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SyMRI is a post-processing software medical device intended for use in visualization of soft tissue. SyMRI analyzes input data from MR imaging systems. SyMRI utilizes data from supported MR sequences to generate parametric maps of R1, R2 relaxation rates, and proton density (PD).

SyMRI is intended for automatic labeling, visualization and volumetric quantification of segmentable brain tissues from a set of MR images. Brain tissue volumes are determined based on modeling of parametric maps from SyMRI.

SyMRI can also generate multiple image contrasts from the parametric maps. SyMRI enables post-acquisition image contrast adjustment.

SyMRI is indicated for head imaging.

When interpreted by a trained physician, output from SyMRI can provide information useful in determining diagnosis. SyMRI 2D is intended to be used in combination with at least one other, conventional MR acquisition (e.g. T2-FLAIR). T1W and T2W images from SyMRI 3D may replace conventional MR images in a clinical setting when interpreting together with a conventional 3D T2W FLAIR image.

SyMRI works by post-processing a multi-delay, multi-echo acquisition into parametric maps. The acquisition is either a multi-slice 2D approach (M2D-MDME), consisting of 4 delays with a short and a long echo time each (8 images per slice), or a 3D approach (3D-QALAS) consisting of 4 delays with a short echo and 1 delay with a long echo time (5 images per slice).

The parametric maps are R1, R2 relaxation rates, and proton density (PD). The inverse relaxation parameters, T1 relaxation time (1/R1), and T2 relaxation time (1/R2) are also provided as parametric maps.

SyMRI also enables the users to obtain volumetric information in the head, including white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), Myelin correlated (MyC) partial volume, brain parenchyma (BP) and intracranial cavity (IC). This is accomplished by using tissue definitions based on the parametric maps. The tissue definitions provide tissue partial volume, or tissue fraction, per voxel. SyMRI also provides image processing tools to extract the values of the parametric maps, and tissue partial volume, per individual voxel, per region of interest, or the entire imaging volume.

The parametric maps can be visualized as contrast weighted MR images, such as T1, T2, PD, and Inversion Recovery (IR) weighted images (including T1-FLAIR, STIR, Double IR, and PSIR weighted images).

The parametric maps can be visualized as contrast weighted MR images from SyMRI 3D may replace conventional MR images in a clinical setting when interpreting together with a conventional 3D T2W FLAIR image.

SyMRI calculates the pixel signal intensity as a function of R1, R2, PD, and desired MR scanner settings, such as echo time (TE), repetition time (TR), and inversion delay time (TI). A number of default settings for TE, TR, and TI are provided, but the user has the ability to change the contrast of the images. SyMRI generates all the different image contrasts from the same parametric maps, derived from the same acquisition. This leads to enhanced image slice registration, owing to the absence of inter-acquisition patient movement. SyMRI provides the user the ability to change the contrast of the images after the acquisition. This is performed by adjusting the TE, TR, and/or TI parameters post-acquisition, to generate the specific contrast desired.

SyMRI is intended to be used on data produced by any of the following acquisition sequences:

• . MDME sequence data from GE MAGiC
• MDME sequence data from Philips SyntAc
• . MDME sequence data from Siemens TSE_MDME
• 3D-QALAS sequence data from Philips 3DsyntAc

This document describes the acceptance criteria and study proving the device meets them for SyMRI.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: 189 subjects
• Data Provenance:
  • Country of Origin: United States (6 institutes in the US)
  • Study Type: Prospective, multi-reader clinical investigation.
  • Subject characteristics: Patients with a wide range of different pathologies, and healthy controls. Both adults and pediatric patients were included.

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

The document does not explicitly state the number of experts used to establish a ground truth for the test set. However, it mentions that five experienced radiologists assessed the images in the clinical investigation. Their qualifications are described as "experienced radiologists."

4. Adjudication Method for the Test Set

The document does not explicitly state an adjudication method (e.g., 2+1, 3+1). It states that "Five experienced radiologists... assessed the images in two reading sessions with a four-week memory washout period in between." This suggests individual assessments rather than a consensus-based adjudication for primary readings, though further details on how discrepancies (if any) were handled are not provided.

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

• Yes, a MRMC comparative effectiveness study was done. The study involved five experienced radiologists assessing images in two reading sessions.
• Effect Size of Human Reader Improvement with AI vs. Without AI Assistance: This specific information (effect size of human readers improving with AI assistance vs. without) is not directly provided. The study focused on demonstrating the non-inferiority of synthetic 3D images (generated by the AI device, SyMRI) compared to conventional 3D images in terms of diagnostic performance (sensitivity, specificity, diagnostic accuracy). It also evaluated image quality and artifact prevalence of the synthetic images. It does not describe a scenario where human readers interpreted conventional images and then re-interpreted them with AI assistance to measure improvement. Instead, it compares the diagnostic utility of AI-generated images versus conventional images.

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone performance assessment was conducted for the quantitative aspects of the device:

• Accuracy of R1/R2/PD quantification: Evaluated compared to "gold standard inversion recovery (R1), CPMG multi-echo (R2), heavy water phantoms (PD) and standard system Model 130 NIST/ISMRM phantom." This demonstrates an algorithm-only accuracy assessment against established physical standards.
• Segmentation Accuracy & Precision: Verified against pre-defined performance criteria similar to the predicate device. While not explicitly stated as "algorithm only," the context of "verification results" for quantification and segmentation usually refers to algorithmic performance.

7. Type of Ground Truth Used

• For Quantitative Parameters (R1, R2, PD): Gold standard phantoms (inversion recovery, CPMG multi-echo, heavy water phantoms, NIST/ISMRM Model 130 phantom).
• For Diagnostic Performance (Pathology Detection, Diagnostic Accuracy, Image Quality): The consensus or individual expert assessments of the five experienced radiologists after reviewing both synthetic and conventional images served as the reference for comparison, aiming to establish non-inferiority against conventional imaging. Although not explicitly called "ground truth," the conventional images and expert interpretations thereof served as the benchmark. The study compared the device-generated synthetic images to equivalent conventional MR images. It also mentions "radiological finding class," implying expert-derived classifications.

8. Sample Size for the Training Set

The document does not provide information on the sample size used for the training set of the SyMRI algorithm. It focuses on the validation study.

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

The document does not provide information on how the ground truth for the training set was established, as it does not describe the training process for the algorithm. It primarily details the performance validation of the device.

Ask a specific question about this device

SyMRI is a post-processing software medical device intended for use in visualization of soft tissue. SyMRI analyzes input data from MR imaging systems. SyMRI utilizes data from supported MR sequences to generate parametric maps of R1, R2 relaxation rates, and proton density (PD)

SyMRI is intended for automatic labeling, visualization and volumetric quantification of segmentable brain tissues from a set of MR images. Brain tissue volumes are determined based on modeling of parametric maps from SyMRI.

When interpreted by a trained physician, the parametric maps, tissue maps, and volumetrics from SyMRI can provide information useful in determining diagnosis. SyMRI is indicated for head imaging.

SyMRI can also generate multiple contrast weighted images from the parametric maps generated by post-processing data from M2D-MDME sequence. SyMRI enables post-acquisition image contrasts adjustments from acquisition using M2D-MDME sequence.

When M2D-MDME acquisition data is used as input to SyMRI the synthetic contrast weighted images can also provide information useful in determining diagnosis. SyMRI is intended to be used in combination with at least one other, conventional MR acquisition (e.g. T2-FLAIR).

SyntheticMR's SyMRI is a Class II Magnetic resonance diagnostic device (Requlation # 892.1000) with product code LNH. The device has no components and/or accessories.

SyMRI works by post-processing a multi-delay, multi-echo acquisition into parametric maps. The acquisition is either a multi-slice 2D approach (M2D-MDME), consisting of 4 delays with a short and a long echo time each (8 images per slice), or a 3D approach (3D-QALAS) consisting of 4 delays with a short echo and 1 delay with a long echo time (5 images per slice).

The parametric maps are R1, R2 relaxation rates, and proton density (PD). The inverse relaxation parameters, T1 relaxation time (1/R1), and T2 relaxation time (1/R2) are also provided as parametric maps.

SyMRI enables the users to obtain volumetric information in the head, including white matter (WM), grey matter (GM), cerebrospinal fluid (CSF), Myelin correlated (MyC) partial volume, brain parenchyma (BP) and intracranial cavity (IC). This is accomplished by using tissue definitions based on the parametric maps. The tissue definitions provide tissue partial volume, or tissue fraction, per voxel. SyMRI also provides image processing tools to extract the values of the parametric maps, and tissue partial volume, per individual voxel, per region of interest, or the entire imaging volume.

The parametric maps can also be visualized as contrast weighted MR images, such as T1, T2, PD, and Inversion Recovery (IR) weighted images (including T1-FLAIR, T2-FLAIR, STIR, Double IR, and PSIR weighted images). SyMRI calculates the pixel signal intensity as a function of R1, R2, PD, and desired MR scanner settings, such as echo time (TE), repetition time (TR), and inversion delay time (TI). A number of default settings for TE, TR, and TI are provided, but the user has the ability to change the contrast of the images.

SyMRI generates all the different image contrasts from the same parametric maps, derived from the same acquisition. This leads to enhanced image slice registration, owing to the absence of interacquisition patient movement. SyMRI provides the user the ability to change the contrast of the images after the acquisition. This is performed by adjusting the TE, TR, and/or TI parameters postacquisition, to generate the specific contrast desired.

SyMRI is intended to be used to process data produced by any of the following acquisition sequences:

• M2D-MDME sequence data from GE MAGiC ●
• M2D-MDME sequence data from Philips SyntAc
• . M2D-MDME sequence data from Siemens TSE MDME
• 3D-QALAS sequence data from Philips 3DSyntAc Only 3T .

SyMRI can also create contrast weighted images from 3D-QALAS but these are only available in the product for quality assurance purposes as a risk mitigation related artifacts that could affect quantification and segmentation, and should not be used for clinical purposes.

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

Acceptance Criteria and Device Performance

Study Details

1. Sample sizes used for the test set and the data provenance:

   • Quantification Accuracy & Precision Test (Phantoms): The text mentions "gold standard inversion recovery (R1), CPMG multi-echo (R2), heavy water phantoms (PD) and standard system Model 130 NIST/ISMRM phantom." No specific number of phantoms or scans is provided.
   • Segmentation Precision Test (Healthy Volunteers): "healthy volunteers scanned multiple times." No exact number of subjects or scans is explicitly stated for this particular test, though 45 healthy volunteers were used for the equivalence test.
   • Volumetric Equivalence Test (3D-QALAS vs. M2D-MDME): 45 healthy volunteers.
   • Data Provenance: Not explicitly stated, but given the mention of "healthy volunteers" and "bench test," it implies a prospective study. 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 document does not mention the use of experts to establish ground truth for the test set for quantification or segmentation accuracy/precision. Ground truth for quantification was based on "gold standard" physical measurements (e.g., phantoms) and for segmentation precision, it was based on within-subject variability, which doesn't typically require human expert ground truth review in the same way.
   • For the volumetric equivalence study, the ground truth was a statistical comparison between two acquisition methods within the device itself, not against an external expert-derived ground truth.

3. Adjudication method for the test set:

   • Not applicable as the ground truth establishment did not involve multiple human experts requiring adjudication.

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 MRMC comparative effectiveness study was done, nor was there any evaluation of human reader improvement with AI assistance mentioned in the provided text. The study focuses on the technical performance of the device itself (quantification, segmentation, and equivalence between acquisition methods). The device is a "post-processing software medical device intended for use in visualization of soft tissue" and its interpretation is "When interpreted by a trained physician," implying human interpretation, but no study on the impact of the device on physician performance is detailed.

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

   • Yes, the described performance data (Quantification Accuracy & Precision, Segmentation Precision, and Volumetric Equivalence) are all standalone performance evaluations of the SyMRI algorithm. These tests assess the device's technical output (parametric maps, segmentation, volumes) directly, without involving human interpretation as an outcome measure.

6. The type of ground truth used:

   • Quantification: "Gold standard" physical measurements, specifically "inversion recovery (R1), CPMG multi-echo (R2), heavy water phantoms (PD) and standard system Model 130 NIST/ISMRM phantom." This represents a form of physical/reference standard ground truth.
   • Segmentation Precision: Repeatability (scan-rescan variability) in healthy volunteers. This is an internal consistency/precision ground truth rather than an external anatomical ground truth.
   • Volumetric Equivalence: A statistical comparison between the device's outputs from two different acquisition methods (M2D-MDME and 3D-QALAS). The M2D-MDME method effectively serves as a reference within the context of determining equivalence.

7. The sample size for the training set:

   • The document does not provide information on the training set used for the SyMRI algorithm. The performance data section focuses solely on verification and validation (testing) and does not disclose details about the development or training phase of the software.

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

   • Not provided, as information on the training set itself is absent from the text.

Ask a specific question about this device

SyMRI is a post-processing software medical device intended for use in visualization of soft tissue. SyMRI analyzes input data from MR imaging systems. SyMRI utilizes data from a multi-echo acquisition (MDME) to generate parametric maps of R1, R2 relaxation rates, and proton density (PD). SyMRI can generate multiple image contrasts from the parametric maps. SyMRI enables post-acquisition image contrast adjustment. SyMRI is indicated for head imaging.

SyMRI is also intended for automatic labeling, visualization and volumetric quantification of segmentable brain tissues from a set of MR images. Brain tissue volumes are determined based on modeling of parametric maps from MDME.

When interpreted by a trained physician, SyMRI images can provide information useful in determining diagnosis. SyMRI should always be used in combination with at least one other, conventional MR acquisition (e.g. T2-FLAIR).

SyMRI is a post-processing software medical device intended for use in visualization of soft tissue. SyMRI analyzes input data from MR imaging systems. SyMRI utilizes data from a multi-echo acquisition (MDME) to generate parametric maps of R1, R2 relaxation rates, and proton density (PD). SyMRI can generate multiple image contrasts from the parametric maps. SyMRI enables post-acquisition image contrast adjustment. SyMRI is indicated for head imaging.

SyMRI is also intended for automatic labeling, visualization and volumetric quantification of segmentable brain tissues from a set of MR images. Brain tissue volumes are determined based on modeling of parametric maps from MDME.

The provided FDA 510(k) clearance letter and Indications for Use document do not contain the detailed information required to describe the acceptance criteria and the study that proves the device meets them. This type of information is typically found in the technical documentation submitted with the 510(k) application, such as validation studies, performance testing reports, and clinical trial summaries, which are not included in the provided text.

Specifically, the document focuses on the regulatory clearance process rather than the technical details of the device's performance validation. Therefore, I cannot extract the following information from the provided text:

1. A table of acceptance criteria and the reported device performance: This would be in a performance study report.
2. Sample size used for the test set and the data provenance: This would be detailed in the methodology section of a validation study.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: This information is specific to how a test set's ground truth was determined in a clinical or validation study.
4. Adjudication method for the test set: Similarly, this relates to expert review processes in a study.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and its effect size: This would be a specific study design and its results.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: This pertains to the type of performance evaluation conducted.
7. The type of ground truth used: This would be explicitly stated in the study's methods.
8. The sample size for the training set: This refers to the development phase of the algorithm, not typically found in the regulatory clearance document.
9. How the ground truth for the training set was established: This is also part of the algorithm development documentation.

The provided text only states that SyMRI "analyzes input data from MR imaging systems" to "generate parametric maps of R1, R2 relaxation rates, and proton density (PD)" and "can generate multiple image contrasts from the parametric maps." It also mentions its use for "automatic labeling, visualization and volumetric quantification of segmentable brain tissues from a set of MR images." It emphasizes that "SyMRI should always be used in combination with at least one other, conventional MR acquisition (e.g. T2-FLAIR)" and that "SyMRI images can provide information useful in determining diagnosis" when "interpreted by a trained physician."

These are descriptions of the device's functions and intended use, not performance metrics, acceptance criteria, or study details.

To answer your request, I would need access to the actual validation studies or performance testing reports submitted by SyntheticMR AB for their SyMRI device, which are not present in the provided FDA communication.

Ask a specific question about this device

SyMRI is a post-processing software medical device intended for use in visualization of the brain. SyMRI analyzes input data from MR imaging systems. SyMRI utilizes data from a multi-echo acquisition (MDME) to generate parametric maps of R1, R2 relaxation rates, and proton density (PD). SyMRI can generate multiple image contrasts from the parametric maps. SyMRI enables post-acquisition image contrast adjustment. SyMRI is indicated for head imaging.

SyMRI is also intended for automatic labeling, visualization and volumetric quantification of segmentable brain tissues from a set of MR images. Brain tissue volumes are determined based on modeling of parametric maps from MDME.

When interpreted by a trained physician, SyMRI images can provide information useful in determining diagnosis. SyMRI should always be used in combination with at least one other, conventional MR acquisition (e.g. T2-FLAIR).

SyMRI is a post-processing software medical device intended for use in visualization of the brain. SyMRI analyzes input data from MR imaging systems. SyMRI utilizes data from a multi-echo acquisition (MDME) to generate parametric maps of R1, R2 relaxation rates, and proton density (PD). SyMRI can generate multiple image contrasts from the parametric maps. SyMRI enables post-acquisition image contrast adjustment. SyMRI is indicated for head imaging.

SyMRI is also intended for automatic labeling, visualization and volumetric quantification of segmentable brain tissues from a set of MR images. Brain tissue volumes are determined based on modeling of parametric maps from MDME.

This document is an FDA 510(k) clearance letter for the SyMRI device. It provides information about the device's indications for use but does not contain details about specific acceptance criteria, device performance studies, or the methodologies used to establish ground truth or conduct multi-reader studies.

Therefore, I cannot provide the requested information based solely on the provided text. The provided text is a regulatory clearance document, not a clinical study report or a detailed performance validation report.

To answer your questions accurately, I would need a clinical testing report or a similar technical document that details the device's validation studies.

Based on the provided text, I can only extract the following:

Device Name: SyMRI
Regulation Number: 21 CFR 892.1000
Regulation Name: Magnetic resonance diagnostic device
Regulatory Class: Class II

Indications for Use:

• Post-processing software medical device for visualization of the brain.
• Analyzes input data from MR imaging systems.
• Utilizes data from a multi-echo acquisition (MDME) to generate parametric maps of R1, R2 relaxation rates, and proton density (PD).
• Can generate multiple image contrasts from the parametric maps.
• Enables post-acquisition image contrast adjustment.
• Indicated for head imaging.
• Intended for automatic labeling, visualization, and volumetric quantification of segmentable brain tissues from a set of MR images.
• Brain tissue volumes are determined based on modeling of parametric maps from MDME.
• When interpreted by a trained physician, SyMRI images can provide information useful in determining diagnosis.
• SyMRI should always be used in combination with at least one other, conventional MR acquisition (e.g., T2-FLAIR).

I cannot answer the following questions from the provided text:

1. A table of acceptance criteria and the reported device performance: This information is not in the FDA clearance letter.
2. Sample sized used for the test set and the data provenance: Not provided.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not provided.
4. Adjudication method for the test set: Not provided.
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: Not provided.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not provided.
7. The type of ground truth used: Not explicitly stated (though it references "trained physician interpretation").
8. The sample size for the training set: Not provided.
9. How the ground truth for the training set was established: Not provided.

Ask a specific question about this device

SyMRI is a post-processing software medical device intended for use in visualization of the brain. SyMRI analyzes input data from MR imaging systems. SyMRI utilizes data from a multi-echo acquisition (MDME) to generate parametric maps of R1, R2 relaxation rates, and proton density (PD). SyMRI can generate multiple image contrasts from the parametric maps. SyMRI enables post-acquisition image contrast adjustment. SyMRI is indicated for head imaging.

SyMRI is also intended for automatic labeling, visualization and volumetric quantification of segmentable brain tissues from a set of MR images. Brain tissue volumes are determined based on modeling of parametric maps from MDME.

When interpreted by a trained physician, SyMRI images can provide information useful in determining diagnosis. SyMRI should always be used in combination with at least one other, conventional MR acquisition (e.g. T2-FLAIR).

SyMRI is a post-processing software medical device intended for use in visualization of the brain. SyMRI analyzes input data from MR imaging systems. SyMRI utilizes data from a multi-echo acquisition (MDME) to generate parametric maps of R1, R2 relaxation rates, and proton density (PD). SyMRI can generate multiple image contrasts from the parametric maps. SyMRI enables post-acquisition image contrast adjustment. SyMRI is indicated for head imaging.

SyMRI is also intended for automatic labeling, visualization and volumetric quantification of segmentable brain tissues from a set of MR images. Brain tissue volumes are determined based on modeling of parametric maps from MDME.

This FDA 510(k) clearance letter for the SyMRI device ([K191036](https://510k.innolitics.com/search/K191036)) does not contain the detailed information necessary to answer all the questions about acceptance criteria and the study that proves the device meets them. The document primarily confirms the substantial equivalence of SyMRI to a predicate device and outlines regulatory compliance. It does not include specific performance data or study methodology.

Therefore, many sections below will be marked as "Not provided in the document."

Here's an attempt to answer the questions based only on the provided text:

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

This information is Not provided in the document. FDA 510(k) letters typically do not contain the detailed performance specifications or the results of the studies. They acknowledge that such studies were performed to demonstrate substantial equivalence but do not present the raw data or acceptance criteria.

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

This information is Not provided in the document.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

This information is Not provided in the document.

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

This information is Not provided in the document.

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

This information is Not provided in the document. The Indications for Use state, "When interpreted by a trained physician, SyMRI images can provide information useful in determining diagnosis. SyMRI should always be used in combination with at least one other, conventional MR acquisition (e.g. T2-FLAIR)." This suggests a human-in-the-loop scenario, but no MRMC study or effect size is detailed.

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

The Indications for Use imply that SyMRI is not a standalone device for diagnosis, as it "should always be used in combination with at least one other, conventional MR acquisition" and its images are useful "When interpreted by a trained physician". Therefore, a standalone performance study is unlikely to be the primary basis for its clearance for diagnostic purposes. However, the document does not explicitly state whether a standalone study was performed for other aspects (e.g., image generation or volumetric quantification accuracy).

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

This information is Not provided in the document.

8. The sample size for the training set

This information is Not provided in the document.

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

This information is Not provided in the document.

Ask a specific question about this device

SyMRI is a post-processing software medical device intended for use in visualization of the brain. SyMRI analyzes input data from MR imaging systems. SyMRI utilizes data from a multi-delay, multi-echo acquisition (MDME) to generate parametric maps of R1, R2 relaxation rates, and proton density (PD). SyMRI can generate multiple image contrasts from the parametric maps. SyMRI enables post-acquisition image contrast SyMRI is indicated for head imaging.

SyMRI is also intended for automatic labeling, visualization and volumetric quantification of segmentable brain tissues from a set of MR images. Brain tissue volumes are determined based on modeling of parametric maps from MDME.

When interpreted by a trained physician, SyMRI images can provide information useful in determining diagnosis. SyMRI should always be used in combination with at least one other, conventional MR acquisition (e.g. T2-FLAIR),

SyMRI is a post-processing software medical device intended for use in visualization of the brain. SyMRI analyzes input data from MR imaging systems. SyMRI utilizes data from a multi-delay, multi-echo acquisition (MDME) to generate parametric maps of R1, R2 relaxation rates, and proton density (PD). SyMRI can generate multiple image contrasts from the parametric maps. SyMRI enables post-acquisition image contrast SyMRI is indicated for head imaging.

SyMRI is also intended for automatic labeling, visualization and volumetric quantification of segmentable brain tissues from a set of MR images. Brain tissue volumes are determined based on modeling of parametric maps from MDME.

The provided text is a 510(k) clearance letter from the FDA for a device named SyMRI. It describes the device's indications for use and classification but does not contain any information about the acceptance criteria or the study that proves the device meets those criteria.

Therefore, I cannot fulfill your request for the specific details regarding:

1. A table of acceptance criteria and the reported device performance
2. Sample sizes and data provenance
3. Number and qualifications of experts for ground truth
4. Adjudication method
5. MRMC comparative effectiveness study and effect size
6. Standalone performance
7. Type of ground truth used
8. Training set sample size
9. Ground truth establishment for the training set

The document primarily focuses on the regulatory clearance of the SyMRI device based on its substantial equivalence to predicate devices, rather than detailing the performance studies conducted to establish that equivalence.

Ask a specific question about this device

SyMRI is a post-processing software medical device intended for use in visualization of the brain. SyMRI analyzes input data from MR imaging systems. SyMRI utilizes data from a multi-echo acquisition (MDME) to generate parametric maps of R1, R2 relaxation rates, and proton density (PD). SyMRI can generate multiple image contrasts from the parametric maps. SyMRI enables post-acquisition image contrast SyMRI is indicated for head imaging.

SyMRI is also intended for automatic labeling, visualization and volumetric quantification of segmentable brain tissues from a set of MR images. Brain tissue volumes are determined based on modeling of parametric maps from MDME images.

When interpreted by a trained physician, SyMRI images can provide information useful in determining diagnosis. SyMRI should always be used in combination with at least one other, conventional MR acquisition (e.g. T2-FLAIR).

SyMRI is a post-processing software medical device intended for use in visualization of the brain. SyMRI analyzes input data from MR imaging systems. SyMRI utilizes data from a multi-echo acquisition (MDME) to generate parametric maps of R1, R2 relaxation rates, and proton density (PD). SyMRI can generate multiple image contrasts from the parametric maps. SyMRI enables post-acquisition image contrast SyMRI is indicated for head imaging.

SyMRI is also intended for automatic labeling, visualization and volumetric quantification of segmentable brain tissues from a set of MR images. Brain tissue volumes are determined based on modeling of parametric maps from MDME images.

The provided text from the FDA 510(k) clearance letter for SyMRI does not contain the detailed study information required to answer all parts of your request. This document is a clearance letter, not a clinical study report. It confirms the device's substantial equivalence to a predicate device but typically does not include the granular details of the validation studies (like specific acceptance criteria, sample sizes for test sets, expert qualifications for ground truth, or MRMC study results).

However, I can extract and infer some information based on the typical content of such submissions and the scope of the device described.

Based on the provided text, here's what can and cannot be answered:

Information NOT available in the provided text:

• A table of acceptance criteria and the reported device performance: This document does not detail specific acceptance criteria or quantitative performance metrics.
• Sample sized used for the test set and the data provenance: No information on test sample size or data origin (country, retrospective/prospective).
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not specified.
• Adjudication method: Not specified.
• 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: Not mentioned.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not explicitly stated, though the nature of the device (post-processing software) implies elements of standalone processing.
• The type of ground truth used: Not explicitly stated (e.g., expert consensus vs. pathology).
• The sample size for the training set: Not specified.
• How the ground truth for the training set was established: Not specified.

Information that can be inferred or partially addressed from the provided text:

While the document doesn't explicitly state acceptance criteria in a table format, the core "acceptance" for 510(k) clearance is substantial equivalence to a predicate device. This means the new device is as safe and effective as a legally marketed device. The performance data submitted to demonstrate this equivalence would typically include validation of the features described.

Here's an attempt to structure an answer, acknowledging the limitations of the provided text:

Description of Device Acceptance Criteria and Supporting Study (Based on Inference and Typical 510(k) Scope)

Device Name: SyMRI
510(k) Number: K173558
Device Type: Post-processing software medical device for visualization of the brain, generation of parametric maps (R1, R2, PD), multiple image contrasts, and automatic labeling/volumetric quantification of brain tissues.

General Acceptance Premise (for 510(k) Clearance):
The primary acceptance criterion for 510(k) marketing clearance is demonstrating substantial equivalence to a predicate device in terms of safety and effectiveness. For a software device like SyMRI, this typically involves validating that its outputs (parametric maps, derived contrasts, tissue segmentations, and volume quantifications) are accurate and reliable, and that its use does not introduce new safety concerns compared to the predicate.

Given the device's stated functions, the studies would likely have aimed to demonstrate:

• Accuracy of Parametric Maps (R1, R2, PD): The generated relaxation rates and proton density values align with expected values from the MR physics and potentially a "gold standard" or highly controlled measurements.
• Fidelity of Derived Image Contrasts: The synthetic images generated (e.g., T1, T2, FLAIR-like) are diagnostically equivalent to conventionally acquired images of the same type.
• Accuracy of Brain Tissue Segmentation and Volumetric Quantification: The software correctly identifies and quantifies different brain tissues (e.g., white matter, gray matter, CSF) and that these measurements are consistent and reproducible.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Sizes Used for the Test Set and Data Provenance

• Sample Size: Not specified in the provided document.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Number of Experts: Not specified.
• Qualifications: "When interpreted by a trained physician" is mentioned in the Indications for Use, implying that the ground truth for clinical interpretation or validation would involve qualified medical professionals, most likely radiologists given the nature of MR imaging. Specific experience levels are not mentioned.

4. Adjudication Method for the Test Set

• Not specified.

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

• Not specified if an MRMC study was performed. The letter states the device "can provide information useful in determining diagnosis" and "should always be used in combination with at least one other, conventional MR acquisition." This phrasing suggests a component of human interpretation is always involved, but it doesn't detail a comparative study setup.
• Effect Size: Not applicable as the conduct of an MRMC study is not confirmed or detailed.

6. Standalone (Algorithm Only) Performance

• The device is "post-processing software" that "analyzes input data" and "generates parametric maps" and "automatic labeling, visualization and volumetric quantification." This describes the algorithm's standalone function in processing data and producing outputs. The performance of these standalone functionalities (e.g., accuracy of parameter estimation, segmentation accuracy) would have been evaluated, though specific metrics are not in this letter.
• The "Indications for Use" clearly states, "SyMRI should always be used in combination with at least one other, conventional MR acquisition (e.g. T2-FLAIR)" and "When interpreted by a trained physician." This indicates that while the algorithm has standalone processing capabilities, its clinical use is intended to be human-in-the-loop and complementary to other imaging.

7. Type of Ground Truth Used

• Not explicitly stated. For parametric map validation, a "gold standard" might be phantom data or highly controlled reference scans. For segmentation and volumetric quantification, ground truth would typically be established by:
  • Expert Consensus: Manual segmentation by one or multiple expert readers (e.g., radiologists, neuroanatomists), potentially with arbitration.
  • Pathology/Histology: Less likely for general brain imaging, but possible for specific validation of tissue characteristics if biopsies were involved (highly unlikely for a general imaging device).
  • Outcomes Data: Not typically the primary ground truth for basic image processing accuracy, but could be part of broader clinical utility studies.

8. Sample Size for the Training Set

• Not specified.

9. How Ground Truth for the Training Set Was Established

• Not specified. Assuming a machine learning component (common for "automatic labeling" and "volumetric quantification"), the ground truth for training would likely be established in a similar manner to the test set ground truth (e.g., manual expert annotations), performed on a distinct dataset.

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SyMRI is a post-processing software medical device intended for use in visualization of the brain. SyMRI analyzes input data from MR imaging systems. SyMRI utilizes data from a multi-delay, multi-echo acquisition (MDME) to generate parametric maps of R1, R2 relaxation rates, and proton density (PD). SyMRI can generate multiple image contrasts from the parametric maps. SyMRI enables post-acquisition image contrast adjustment. SyMRI is indicated for head imaging. When interpreted by a trained physician, SyMRI images can provide information useful in determining diagnosis. SyMRI should always be used in combination with at least one other, conventional MR acquisition (e.g. T2-FLAIR).

SyMRI allows the user to generate multiple image contrasts from a single acquisition scan. This is accomplished by post-processing a multi-delay, multi-echo acquisition (MDME) into parametric maps. The parametric maps are R1, R2 relaxation rates, and proton density (PD). The inverse relaxation parameters, T1 relaxation time (1/R1), and T2 relaxation time (1/R2) are also provided as parametric maps. The parametric maps can be visualized as contrast weighted MR images, such as T1, T2, PD, and Inversion Recovery (IR) weighted images (including T1-FLAIR, T2-FLAIR, STIR, Double IR, and PSIR weighted images). SyMRI calculates the pixel signal intensity as a function of R1, R2, PD, and desired MR scanner settings, such as echo time (TE), repetition time (TR), and inversion delay time (TI). A number of default settings for TE. TR, and TI are provided, but the user has the ability to change the contrast of the images. SyMRI generates all the different image contrasts from the same parametric maps, derived from the same acquisition. This leads to enhanced image slice registration, owing to the absence of interacquisition patient movement. SyMRI provides the user the ability to change the contrast of the images after the acquisition. This is performed by adjusting the TE, TR, and/or TI parameters post-acquisition, to generate the specific contrast desired.

SyMRI also provides image processing tools to extract the values of the parametric maps per individual pixel, per region of interest, or the entire imaging volume.

SyMRI is intended to be used on MDME sequence data from GE MAGiC.

The provided text describes the SyMRI device and its comparison to the predicate device MAGiC, but it does not contain a detailed study with acceptance criteria and reported device performance metrics in the format requested. Instead, it makes a general statement about substantial equivalence based on the algorithm and image quality.

Therefore, many of the requested fields cannot be directly extracted from the provided text. I will fill in what can be inferred or explicitly stated, and note when information is missing.

Here's the breakdown of the information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document states that "Additional phantom head to head comparison of R1, R2 and PD parametric maps, which included one contrast of each major synthetic image (T1w, T2w, T2 FLAIR), were performed to compare SyMRI to MAGiC. There was no difference between SyMRI and MAGiC." This implies the acceptance criterion was "no difference" compared to the predicate device, but specific quantitative metrics are not provided.

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

• Sample Size: Not explicitly stated. The text mentions a "phantom head to head comparison," implying phantom data, but the number of phantoms or images is not specified.
• Data Provenance: Phantom data (implied). No country of origin is mentioned. The study appears to be a comparative study rather than a retrospective or prospective clinical study on patient data for validation criteria described.

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

Not applicable/Not mentioned. The comparison was primarily a technical, quantitative comparison of parametric maps and image modalities between two algorithms, not an assessment by human experts against ground truth.

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

Not applicable/Not mentioned. No human adjudication process is described.

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 as having been performed. The comparison was directly between the SyMRI algorithm and the MAGiC algorithm using phantom data.

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

Yes, a standalone performance comparison was performed. The comparison was of the SyMRI algorithm directly against the MAGiC algorithm for generating parametric maps and synthetic images.

7. The type of ground truth used

The implicit "ground truth" used for comparison was the output of the predicate device, MAGiC. For the specific phantom study mentioned, the "ground truth" was that the parametric maps and synthetic images generated by SyMRI should be indistinguishable from those generated by MAGiC.

8. The sample size for the training set

Not applicable/Not mentioned. The document describes a post-processing software ("SyMRI and MAGiC are the same algorithm for post processing"). It is not a machine learning model in the sense of requiring a "training set" in the context of deep learning. It's an algorithm that generates parametric maps and synthetic images from input MR data.

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

Not applicable/Not mentioned for the reasons stated above.

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