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The MAGNETOM systems are indicated for use as magnetic resonance diagnostic devices (MRDD) that produce transverse, sagittal, coronal and oblique cross sectional images, spectroscopic images and or spectra, and that display the internal structure and/or function of the head, body or extremities.

Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used. These images and the physical parameters derived from the inages and/or spectra when interpreted by a trained physician, yield information that may assist in diagnosis.

The MAGNETOM systems described above may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room display and MR-Safe biopsy needles.

The subject device, synqo MR E11B system software, is being made available for the following MAGNETOM MR Systems:

• MAGNETOM Aera (24-channel configuration), .
• MAGNETOM Avanto™ ●
• MAGNETOM Skyra™, ●
• . MAGNETOM Prisma and
• . MAGNETOM Prisma™

Two new coils, Body 30/60 and Body 6 long, will be available for the subject device systems. The feature FREEZEit will be extended to other body regions. In addition to the abdomen region, FREEZEit will be extended to other regions such as the head, head and neck, pelvis, and chest region. . The syngo MR E11B SW also includes new sequences as well as minor modifications of already existing features. A high level summary of the new sequences can be viewed below:

DSI
With software version syngo MR E11B Siemens offers DSI for MAGNETOM Prisma, Prismall and Skyra" systems. The DSI option allows diffusion-weighted images to be acquired according to a DSI-compatible q-space sampling scheme.

QISS evaluation
QISS (Quiescent-Interval Single-Shot) MR Angiography is a technique for non-contrastenhanced MR Angiography (non-CEMRA) that is particularly suited for examinations of patients with PAD. Since patients with PAD may also suffer from additional impairments such as renal dysfunction, the administration of contrast agent may often be unadvisable in this patient group. Siemens provides a manageable and optimized QISS workflow for imaging peripheral arteries, which can be easily adapted by the customer based on the patient's needs.

A new "Dot Engine" is provided to ease MRI acquisitions in Radiation Therapy.

RT Dot Engine
RT Dot Engine is a new Dot Engine for aiding in Radiation Therapy planning. The RT Dot Engine does not provide new functionality, but collects and displays existing system information for the user. The RT Dot Engine comprises existing protocols, enhanced with the RT Planning Dot Add-in and the "MPR Planning" interaction step. The RT (Radiation Therapy) Dot Engine is used to ease MRI acquisitions of the head and the head/neck region with stereotactic frames or mask-based fixation techniques. RT Dot Engine is a workflow solution for acquiring MR images intended to aid in Radiation Therapy Planning. RT Dot engine helps streamline acquisition of MR images to be used along with any RT planning software that uses MR images in addition to CT images.

The provided text is a 510(k) summary for a medical device and does not contain the level of detail typically found in a clinical study report regarding acceptance criteria and performance studies for an AI-powered device.

This document describes a Magnetic Resonance Diagnostic Device (MRDD) software upgrade (syngo MR E11B) for existing Siemens MAGNETOM MR systems. The submission is a 510(k) premarket notification, which seeks to demonstrate substantial equivalence to a legally marketed predicate device, rather than proving performance against specific acceptance criteria for a novel AI algorithm.

Therefore, many of the requested details about acceptance criteria, clinical study design, sample sizes, ground truth establishment, and expert adjudication are not present in this type of regulatory document.

However, I can extract the information that is available and clarify what is missing based on the context of a 510(k) submission for an MRI system software upgrade:

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

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

• Sample Size for Test Set: Not explicitly stated as a formal "test set" in the context of an algorithm evaluation. The document mentions "clinical images were provided to support the new coils as well as the new software features," but the number of images or patients is not specified.
• Data Provenance: Not specified. Given the nature of a 510(k) for a software upgrade to an MRI machine, the "clinical images" likely came from internal testing or routine clinical acquisitions.

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)

• The document states "These images and the physical parameters derived from the images and/or spectra when interpreted by a trained physician, yield information that may assist in diagnosis." However, it does not specify the number or qualifications of experts used to establish a formal ground truth for testing the software's performance, as this is an MRI system software upgrade, not a diagnostic AI algorithm.

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

• No adjudication method is mentioned.

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 MRMC study was conducted or reported. This device is a software upgrade for an MRI system, not an AI diagnostic assistant tool.

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

• Not applicable in the context of this device. The software "produces transverse, sagittal, coronal and oblique cross sectional images, spectroscopic images and or spectra," which are then "interpreted by a trained physician." It is not a standalone diagnostic algorithm.

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

• For the nonclinical tests (SNR, uniformity, heating, acoustic noise), the "ground truth" would be established by technical specifications and phantom measurements.
• For image quality assessments, a "ground truth" (e.g., against specific diagnostic findings) is not detailed. The assessment likely involved expert review of image quality (e.g., resolution, artifact reduction, diagnostic clarity) rather than a comparison to a definitive clinical ground truth established by pathology or long-term outcomes. The primary focus is on demonstrating that the images produced are diagnostically acceptable and equivalent to the predicate.

8. The sample size for the training set

• Not applicable. This document describes a software upgrade for an MRI system, which includes new sequences and features (e.g., DSI, QISS, RT Dot Engine). It is not an AI algorithm that would typically have a "training set" in the machine learning sense. The software development follows traditional engineering and quality assurance practices.

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

• Not applicable, as no training set (in the AI/ML context) is mentioned for this device.

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