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The SIGNA Architect, SIGNA Artist, Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T and the Optima MR450w 1.5T systems are whole body magnetic resonance scanners designed to support 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 SIGNA Architect, SIGNA Artist, Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T and the Optima MR450w 1.5T systems reflect the spatial distribution or molecular environment of nuclei exhibiting magnetic resonance. These images and/or spectra when interpreted by a trained physician vield information that may assist in diagnosis.

The Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T, Optima MR450w 1.5T, SIGNA Architect and SIGNA Artist systems are whole body magnetic resonance scanners designed to support high resolution, high signal-to-noise ratio, and short scan times. The systems each feature a superconducting magnet. The data acquisition system accommodates up to 128 independent receive channels in various increments and multiple independent coil elements per channel during a single acquisition series. Each 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. Each 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, Discovery MR450 1.5T, Discovery MR750w 3.0T, Optima MR450w 1.5T, SIGNA Architect. SIGNA Artist systems are designed to conform to NEMA DICOM standards (Digital Imaging and Communications in Medicine).

The original description hasn't changed from predicate devices (K160618), other than reflecting the additional receive channels available.

The modifications to these systems include the MAGIC DWI and CardioMaps software features, delivered via the DV26 program. The proposed software features will be ported over to other GE Healthcare MR systems based on appropriate design controls and evaluation of the change in accordance with FDA's Guidance—Deciding When to Submit a 510(k) for a Change to an Existing Device.

This document describes the premarket notification (510(k)) for GE Medical Systems' SIGNA Architect, SIGNA Artist, Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T and the Optima MR450w 1.5T Magnetic Resonance (MR) diagnostic devices. The submission focuses on the addition of MAGIC DWI (Diffusion-Weighted Imaging) and CardioMaps software features.

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

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state specific quantitative acceptance criteria or performance metrics for the MAGIC DWI and CardioMaps software features in a table format. Instead, it indicates that testing was completed with "passing results per the pass/fail criteria defined in the test cases."

Implicit Acceptance Criteria (inferred from the document):

• Safety and Effectiveness: The primary acceptance criterion is that the modified software features (MAGIC DWI and CardioMaps) are "as safe and effective as the predicate" devices and do "not raise different questions of safety and effectiveness."
• Compliance with Standards: The software features must comply with voluntary standards: AAMI/ANSI 62304, AAMI/ANSI ES60601-1, and IEC 60601-2-33.
• Acceptable Performance: Phantom testing for both software features must demonstrate "acceptable performance."

Reported Device Performance:

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

• Test Set Sample Size: The document does not specify a numerical sample size for either the phantom testing or the clinical images. It generically refers to "phantom testing" and "sample clinical images."
• Data Provenance: Not explicitly stated. For phantom testing, it's typically controlled laboratory conditions. For clinical images, it's not mentioned whether they are retrospective or prospective, nor their country of origin.

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

The document does not provide this information.
The summary states that images and/or spectra are interpreted by a "trained physician," but it doesn't detail the number or qualifications of experts involved in establishing ground truth for the specific performance evaluation of the new software features.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method.
It states that "passing results per the pass/fail criteria defined in the test cases" were achieved for phantom testing. For clinical images, it mentions they are "interpreted by a trained physician," implying clinical judgment, but no formal adjudication process (like 2+1 or 3+1) is described for the evaluation presented in this summary.

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

The document does not indicate that an MRMC comparative effectiveness study was performed.
The evaluation relies on compliance with standards, phantom testing, and presentation of sample clinical images to demonstrate "substantial equivalence" rather than a comparative effectiveness study measuring human reader improvement with AI assistance. The software features are enhancements to image acquisition and processing, not explicitly AI-assisted diagnostic tools in the context of comparative reading studies.

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

While the software features (MAGIC DWI and CardioMaps) represent algorithm-only additions, the document emphasizes that the "images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis."
The "phantom testing" and quality assurance measures (e.g., unit-level, integration, performance, safety testing) can be considered standalone evaluations of the algorithms' output quality and adherence to specifications. However, the ultimate "performance" in the diagnostic context is tied to physician interpretation. The regulatory focus here is on the system producing diagnostically useful images, not on an algorithm making a standalone diagnosis.

7. The Type of Ground Truth Used

• For Phantom Testing: The ground truth would typically be established by known physical properties or measurements of the phantom itself. The "pass/fail criteria" would be based on expected quantitative accuracy, image quality, or signal properties against these known values.
• For Clinical Images: The document mentions "images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis." This implies that the effectiveness in a clinical setting is ultimately judged by expert clinical interpretation, but it does not specify a formal "ground truth" (e.g., pathology, surgical findings, long-term outcomes) used to validate the clinical utility of the specific new software features. It's more about demonstrating that the images produced can be interpreted by a physician to assist diagnosis.

8. The Sample Size for the Training Set

The document does not provide any information regarding a training set sample size. This is likely because the referenced software features are defined as modifications to existing MR systems, and while they involve algorithms, the summary doesn't describe them as machine learning models that require distinct "training sets" in the typical sense. The development process described (risk analysis, requirements reviews, design reviews, various levels of testing) is a standard software engineering approach.

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

As no training set is mentioned for machine learning, information on how its ground truth was established is not applicable or provided in this document.

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