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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 increased 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 the spine, which includes the bony anatomy of the cervical, thoracic, lumbar, and S1 vertebrae. BoneMRI is indicated for use in patients 12 years and older.

BoneMRI is not to be used for diagnosis or monitoring of (primary or metastatic) tumors. BoneMRI images are not intended to replace CT images in general but can be used to visualize 3D bone morphology, tissue radiodensity and tissue radiodensity contrast.

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 of the gradient echo MRI scan, 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 on the clinic or hospital networks. It is available as fully on-premise software with specific GPU hardware requirements, or partly running as a managed cloud service, for which the environment in which the managed modules run is controlled by MRIguidance. The on-premise software is fully controlled by the clinic or hospital, and as such, no protected health information (PHI) will leave the clinic or hospital network. All data sent to the managed cloud server will be de-identified before it leaves the clinic or hospital network, and as such, the managed cloud service will not receive PHI.

Within the hospital network, the application communicates with a DICOM compatible imaging archive (e.g., a PACS) to receive input MRI and to return BoneMRI images. Reading of the resulting BoneMRI images is performed using regular DICOM compatible medical image viewing software.

The BoneMRI application uses an algorithm to detect bone images from MRIs obtained using a specific gradient echo acquisition sequence. The algorithm training sets included images 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 summary 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:

2. Sample Size Used for the Test Set and Data Provenance:

• Pelvic Region: 76 patients
• Spine Region: 117 patients
• Data Provenance: Retrospective clinical data from various medical sites in the US and EU. The test data was acquired at different medical sites/departments/clinical studies than the training data and was unseen.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:

The document does not explicitly state the number of experts or their qualifications for establishing the ground truth. It mentions that "the objective was to validate the quantitative accuracy of BoneMRI using rigorous, objective, and unbiased statistical tests comparing bone morphology, radiodensity, and radiodensity contrast in BoneMRI and CT images." This implies a comparison against existing CT scans as the reference.

4. Adjudication Method for the Test Set:

The document does not explicitly describe an adjudication method involving multiple experts. The validation was "voxel-by-voxel" comparing BoneMRI images to co-registered CT scans.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

A multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned or described in the provided text. The performance validation focused on the algorithm's standalone quantitative accuracy against CT images.

6. Standalone (Algorithm Only) Performance:

Yes, a standalone performance study was conducted. The "Performance Validation" section describes a "quantitative voxel-by-voxel validation of BoneMRI" where the algorithm's output (BoneMRI images) was compared directly against CT images, without human interpretation in the validation process itself.

7. Type of Ground Truth Used:

The ground truth used was co-registered Computed Tomography (CT) scans. The study aimed to compare the bone morphology, radiodensity, and radiodensity contrast generated by BoneMRI to those in CT images.

8. Sample Size for the Training Set:

The document states, "The algorithm training sets included images from multiple clinical sites, multiple anatomies, and multiple scanners." However, it does not specify the exact sample size of the training set.

9. How the Ground Truth for the Training Set Was Established:

The document states that "The algorithm training sets included images 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." It also mentions "Hounsfield Unit (HU) value" assigned to volume elements. This implies that the training data likely consisted of MRI-CT pairs where the CT scans provided the ground truth for bone morphology and radiodensity, and these were used to train the convolutional neural network to assign HU values based on MRI intensity and contextual information. The specific process of establishing ground truth for individual training cases is not detailed beyond this.

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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 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 on the clinic or hospital networks. It is available as fully on-premise software with specific GPU hardware requirements, or partly running as a managed service, for which the environment in which the managed modules run is controlled by MRIquidance, but the managed service will not receive protected health information (PHI). Within the hospital network, the application communicates with a DICOM compatible imaging archive (e.g., a PACS) to receive input MRI and to return BoneMRI images. Reading of the resulting BoneMRI images is performed using reqular DICOM compatible medical imaging viewing software.

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.

The document describes the performance testing of BoneMRI v1.6, an image processing software designed to enhance bone structures in MRI images of the pelvic and lumbar spine regions. The study aims to demonstrate that BoneMRI v1.6 meets specified accuracy acceptance criteria for bone morphology, radiodensity, and radiodensity contrast.

Here's the breakdown of the acceptance criteria and study details:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria were established for the accuracy of 3D bone morphology, radiodensity, and radiodensity contrast when compared to co-registered CT scans.

The results from the validation testing were found to fall within the pre-specified acceptance criteria (p

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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

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.

Ask a specific question about this device

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.

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.

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

Study Details

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

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