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Multi-NuclearOption - SupportedNuclei	Yes	Yes	Yes
Computer	YesModified	Yes	Yes
Coils	YesNew, based on predicate:Tx/Rx Knee 15, Tx/Rx Knee 15Flare, 4 Ch BIBreast coil (already introducedwith reference system)	Yes	Yes
Other HWcomponents	New or modified HWcomponents as listed in theDevice Description above	Yes	Yes

Summary software comparison table for the subject and predicate devices

	Subject Device	Predicate Device
Software	MAGNETOM Cima.X Fit withsoftware syngo MR XA61A	MAGNETOM Vida with syngoMR XA50A(K213693)
Sequences
SE-based pulse sequence types	New feature as listed in the DeviceDescription above	Yes
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	Modified features andapplications as listed in the DeviceDescription above	Yes
User interface and user interaction	Yes	Yes
Viewing and post-processing	New or modified viewing and post-processing features as listed in theDevice Description above	Yes
Workflow and software utilization	Yes	Yes
Patient Management	Yes	Yes
Scan Modes and Pulse Sequences	Yes	Yes
Scanning	Modified and new features andapplications as listed in theCover letter, Device Descriptionand Substantial EquivalenceComparison Tables	Yes
Reconstruction	New featureas listed in the Device Descriptionabove	Yes
Image Display	Yes	Yes

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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

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
Performance bench test	new and modified hardware
Electrical, mechanical, structural, and related system safety test	System as a whole	- AAMI / ANSI ES60601-1- IEC 60601-2-33
Electrical safety and electromagnetic compatibility (EMC)	System as a whole	IEC 60601-1-2

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 device:

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

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.

Al Features/Applications training and validation:

The information below shows an executive summary of training and validation dataset of the Al features:

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	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.

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.

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Feature	Publications
Deep Resolve Boost EPI Diffusion	[14_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)[14_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)[14_3] Afat S et al., Acquisition time reduction of diffusion-weighted liver imaging using deep learning image reconstruction. Diagn Interv Imaging, (2022).[14_4] Benkert T et al., Improved Clinical Diffusion Weighted Imaging by Combining Deep Learning Reconstruction, Partial Fourier, and Super Resolution, ISMRM (2022)
Deep Resolve Boost HASTE	[14_6] 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.[14_7] 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.[14_8] 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.[14_9] 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.[14_10] 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.[14_11] Ginocchio LA et al., Accelerated T2-weighted MRI of the liver at 3 T using a single-shot technique with deep learning-based image reconstruction: impact on the image quality and lesion detection. Abdom Radiol (NY). 2022 Sep 28.[14_12] Herrmann J et al., Comprehensive clinical evaluation of a deep learning-accelerated, single-
GRE_PC	breath-hold abdominal HASTE at 1.5 T and 3 T. AcadRadiol. 2022 Apr 22:S1076-6332(22)00195-7.[14_13] Ichinohe F. et al., Usefulness of Breath-HoldFat-Suppressed T2-Weighted Images With DeepLearning-Based Reconstruction of the Liver, InvestRadiol., 2022[14_14] Guenthner C. et al. Ristretto MRE: Ageneralized multi-shot GRE-MRE sequence. NMRBiomed 2019; 32:e4049.
Ghost reduction (DPG)	[14_24] Hoge WS, Polimeni JR. Dual-polarity GRAPPAfor simultaneous reconstruction and ghost correction ofecho planar imaging data. Magn Reson Med. 2016Jul;76(1):32-44. doi: 10.1002/mrm.25839. Epub 2015
Fleet Reference Scan	[14_5] Polimeni JR, Bhat H, Witzel T, Benner T, FeiweierT, Inati SJ, Renvall V, Heberlein K, Wald LL. Reducingsensitivity losses due to respiration and motion inaccelerated echo planar imaging by reordering theautocalibration data acquisition. Magn Reson Med.2016 Feb;75(2):665-79. doi: 10.1002/mrm.25628. Epub2015 Mar 23. PMID: 25809559; PMCID: PMC4580494.
BioMatrix Motion Sensor (SAMER)	[14_25] D. Polak, D N. Splitthoff, etc. Scout acceleratedmotion estimation and reduction (SAMER). MagnReson Med. 2022 Jan;87(1):163-178.[14_26] M. Lang, A. Tabari, etc. Clinical Evaluation ofScout Accelerated Motion Estimation and ReductionTechnique for 3D MR Imaging in the Inpatient andEmergency Department Settings. American Journal ofNeuroradiology 2023 Jan.
Complex Averaging	[14_22] Walsh DO, Gmitro AF, Marcellin MW. Adaptivereconstructionof phased array MR imagery. Magn Reson Med. 2000May1;43(5):682-90.[14_23] Kordbacheh H, Seethamraju RT, Weiland E,Kiefer B, Nickel MD, Chulroek T, et al. Image quality anddiagnostic accuracy of complex-averaged high b valueimages in diffusion-weighted MRI of prostate cancer.Abdom Radiol (NY). 2019;44(6):2244-53.

Furthermore, additional clinical publications were referenced to provide information on the use of the following features and functions:

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10. Safety and Effectiveness

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

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

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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 Cima.X Fit with software syngo MR XA61A conforms to the following FDA recognized and international IEC, ISO and NEMA standards:

RecognitionNumber	Product Area	Title of Standard	Reference Numberand date	StandardsDevelopmentOrganization
19-4	General	Medical electrical equipment - part 1:general requirements for basic safety andessential 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 safety andessential performance - Collateral Standard: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 the basic safetyand essential performance of magneticresonance equipment for medical 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 Ratio andImage Uniformity for Single-Channel Non-Volume Coils in Diagnostic MR Imaging	MS 6-2008 (R2014)	NEMA
12-349	Radiology	Digital Imaging and Communications inMedicine (DICOM)	PS 3.1 - 3.20 (2022d)	NEMA
2-258	Biocompatibi-lity	Biological evaluation of medical devices -part 1: evaluation and testing within a riskmanagement process. (Biocompatibility)	10993-1 Fifth edition2018-08	AAMIANSIISO

11. Conclusion as to Substantial Equivalence

MAGNETOM Cima.X Fit with software syngo MR XA61A 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 tested and the conclusions from all verification

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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 Cima.X Fit with software syngo MR XA61A is substantially equivalent to the currently marketed device MAGNETOM Vida with software syngo MR XA50A (K213693, cleared on February 25, 2022).

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.