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HyperSense is a software feature intended for use on GE MR 1.5T and 3.0T Systems. HyperSense is an acceleration technique based on sparse data sampling and iterative reconstruction that allows users to reduce scan times or increase scan resolution. HyperSense can be used for non-contrast enhanced imaging of the head, neck, spine, extremities, pelvis, and abdomen.

HyperSense is a software feature used on GE 1.5T and 3.0T MR systems. HyperSense is an acceleration technique based on sparse data sampling and iterative reconstruction, enabling faster imaging without the penalties commonly found with conventional parallel imaging. HyperSense is intended to be used with volumetric acquisitions, and can be combined with other methods of acceleration (ARC) for achieving high signal to noise ratio with shorter acquisition times. HyperSense can deliver higher spatial resolution images or reduced scan times.

HyperSense Device Acceptance Criteria and Study Details

The provided document describes the GE HyperSense, a software feature for MR systems that uses sparse data sampling and iterative reconstruction to reduce scan times or increase resolution.

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

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document states that "the non-clinical testing was completed with passing results per the pass/fail criteria defined in the test cases" for SNR and spatial resolution. For clinical testing, it states "The clinical results demonstrated that HyperSense maintains comparable imaging performance results as its predicate devices."

2. Sample Size for Test Set and Data Provenance

• Sample Size for Test Set: Not explicitly stated for either phantom or clinical studies.
• Data Provenance: Not explicitly stated for the clinical study. It mentions "sample clinical images are included in this submission," suggesting the data was likely from relevant GE MR systems. The study is referred to as "A clinical study has been performed," implying a prospective nature, though not explicitly stated.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not explicitly stated.
• Qualifications of Experts: Not explicitly stated.

4. Adjudication Method

• Adjudication Method: Not explicitly stated.

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

• MRMC Study: No, an MRMC comparative effectiveness study is not mentioned. The clinical study focused on evaluating the impact of HyperSense on image quality compared to its predicate devices, not on human reader improvement with AI assistance.

6. Standalone Performance Study

• Standalone Study: Yes, a standalone performance study in the form of phantom testing was conducted to evaluate the impact of HyperSense on Signal to Noise Ratio (SNR) and spatial resolution. The "clinical study" also appears to be a standalone assessment of the device's image quality. The device is a "software only feature" that enhances image acquisition and reconstruction, implying its direct impact on the image itself, which is a standalone performance by the algorithm.

7. Type of Ground Truth Used

• Phantom Testing: Objective measurements of SNR and spatial resolution.
• Clinical Study: "Legibility of morphological features," which implies a qualitative assessment by experts (implicitly radiologists or medical professionals experienced with MRI interpretations). This would typically involve expert consensus or assessment against established diagnostic criteria. The document also mentions a "peer reviewed journal article describing a study of the HyperSense technique as supporting evidence," which would also likely rely on similar ground truth methodologies.

8. Sample Size for Training Set

• Sample Size for Training Set: Not mentioned. HyperSense is an acceleration technique and iterative reconstruction method. While it's a software feature, the process of developing and tuning such algorithms often involves various datasets for training and validation, but these details are not provided in the summary.

9. How Ground Truth for Training Set Was Established

• How Ground Truth for Training Set was Established: Not mentioned. Given the nature of an iterative reconstruction algorithm, "ground truth" for training would typically involve high-quality, fully sampled MR images that the algorithm aims to replicate or improve upon from undersampled data. The process would likely involve various forms of quantitative image metrics and expert review to optimize the reconstruction parameters.

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The SIGNA Pioneer is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan times.

It is indicated for use as a diagnostic imaging device to produce axial, sagittal, coronal, and oblique images, spectroscopic images, parametric maps, and/or spectra, dynamic images of the structures of the entire body, including, but not limited to, head, neck, TMJ, spine, breast, heart, abdomen, pelvis, joints, prostate, blood vessels, and musculoskeletal regions of the body.

Depending on the region of interest being imaged, contrast agents may be used.

The images produced by the SIGNA Pioneer reflect the spatial distribution or molecular environment of nuclei exhibiting magnetic resonance. These images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

The SIGNA Pioneer features a 3.0T superconducting magnet with a 70cm bore size. The RF receiver is equipped with 97 RF channels. The data acquisition system accommodates 32 channels for image reconstruction simultaneously. The system uses a combination of time-varying magnetic fields (gradients) and RF transmissions to obtain information regarding the density and position of nuclei exhibiting magnetic resonance. The system can image in the sagittal, coronal, axial, oblique, and double oblique planes, using various pulse sequences and reconstruction algorithms. The SIGNA Pioneer uses multi-drive RF transmit for imaging of the head and body regions. The SIGNA Pioneer is designed to conform to NEMA DICOM standards.

The provided document is a 510(k) summary for the GE Healthcare SIGNA Pioneer Magnetic Resonance Diagnostic Device. It states that the device has been found substantially equivalent to a predicate device (Discovery MR750w 3.0T, K142085). The summary primarily focuses on affirming that the SIGNA Pioneer performs equivalently to the predicate device and meets established safety standards rather than establishing new acceptance criteria for an AI/algorithm-driven device.

Therefore, the information requested in the prompt, which is typically relevant for studies evaluating the performance of AI/algorithm-driven devices against specific acceptance criteria, is largely not present in this document. This document describes a traditional medical device (an MRI scanner) and its substantial equivalence to another MRI scanner, not a standalone AI diagnostic software.

However, I can extract the relevant information that is present and identify what is missing based on your questions.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present specific "acceptance criteria" in the format of a table with numerical thresholds for performance metrics for an AI/algorithm. Instead, it states that the device was verified to meet safety criteria and demonstrated acceptable diagnostic imaging performance, which is "substantially equivalent" to the predicate device.

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

• Test Set Sample Size: Not specified. The document only mentions "clinical images and clinical results summary" were used, but no numbers are provided for cases or subjects.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective).

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

• Number of Experts: Not specified.
• Qualifications: The document states that "images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis." This implies physicians were involved in interpreting clinical data, but their number and specific qualifications (e.g., years of experience, subspecialty) are not detailed.

4. Adjudication Method for the Test Set

• Not specified.

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

• Was it done?: No, a traditional MRMC comparative effectiveness study as typically understood for AI-assisted reading was not performed or described. The comparison is between the SIGNA Pioneer MRI device and a predicate MRI device, focusing on substantial equivalence in overall performance and safety, not on how an AI improves human reader performance.
• Effect size of AI improvement: Not applicable, as this was not an AI assistance study.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Was it done?: No. This document describes an MRI scanning device, which produces images for physician interpretation. It is not an algorithm that performs a diagnosis in a standalone manner. The device's "performance" refers to the quality of the images it produces and its adherence to safety standards.

7. Type of Ground Truth Used

• The document implies that "clinical images and clinical results" were evaluated, likely against the interpretations of "trained physicians" (expert consensus based on clinical findings) for diagnostic imaging performance. However, specific methodologies for establishing ground truth (e.g., pathology, long-term outcomes) are not detailed.

8. Sample Size for the Training Set

• Not applicable/Not specified. This device is a hardware scanner, not a machine learning algorithm that requires a training set in the conventional sense for its primary function. While some integrated software features might have been developed using data, the document does not distinguish or describe a "training set" for the fundamental device performance.

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

• Not applicable/Not specified for the reasons stated above.

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1.5T SIGNA Creator and 1.5T SIGNA Explorer is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan times. It is indicated for use as a diagnostic imaging device to produce axial, sagittal, coronal, and oblique images, spectroscopic images, parametric maps, and/or spectra, dynamic images of the structures and/or functions of the entire body, including, but not limited to, head, neck, TMJ, spine, breast, heart, abdomen, pelvis, joints, prostate, blood vessels, and musculoskeletal regions of the body. Depending on the region of interest being imaged, contrast agents may be used.
The images produced by 1.5T SIGNA Creator and 1.5T SIGNA Explorer reflects the spatial distribution or molecular environment of nuclei exhibiting magnetic resonance. These images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

1.5T SIGNA Creator and 1.5T SIGNA Explorer is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan times. The 1.5T SIGNA Creator and 1.5T SIGNA Explorer features a superconducting magnet operating at 1.5 Tesla. The system uses a combination of time-varying magnetic fields (gradients) and RF transmissions to obtain information regarding the density and position of nuclei exhibiting magnetic resonance. The data acquisition system accommodates 16 independent receive channels and multiple independent coil elements per channel during a single acquisition series.

Here's an analysis of the provided text regarding the acceptance criteria and study for the 1.5T SIGNA Creator and 1.5T SIGNA Explorer:

The provided document is a 510(k) Premarket Notification Summary from the FDA for GE Healthcare's 1.5T SIGNA Creator and 1.5T SIGNA Explorer Magnetic Resonance Imaging (MRI) systems. The primary purpose of a 510(k) submission is to demonstrate substantial equivalence to a legally marketed predicate device, rather than proving performance against specific acceptance criteria in a detailed clinical study for a novel device.

Therefore, the information you're requesting regarding explicit acceptance criteria and a dedicated study to prove precise performance metrics is largely not present in this type of regulatory document. Instead, the document focuses on demonstrating that the new device meets the same safety and effectiveness standards as its predicate.

Here's a breakdown of the information that can be extracted and what is not available based on your request:

1. Table of Acceptance Criteria and Reported Device Performance

Not explicitly provided in this document.

This 510(k) submission does not include a table of quantitative acceptance criteria (e.g., specific sensitivity, specificity, accuracy thresholds for a diagnostic task) and corresponding performance metrics for the 1.5T SIGNA Creator and 1.5T SIGNA Explorer. The acceptance criteria for a 510(k) are generally around demonstrating that the new device is as safe and effective as the predicate device(s).

The document states:

• "The subject of this premarket submission, 1.5T SIGNA Creator and 1.5T SIGNA Explorer did not require clinical studies to support substantial equivalence."
• "Internal scans were conducted as part of validation for workflow and image quality, and sample clinical images are included in the submission."
• "Additionally, the result of the above described testing demonstrates that the device performs as intended."
• "GE Healthcare considers the 1.5T SIGNA Creator and 1.5T SIGNA Explorer to be as safe, as effective, and performance is substantially equivalent to the predicate device(s)."

These statements highlight that the absence of significant differences and the compliance with established standards are the "acceptance criteria" for substantial equivalence.

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

Not applicable/available as a formal "test set" for performance evaluation.

Since no dedicated clinical study was performed to assess diagnostic performance against specific acceptance criteria, there is no "test set" in the traditional sense for diagnostic accuracy. The document mentions "internal scans" as part of validation, but does not specify sample size or data provenance for these (e.g., country of origin, retrospective/prospective). These internal scans would likely be used to evaluate image quality and workflow, not necessarily diagnostic performance against a ground truth.

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

Not applicable.

Because there was no diagnostic performance study with a formal "test set," there's no mention of experts establishing ground truth for such a study.

4. Adjudication Method for the Test Set

Not applicable.

No formal test set or diagnostic performance study means no adjudication method is mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a MRMC comparative effectiveness study was not done.

The document explicitly states: "The subject of this premarket submission, 1.5T SIGNA Creator and 1.5T SIGNA Explorer did not require clinical studies to support substantial equivalence." This implies that no MRMC study or any other clinical study was deemed necessary to demonstrate the device's functionality beyond substantial equivalence to the predicate. Therefore, no effect size of human reader improvement with/without AI assistance is applicable, as this device is an MRI scanner, not an AI-assisted diagnostic tool.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Not applicable.

This is an MRI hardware device, not a diagnostic algorithm. Therefore, "standalone" algorithm performance is not relevant.

7. The Type of Ground Truth Used

Not applicable.

Without a diagnostic performance study, the concept of "ground truth" for diagnostic accuracy is not discussed in this document. The "ground truth" underpinning this submission is fundamentally that the new device is technologically equivalent and operates within the same safety and performance parameters as already-approved predicate devices.

8. The Sample Size for the Training Set

Not applicable.

This is not an AI/algorithm-driven device requiring a training set in the machine learning sense.

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

Not applicable.

As it's not an AI/algorithm-driven device, there is no training set or associated ground truth establishment process.

Summary of what the document does provide regarding "performance" and "acceptance criteria":

• Reliance on Substantial Equivalence: The core "acceptance criterion" for this 510(k) approval is demonstrating substantial equivalence to predicate devices (1.5T Brivo MR355 and 1.5T Optima MR360, K123417; and 1.5T Optima MR450w, K142085).
• Technological Equivalence: The document states, "Proposed 1.5T SIGNA Creator and 1.5T SIGNA Explorer Technology employs the same fundamental scientific technology as its predicate device..."
• Compliance with Voluntary and Recognized Standards: The device underwent testing to comply with various standards, which serve as performance benchmarks in lieu of specific clinical performance criteria:
  • IEC60601-1, IEC60601-2-33, IEC60601-1-1, IEC60601-1-2 (Electrical safety and performance for medical devices, specifically MRI)
  • NEMA MS1, MS2, MS3, MS4, MS5, MS8 (Performance standards for MRI systems, defining terms, measurements, and reporting for aspects like image quality, S/N, geometric distortion, etc.)
  • NEMA PS PS3.1-3.20 (DICOM standard for communication interface)
• Quality Assurance Measures: The device applied standard quality assurance measures, including Risk Analysis, Requirements Reviews, Design Reviews, Testing on unit level (Module verification), Integration testing (System verification), Performance testing (Verification), Safety testing (Verification), and Simulated use testing (Validation). These indicate that the device meets internal design and quality standards.
• Internal Scans for Validation: "Internal scans were conducted as part of validation for workflow and image quality." While not a formal clinical study, these would demonstrate that the system produces images and operates as expected.

In conclusion, for a 510(k) submission regarding an MRI system like this, the "acceptance criteria" are tied to demonstrating that the device is as safe and effective as a previously approved predicate device, primarily through technological comparison, compliance with recognized performance standards, and internal validation of image quality and workflow, rather than detailed clinical performance metrics from controlled diagnostic studies.

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