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The Discovery MR750 3.0T system 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, TMI, 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 Discovery MR750 3.0Tsystem 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 Discovery MR750 3.0T features a superconducting magnet operating at 3.0 Tesla. The data acquisition system accommodates up to 32 independent receive channels in various increments, and multiple independent coil elements per channel during a single acquisition series. The system uses a combination of time-varying magnetic fields (gradients) and RF transmissions to obtain information regarding the density and position of elements 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 Discovery MR750 3.0T uses multi-drive RF transmit for imaging of the head and body regions.

Here's a breakdown of the acceptance criteria and study information for the GE Discovery MR750 3.0T, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document states: "Internal scans were conducted as part of validation for workflow and image quality for the addition of the new feature."

• Sample Size: Not explicitly stated. The term "internal scans" typically implies a smaller, controlled set of images used for engineering validation rather than a large clinical trial.
• Data Provenance: "Internal scans" suggests the data was generated internally by GE Healthcare, likely on their own test subjects or phantoms. There is no mention of country of origin or whether it was retrospective or prospective in a clinical sense; it appears to be primarily technical validation.

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

This information is not provided in the document. The study described focuses on technical and imaging performance comparison to a predicate, rather than an assessment requiring expert ground truth in a diagnostic context.

4. Adjudication Method for the Test Set

This information is not provided in the document. Given the nature of the "internal scans" for workflow and image quality validation, a formal adjudication process as seen in clinical trials with multiple expert readers is unlikely to have been employed.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The study described is a technical and imaging performance comparison, not an AI-assisted diagnostic study. Therefore, there is no information on human reader improvement with or without AI assistance.

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

The device itself is a Magnetic Resonance Diagnostic Device, not an AI algorithm. The tests performed were on the imaging system's performance, not a standalone algorithm.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

The document implies that the ground truth for performance was the performance of the predicate device (K132376). The goal was to demonstrate that the new device "maintains the same imaging performance results as its predicate device." This suggests a comparative "ground truth" rather than an independent gold standard like pathology. For technical performance, compliance with standards (IEC, NEMA) would also serve as a form of "ground truth."

8. The Sample Size for the Training Set

There is no mention of a training set. This type of device (a magnetic resonance scanner) does not typically involve a machine learning training set in the way an AI diagnostic algorithm would. The development involves engineering and software updates, and validation against established technical standards and predicate performance.

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

As there is no mention of a training set, this information is not applicable/not provided.

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The SIGNA PET/MR system combines magnetic resonance diagnostic devices (MRDD) and Positron Emission Tomography (PET) scanners that provide registration and fusion of high resolution physiologic and anatomic information, acquired simultaneously and isocentrically. The combined system maintains independent functionality of the MR and PET devices, allowing for single modality MR and / or PET imaging. These systems are intended to be utilized by appropriately trained health care professionals to aid in the detection, and diagnosis of diseases and disorders. MR is intended to produce transverse, sagittal, coronal and oblique cross-sectional MR images and/ or spectra, and displays the internal struction of the human body. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, approved contrast agents may be used, as described in their labeling. This system may also be used for imaging during interventional procedures when performed with MR compatible devices, such as MR safe biopsy needles. PET images and measures the distribution of PET radiopharmaceuticals in humans to aid the physician in determining various metabolic (molecular) and physiologic functions within the human body for evaluation of diseases and disorders such as, but not limited to, cardiovascular disease, neurological disorders and cancer.

The combined system utilizes the MR for radiation correction maps for PET studies. The system provides inherent anatomical reference for the fused PET and MR images due to precisely aligned MR and PET image coordinate systems.

The GE SIGNA PET/MR system is a combined Magnetic Resonance Diagnostic Device (MRDD) and Positron Emission Tomography (PET) scanner. The system is designed for whole body oncology, neurology and cardiology examinations. The SIGNA PET/MR system provides simultaneous acquisition of high-resolution metabolic and anatomic information from the two major components of each system (MR and PET). Additional components of the system include: a patient table and both the acquisition and processing workstations with associated software.

The SIGNA PET/MR includes a 3.0T superconducting magnet, gradient coil, body coil and local surface RF coils based on those of the reference device Discovery MR750w 3.0T. The system includes dual drive capabilities. The SIGNA PET detectors have been modified from those of the reference device, the Discovery PET/CT 690, to allow them to be integrated into the bore of the MR. This allows for simultaneous, precisely aligned whole body MR and PET acquisition. Similar to Discovery PET/CT D690, PET supports Time of Flight (ToF). SIGNA PET/MR software is based on a combination of Discovery MR750w with Discovery PET/CT 690 software. It is used for patient management, data management, scan control, image reconstruction and image archival and evaluation. All images conform to DICOM imaging format requirements.

The SIGNA PET/MR system and surface coil suite, the subject of this application, is substantially equivalent to the commercially available devices above with modifications made to integrate the two modalities together into a whole-body system.

Here's an analysis of the provided text regarding the acceptance criteria and study for the SIGNA PET/MR device:

1. Table of Acceptance Criteria and Reported Device Performance:

The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting detailed acceptance criteria and performance data from a clinical trial. Therefore, specific numerical acceptance criteria and corresponding device performance metrics are not explicitly stated in this document.

However, based on the non-clinical and clinical tests mentioned, we can infer the intent of the acceptance criteria. The performance is reported in a qualitative manner, affirming compliance and confirming image quality.

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

• Test Set Sample Size: The document only states "Sample images from the SIGNA PET/MR were collected from multiple sites." It does not specify a numerical sample size for the test set used in clinical evaluation.
• Data Provenance: The data was collected "from multiple sites". The country of origin is not specified. Given it's a submission to the US FDA by a company based in the US (Waukesha, WI), it is highly probable that at least some, if not all, of the sites were in the USA, but this is not explicitly stated. The study was likely retrospective for the collected "sample images" to confirm image quality, though this is also not directly stated.

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

• Number of Experts: This information is not provided in the document.
• Qualifications of Experts: This information is not provided in the document. It only mentions that the system is intended to be used by "appropriately trained health care professionals."

4. Adjudication Method for the Test Set:

• The document does not specify any adjudication method. It notes that "sample images... were collected... to confirm simultaneous diagnostic image quality," implying some form of expert review, but details on how disagreements or consensus were reached are absent.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

• No, an MRMC comparative effectiveness study is not mentioned. The document describes clinical evaluation to "confirm simultaneous diagnostic image quality," which is a verification of functionality, not a comparative study of human reader performance with or without AI assistance. The device itself is a diagnostic imaging system, not an AI-assisted diagnostic tool that augments human interpretation.

6. If a Standalone Study (Algorithm Only Without Human-in-the-Loop Performance) was done:

• Yes, in spirit, the clinical evaluation focused on the standalone performance of the device (SIGNA PET/MR system) in producing diagnostic image quality. The "algorithm" here refers to the entire imaging system's capability to generate images, not a separate AI algorithm that interprets those images. The statement "Sample images... were collected... to confirm simultaneous diagnostic image quality" implies an evaluation of the system's output directly.

7. The Type of Ground Truth Used:

• The document states "to confirm simultaneous diagnostic image quality." This suggests the ground truth was likely expert consensus or qualitative assessment by healthcare professionals evaluating the diagnostic utility and clarity of the acquired images. It is not pathology, outcomes data, or a quantifiable "true" diagnostic outcome in the strict sense for individual cases.

8. The Sample Size for the Training Set:

• This information is not applicable and not provided. The SIGNA PET/MR system is a medical imaging hardware device combined with imaging software, not an AI/ML algorithm that is "trained" on a dataset in the conventional sense of machine learning for classification or prediction. Its software functions (patient management, data management, scan control, image reconstruction, archival, and evaluation) are based on established engineering principles and prior device software, not iterative learning from a large training dataset.

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

• This information is not applicable and not provided for the reasons stated in point 8.

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