(152 days)
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 bony anatomy of the sacrum, hip bones and femoral heads; and the lumbar spine region, which includes the bony anatomy of the vertebrae from L3 to S1. BoneMRI is not to be used for diagnosis or monitoring of (primary or metastatic) tumors.
Warning: BoneMRI images are not intended to replace CT images.
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 orthopedic 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. The application uses an algorithm to detect bone images from MRIs obtained using a specific acquisition sequence. The algorithm training sets included information from multiple clinical sites, multiple anatomies and multiple scanners to ensure that the trained algorithm was robust with respect to the approved indications for use. None of the data used in the training dataset was used subsequently in the validation dataset.
Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Metric | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| 3D bone morphology reconstruction accuracy | Mean absolute cortical delineation error below 1.0 mm | Mean absolute cortical delineation error below 1.0 mm on average |
| Tissue radiodensity reconstruction accuracy | Mean deviation below 25 HU (overall) | Mean deviation below 25 HU on average |
| Tissue radiodensity reconstruction accuracy (bone) | Mean deviation below 55 HU (specifically for bone) | Mean deviation below 55 HU specifically for bone |
| Tissue radiodensity contrast correlation | Mean HU correlation coefficient above 0.75 (bone) | Mean HU correlation coefficient above 0.75 specifically for bone |
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size: 73 patients.
- Data Provenance:
- Country of Origin: Europe, Asia.
- Retrospective/Prospective: The imaging data consists of BoneMRI and standard CT from the same patient and anatomical region, acquired during "previously conducted clinical investigations." This indicates the data was retrospective.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts
The document does not specify the number of experts used or their qualifications for establishing ground truth for the test set. Instead, it states that the ground truth was established by comparing BoneMRI images to co-registered CT scans, which served as the "reference standard." The validations were conducted by MRIguidance "based on an algorithm to detect bone images from MRIs." This implies an algorithmic, rather than human expert-based, determination of ground truth for the quantitative analysis.
4. Adjudication Method for the Test Set
The document does not describe an adjudication method for the test set in the context of human expert review. Given that the study was a quantitative voxel-by-voxel analysis comparing BoneMRI to CT as a reference standard, expert adjudication in the traditional sense (e.g., for disagreements in human annotations) would not be applicable here.
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 focusing on human reader improvement with AI assistance was not described in this document. The study described is a quantitative technical validation of the device's ability to reconstruct bone morphology and radiodensity compared to CT as a reference.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done
Yes, a standalone performance evaluation was done. The study focused on the quantitative accuracy of the BoneMRI application (the algorithm) in reconstructing 3D bone morphology, radiodensity, and radiodensity contrast, using CT as the reference standard. This was a "voxel-by-voxel validation" of the algorithm's output.
7. The Type of Ground Truth Used
The ground truth used was co-registered CT scans (reference standard). The study aimed to validate the quantitative accuracy of BoneMRI by comparing its output (3D bone morphology, radiodensity, and radiodensity contrast) directly against the corresponding measurements from CT images.
8. The Sample Size for the Training Set
The document states, "The algorithm training sets included information from multiple clinical sites, multiple anatomies and multiple scanners to ensure that the trained algorithm was robust with respect to the approved indications for use." However, it does not specify the sample size for the training set. It explicitly mentions, "None of the data used in the training dataset was used subsequently in the validation dataset."
9. How the Ground Truth for the Training Set was Established
The document mentions that the "parameters of the model were obtained through an algorithm development pipeline." While it doesn't explicitly describe the method for establishing ground truth for individual training cases, given that the validation focused on comparing against CT, it is highly likely that CT images also served as the ground truth (or a strong reference for ground truth) during the training phase to enable the algorithm to learn the relationship between MRI data and CT-like bone characteristics (e.g., radiodensity, morphology). The mention of "assign[ing] a Hounsfield Unit (HU) value to a single volume element" suggests a quantitative ground truth for training.
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November 16, 2022
MRIguidance B.V. % Sujith Shetty Executive Vice President MAXIS Medical 3031 Tisch Way, Suite 1010 SAN JOSE CA 95128
Re: K221762
Trade/Device Name: BoneMRI v1.4 Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: OIH Dated: October 7, 2022 Received: October 11, 2022
Dear Sujith 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 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.
Daniel M. Krainak, Ph.D. Assistant Director Magnetic Resonance and Nuclear Medicine Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT 8: Office of 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) K221762
Device Name BoneMRI V1.4
Indications for Use (Describe)
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 bony anatomy of the sacrum, hip bones and femoral heads; and the lumbar spine region, which includes the bony anatomy of the vertebrae from L3 to S1. BoneMRI is not to be used for diagnosis or monitoring of (primary or metastatic) tumors.
Warning: BoneMRI images are not intended to replace CT images.
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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Image /page/3/Picture/1 description: The image shows the logo for MRI Guidance. The logo consists of a circle with an arrow pointing to the upper left, followed by the text "MRI" in a larger font and "guidance" in a smaller font. The logo is set against a blue background.
510(K) 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: K221762
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 MRIquidance B.V. Email: marijn@mriguidance.com Tel.: +31 610 505 649 Date Prepared: December 22, 2021
Official Correspondent
Dr. Sujith Shetty Executive Vice President MAXIS LLC Email: sjshetty@maxismedical.com
Device Information
| Trade Name: | BoneMRI application |
|---|---|
| Common Name: | MRI image enhancement software |
| Classification name: | Medical image management and processingsystem (21CRF892.2050) |
| Regulatory Class: | Class II |
| Product Code: | QIH |
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Predicate Device
| Name | Manufacturer | 510(k)# |
|---|---|---|
| BoneMRI v1.2 | MRIguidance B.V. | K202404 |
odicato Dovico
This predicate has not been subject to a design-related recall. No reference devices were used in this submission.
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 orthopedic 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. The application uses an algorithm to detect bone images from MRIs obtained using a specific acquisition sequence. The algorithm training sets included information from multiple clinical sites, multiple anatomies and multiple scanners to ensure that the trained algorithm was robust with respect to the approved indications for use. None of the data used in the training dataset was used subsequently in the validation dataset.
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Image /page/5/Picture/1 description: The image shows the logo for MRI guidance. The logo consists of a circle with an arrow pointing to the right inside of it. To the right of the circle, the text "MRI guidance" is written in a sans-serif font.
Indications for Use
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 bony anatomy of the sacrum, hip bones, and femoral heads; and the lumbar spine region, which includes the bony anatomy of the vertebrae from L3 to S1. BoneMRI is not to be used for diagnosis or monitoring of (primary or metastatic) tumors.
Warning: BoneMRI images are not intended to replace CT images.
Comparison of Technological Characteristics with the Predicate Device:
A comparison of the intended use, indication for use, and technological characteristics of the BoneMRI v1.4 application to the predicate device, BoneMRI v1.2, is presented below. We have included the attributes suggested in FDA's website guidance for this comparison.
| Predicate DeviceBoneMRI v1.2 | Subject DeviceBoneMRI v1.4 | Comment | |
|---|---|---|---|
| Indications for Use | BoneMRI is animage processingsoftware that can beused for imageenhancement inMRI images. It canbe used to visualizethe bone structuresin MRI images withenhanced contrastwith respect to the | BoneMRI is animage processingsoftware that can beused for imageenhancement inMRI images. It canbe used to visualizethe bone structuresin MRI images withenhanced contrastwith respect to the | Similar –BoneMRIv1.4 has anexpandedindications foruseincluding theboney structuresof the lumbarspine. |
A. Intended Use
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| Predicate DeviceBoneMRI v1.2 | Subject DeviceBoneMRI v1.4 | Comment | |
|---|---|---|---|
| surrounding softtissue. It is to beused in the pelvicregion, whichincludes the boneyanatomy of thesacrum, hip bonesand femoral heads.Warning: BoneMRIimages are notintended to replaceCT images and arenot to be used fordiagnosis ormonitoring of(primary ormetastatic) tumors. | surrounding softtissue. It is to beused in the pelvicregion, whichincludes the bonyanatomy of thesacrum, hip bonesand femoral heads;and the lumbarspine region, whichincludes the bonyanatomy of thevertebrae from L3 toS1. BoneMRI is notto be used fordiagnosis ormonitoring of(primary ormetastatic) tumors.Warning: BoneMRIimages are notintended to replaceCT images | ||
| 21CFR Section | 892.2050 | 892.2050 | The same |
| Product Code | QIH | QIH | The same |
| Target Population | Adults | Adults | The same |
B. Technological Characteristics
| Predicate Device BoneMRI v1.2 | Subject Device BoneMRI v1.4 | Comment | |
|---|---|---|---|
| Device Nature | Software package | Software package | The same |
| Operating System | Linux | Linux | The same |
| Data input | MRI images in DICOM format | MRI images in DICOM format | The same |
| Predicate DeviceBoneMRI v1.2 | Subject DeviceBoneMRI v1.4 | Comment | |
| Data output | MRI images inDICOM format | MRI images inDICOM format | The same |
| ProcessingAlgorithms | MRIguidancesoftwareimplements animageenhancementalgorithm usingconvolutionalneural network.Original images areenhanced byrunning themthrough a cascadeof filter banks,where thresholdingand scalingoperations areapplied. Separateneural network-based filters areobtained to assigna Hounsfield Unit(HU) value to asingle volumeelement, based onintensity andcontextualinformation. Theparameters of themodel wereobtained throughan algorithmdevelopmentpipeline. | MRIguidancesoftwareimplements animageenhancementalgorithm usingconvolutionalneural network.Original imagesare enhanced byrunning themthrough a cascadeof filter banks,where thresholdingand scalingoperations areapplied. Separateneural network-based filters areobtained to assigna Hounsfield Unit(HU) value to asingle volumeelement, based onintensity andcontextualinformation. Theparameters of themodel wereobtained throughan algorithmdevelopmentpipeline. | The same |
| User Interface | None - enhancedimages are viewedon existing PACS | None - enhancedimages are viewedon existing PACS | The same |
| Predicate DeviceBoneMRI v1.2 | Subject DeviceBoneMRI v1.4 | Comment | |
| Workflow | The softwareoperates onDICOM files on thefile system,enhances theimages, and storesthe enhancedimages on the filesystem. The receiptof original DICOMimage files anddelivery ofenhanced imagesas DICOM filesdepends on othersoftware systems.Enhanced imagesco-exist with theoriginal images. | The softwareoperates onDICOM files on thefile system,enhances theimages, and storesthe enhancedimages on the filesystem. Thereceipt of originalDICOM image filesand delivery ofenhanced imagesas DICOM filesdepends on othersoftware systems.Enhanced imagesco-exist with theoriginal images. | The same |
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Image /page/8/Picture/1 description: The image shows the logo for MRI guidance. The logo consists of a circle with an arrow pointing to the right and the text "MRI guidance" to the right of the circle. The text is in a sans-serif font and is white. The background is blue.
Performance Data:
Previous performance testing to verify and validate the software of BoneMRI v1.2 for the pelvic and hip region was submitted and approved under K202404.
BoneMRI conducted the following performance testing:
- Software verification and validation testing 1.
- Studies that utilized retrospective clinical data to demonstrate the software 2. enhanced imaging quality in MR images via an enhancement of bone.
BoneMRI Lumbar spine region – Voxel-by-Voxel analysis
A quantitative voxel-by-voxel validation of BoneMRI was performed on imaging data from 73 patients. The demographics of the patient population is described in the table below.
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| Validation data demographics | |
|---|---|
| Number of patients | 73 |
| Indications | sacroiliitis, degenerative spine and pelvicdiseases, spondylolisthesis, radiculopathy,spondylosis and spinal fractures |
| Gender | Male: 48%Female: 52% |
| Age | $50 \pm 15$ years |
| Data origin/Ethnicity | EuropeAsia |
The imaging data consists of the BoneMRI and a standard CT of the same patient in the same anatomical region, acquired during previously conducted clinical investigations. The validations were conducted by MRIguidance based on an algorithm to detect bone images from MRIs obtained using a specific acquisition sequence.
Training and test datasets were selected and maintained to be appropriately independent of one another. All training and validation activities were recorded to ensure independence. In addition, validation was performed on data from independent sites (cross-site validation) to ensure that validation was performed on data from unseen centers.
The objective was to validate the quantitative accuracy of BoneMRI for the lumbar spine region using rigorous, objective, and unbiased statistical tests comparing bone morphology, radiodensity, and radiodensity contrast in BoneMRI and CT images. Therefore, the endpoints of this testing were the metrics that described the accuracy of 3D bone morphology, radiodensity, and radiodensity contrast versus co-registered CT scans (reference standard) in terms of voxel-by-voxel HUs and standard deviations around these HU values.
The results from the validation testing were compared to the accuracy acceptance criteria, specified below, and were found to fall within the pre-specified acceptance criteria (p<0.05).
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The results demonstrate clinically acceptable accuracy on each of these endpoints.
The data provided demonstrate that BoneMRI application can
- o accurately reconstruct the 3D bone morphology with a mean absolute cortical delineation error below 1.0 mm on average;
- o accurately reconstructs the tissue radiodensity, with a mean deviation below 25 HU on average and a mean deviation below 55 HU specifically for bone;
- o accurately reconstructs the tissue radiodensity contrast, with a mean HU correlation coefficient above 0.75 specifically for bone.
CONCLUSION: BoneMRI demonstrates accurate bone morphology, radiodensity, and radiodensity contrast. Thus, BoneMRI is a useful tool to qualitatively and quantitatively assess the lumbar spine region.
Conclusions:
BoneMRI v1.4, based on the indications for use, product performance, and clinical information provided in this notification, has been shown to be substantially equivalent to the currently marketed predicate device, its predecessor, BoneMRI v1.2. The two devices have the same technological characteristics: both algorithms use the same 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 v1.4 technological characteristics, as well as performance data and verification and validation activities demonstrating that BoneMRI is as safe and effective as the predicate. This evaluation did not raise any new issues pertaining to safety or effectiveness.
§ 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).