BoneMRI is an image processing software that can be used for image enhancement in MRI images. It can be used to visualize the bone structures in MRI images with enhanced contrast with respect to the surrounding soft tissue. It is to be used in the pelvic region, which includes the boney anatomy of the sacrum, hip bones and femoral heads. Warning: BoneMRI images are not intended to replace CT images and are not to be used for diagnosis or monitoring of (primary or metastatic) tumors.

Device Description

The BoneMRI application is a standalone image processing software application that analyses 3D gradient echo MRI scans acquired with a dedicated MRI scan protocol. From the analysis, 3D tomographic radiodensity contrast images, called BoneMRI images, are constructed. The BoneMRI images can be used to visualize the bone structures in MR images with enhanced contrast with respect to the surrounding soft tissue. The application is designed to be used by imaging experts, such as radiologists or orthopaedic surgeons, typically in a physician's office. The BoneMRI application is a server application running in the clinic or hospital networks. It returns the reconstructed BoneMRI images as DICOM images.

AI/ML Overview

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Acceptance Criteria (Quantitative Endpoints)	Reported Device Performance
3D bone morphology with a mean absolute cortical delineation error below 1.0 mm	The data provided demonstrate that BoneMRI application v1.2 can accurately reconstruct the 3D bone morphology with a mean absolute cortical delineation error below 1.0 mm on average.
Tissue radiodensity with a mean deviation below 10 HU	The data provided demonstrate that BoneMRI application v1.2 can accurately reconstruct the tissue radiodensity with a mean deviation below 10 HU on average.
Bone radiodensity with a mean deviation below 55 HU	The data provided demonstrate that BoneMRI application v1.2 can accurately reconstruct the tissue radiodensity with a mean deviation below 55 HU for bone specifically.
Tissue radiodensity contrast with a mean HU correlation coefficient above 0.80	The data provided demonstrate that BoneMRI application v1.2 can accurately reconstruct the tissue radiodensity contrast with a mean HU correlation coefficient above 0.80 on average.
Bone radiodensity contrast with a mean HU correlation coefficient above 0.75	The data provided demonstrate that BoneMRI application v1.2 can accurately reconstruct the tissue radiodensity contrast with a mean HU correlation coefficient above 0.75 for bone specifically.

Study Details

Sample Size used for the test set and data provenance:
- Sample Size: 61 patients.
- Data Provenance: The text states, "imaging data from 61 patients, consisting of the BoneMRI and CT of the same patient, acquired during the previously conducted clinical investigations." This implies the data is retrospective as it was "previously conducted." The country of origin is not specified.
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- The provided text does not specify the number of experts used or their qualifications. The ground truth was established by comparing BoneMRI outputs directly to co-registered CT scans.
Adjudication method for the test set:
- The text describes a "voxel-by-voxel analysis" using an "in-house developed algorithm validation pipeline, the core validation framework." This suggests an automated, quantitative comparison against a reference standard (CT), rather than an expert adjudication method like 2+1 or 3+1. Therefore, the adjudication method was none in the traditional sense of human consensus.
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 was not done. The performance data section focuses on quantitative validation against CT scans, not on reader performance with or without AI assistance.
If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, a standalone performance study was done. The "Voxel-by-Voxel analysis" directly compares the output of the BoneMRI algorithm to CT scans, without involving human readers in the quantitative performance metrics.
The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- The ground truth used was co-registered CT scans. The study directly validated BoneMRI outputs (3D bone morphology, radiodensity, and radiodensity contrast) against these CT scans.
The sample size for the training set:
- The document does not explicitly state the sample size for the training set. It mentions that "The parameters of the model were obtained through an algorithm development pipeline," but does not give specific numbers for training data.
How the ground truth for the training set was established:
- The document does not explicitly describe how the ground truth for the training set was established. It only mentions that the "parameters of the model were obtained through an algorithm development pipeline," which implies data was used for training, but the process for establishing ground truth for that data is not detailed.

Summary

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December 22, 2021

Image /page/0/Picture/1 description: The image contains the logos of the Department of Health and Human Services and the Food and Drug Administration (FDA). The Department of Health and Human Services logo is on the left, and the FDA logo is on the right. The FDA logo includes the FDA acronym in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text.

MRIguidance B.V % Suji Shetty Executive Vice President Maxis Medical 7052 Hollow Lake Way San Jose, California 95120

Re: K202404

Trade/Device Name: BoneMRI Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH Dated: November 29, 2021 Received: November 30, 2021

Dear Suji Shetty:

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

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801); medical device reporting of medical device-related adverse events) (21 CFR 803) for 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,

Jessica Lamb, Ph.D. Assistant 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

510(k) Number (if known) K202404

Device Name BoneMRI

Indications for Use (Describe)

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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5.0 510(K) STATEMENT/SUMMARY

This summary of 510(k) safety and effectiveness information is being submitted in accordance with the requirements of SMDA 1990 and 21 CRF 807.92.

510 (k) number: K202404

I. Applicant Information

MRIguidance B.V. Gildstraat 91a 3572 EL, Utrecht The Netherlands info@mriguidance.com www.mriguidance.com +31 854000810

Contact Person

Marijn van Stralen Chief Technology Officer MRIguidance B.V. Email: marijn@mriguidance.com Tel .: +31 610 505 649 Date Prepared: December 22, 2021

Official Correspondant

Dr. Sujith Shetty Executive Vice President MAXIS LLC Email: sjshetty(@maxismedical.com

II. Device Information

Trade Name:	BoneMRI
Common Name:	MRI image enhancement software
Classification name:	Picture archiving and Communication system (21CRF892.2050)
Regulatory Class:	Class II
Product Code:	QIH

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III. Predicate Device

Name	Manufacturer	510(k)#
SubtleMR	Subtle Medical, Inc.	K191688

This predicate has not been subject to a design-related recall. No reference devices were used in this submission.

IV. Device Description

The BoneMRI images can be used to visualize the bone structures in MR images with enhanced contrast with respect to the surrounding soft tissue. The application is designed to be used by imaging experts, such as radiologists or orthopaedic surgeons, typically in a physician's office.

The BoneMRI application is a server application running in the clinic or hospital networks. It returns the reconstructed BoneMRI images as DICOM images.

V. Indications for Use

Warning: BoneMRI images are not intended to replace CT images and are not to be used for diagnosis or monitoring of (primary or metastatic) tumors.

VI. Comparison of Technological Characteristics with the Predicate Device:

A comparison of the intended use, indication for use, and technological characteristics of the BoneMRI application to the predicate device SubtleMR are presented below. We have included the attributes suggested in FDA's website guidance for this comparison.

Name	Manufacturer	510(k)#
SubtleMR	Subtle Medical, Inc.	K191688

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A. Intended Use

	Predicate DeviceSubtleMR	Subject DeviceBoneMRI	Comment
Intended Use	SubtleMR is an imageprocessing softwarethat can be used forimage enhancement inMRI images. It can beused to reduce imagenoise for head, spine,neck, and knee MRI,or increase imagesharpness for non-contrast-enhancedhead MRI.	BoneMRI is an imageprocessing softwarethat can be used forimage enhancement inMR images. It can beused to visualize thebone structures in MRimages with enhancedcontrast with respectto the surrounding softtissue	Similar -Intended uses arethe same for Imageenhancements forMRI. But theintended usedifferences doesnot affect thesafety andeffectiveness of thedevice when usedas labeled and issimilar to thepredicate use.
21CFR Section	892.2050	892.2050	The same
Product Code	LLZ	QIH	Similar
Target Population	Adults	Adults	The same

B. Technological Characteristics

	Predicate DeviceSubtleMR	Subject DeviceBoneMRI	Comment
Device Nature	Software package	Software package	The same
Operating System	Linux	Linux	The same
Data input	MRI images inDICOM format	MRI images inDICOM format	The same
Data output	MRI images inDICOM format	MRI images inDICOM format	The same
ProcessingAlgorithms	SubtleMR softwareimplements an imageenhancementalgorithm usingconvolutional neuralnetwork basedfiltering. Originalimages are enhancedby running through acascade of filter	MRIguidancesoftware implementsan imageenhancementalgorithm usingconvolutional neuralnetwork. Originalimages are enhancedby running themthrough a cascade of	Different –The algorithm whileusing similarmethodology, usesdifferent filters andoutputs to enhancethe image. Thedifference does notaffect the safety and
	Predicate DeviceSubtleMR	Subject DeviceBoneMRI	Comment
	banks, wherethresholding andscaling operations areapplied. Separateneural network basedfilters are obtainedfor noise reductionand sharpnessincrease. Theparameters of thefilters were obtainedthrough an imageguided optimizationprocess.	filter banks, wherethresholding andscaling operationsare applied. Separateneural network-based filters areobtained to assign aHounsfield Unit(HU) value to asingle volumeelement, based onintensity andcontextualinformation. Theparameters of themodel were obtainedthrough an algorithmdevelopmentpipeline.	effectiveness of thedevice when used aslabeled and is similarto the predicate use.
User Interface	None - enhancedimages are viewed onexisting PACSworkstations	None - enhancedimages are viewedon existing PACSworkstations	The same
Workflow	The softwareoperates on DICOMfiles on the filesystem, enhances theimages, and storesthe enhanced imageson the file system.The receipt oforiginal DICOMimage files anddelivery of enhancedimages as DICOMfiles depends on othersoftware systems.Enhanced images co-exist with the originalimages.	The softwareoperates on DICOMfiles on the filesystem, enhances theimages, and storesthe enhanced imageson the file system.The receipt oforiginal DICOMimage files anddelivery of enhancedimages as DICOMfiles depends onother softwaresystems. Enhancedimages co-exist withthe original images.	The same

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VII. Performance Data:

BoneMRI conducted the following performance testing:

Software verification and validation testing 1.
1. Studies that utilized retrospective clinical data to demonstrate the software enhanced imaging quality in MR images via an enhancement of bone.

BoneMRI Pelvic region - Voxel-bv-Voxel analysis

Quantitative voxel-by-voxel validation of BoneMRI was performed on imaging data from 61 patients, consisting of the BoneMRI and CT of the same patient, acquired during the previously conducted clinical investigations. MRIguidance conducted the validations based on an in-house developed algorithm validation pipeline, the core validation framework. The objective was to validate the quantitative accuracy of BoneMRI for the pelvic region using rigorous, objective, and unbiased statistical tests. The endpoints were the metrics that described the accuracy of 3D bone morphology, radiodensity, and radiodensity contrast versus co-registered CT scans in terms of voxel-by-voxel HUs and standard deviations around these HU values. The results demonstrate clinically acceptable accuracy on all of the endpoints.

The data provided demonstrate that BoneMRI application v1.2 can accurately reconstruct the 3D bone morphology with a mean absolute cortical delineation error below 1.0 mm on average; accurately reconstruct the tissue radiodensity with a mean deviation below 10 HU on average and a mean deviation below 55 HU for bone specifically; accurately reconstruct the tissue radiodensity contrast with a mean HU correlation coefficient above 0.80 on average and a mean HU correlation coefficient above 0.75 for bone specifically.

CONCLUSION: BoneMRI demonstrates accurate bone morphology, radiodensity, and radiodensity contrast. Thus, BoneMRI is a useful tool to qualitatively and quantitatively assess the pelvic region.

VIII. Conclusions:

BoneMRI, based on the indications for use, product performance, and clinical information provided in this notification, the subject device has been shown to be substantially equivalent to the currently marketed predicate device. The two devices have similar technological characteristics: both algorithms use image based reconstruction, and both methods have optimized parameters to ensure the robustness of the algorithm. This 510(k) submission includes information on the BoneMRI technological characteristics, as well as performance data and verification and validation activities demonstrating that BoneMRI is as safe and effective as the predicate, and does not raise different questions of safety and effectiveness.

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).