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Imaging of:

• The Whole Body (including head, abdomen, pelvis, limbs and extremities, spine, neck, . TMJ, heart, blood vessels). {Application terms include MRCP (MR Cholangiopancreatography), MR Urography, MR Myelography, MR Fluoroscopy, SAS (Surface Anatomy Scan), Dynamic Scan and Cine Imaging.]
• Fluid Visualization .
• 2D / 3D Imaging ●
• MR Angiography / MR Vascular Imaging .
• . Water / Fat Imaging
• Perfusion / Diffusion Imaging .

The OPART™ /Ultra system is added into existing OAPRT™ series by incorporating the high performance gradient system. The OPART™ /Ultra system and the OPART™ system with Ultra gradient upgrade kit offers the modified sequences for the faster acquisition than existing OPART™ systems.

The provided text describes a 510(k) submission for a Magnetic Resonance Diagnostic Device Accessory, the OPART™/Ultra, and its upgrade kit. This document focuses on demonstrating substantial equivalence to a predicate device, rather than providing detailed acceptance criteria and a study report as would be found for a novel device. Therefore, much of the requested information, particularly regarding specific performance metrics, sample sizes for test/training sets, expert involvement, and ground truth establishment, is not present in the provided text.

However, I can extract and present the information that is available.

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly present "acceptance criteria" in a quantitative format for imaging performance. Instead, it compares safety parameters of the new device (OPART™/Ultra) against its predicate (OPART™) and other substantially equivalent devices. The "performance" here refers to physical and operational characteristics rather than diagnostic accuracy.

A note on "acceptance criteria": For a 510(k) submission like this, the "acceptance criteria" for safety parameters are often implicitly that the new device's performance remains within acceptable safety limits, or that any changes do not adversely affect safety and are appropriately documented. The increases in acoustic noise (115.4 dB Peak, 102.5 dB A-weighted) and maximum dB/dt (51 T/sec) for the OPART™/Ultra are noted, and the document states that TOSHIBA performed "dB/dt and acoustic noise verifications," implying these increases were evaluated for safety and deemed acceptable for the intended use. The core "acceptance criterion" for the overall device is substantial equivalence to legally marketed predicate devices.

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

The document does not describe a "test set" in the context of diagnostic performance evaluation with patient data. The evaluations mentioned are primarily engineering-based verifications of physical parameters and comparisons to predicate device specifications. There is no mention of patient data being used for device performance testing in this document.

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

Not applicable. As no diagnostic performance study utilizing patient data and ground truth is described, there's no mention of experts or their qualifications.

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

Not applicable, as no diagnostic performance study 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

Not applicable. This is a submission for an MRI system and its upgrade, not an AI-powered diagnostic tool. No MRMC study or AI assistance is mentioned.

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

Not applicable. This document is about a hardware and software upgrade for an MRI system, not a standalone algorithm.

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

Not applicable, as no diagnostic performance study requiring ground truth is described. The "ground truth" for the engineering parameters would be the actual measured values of the physical characteristics (e.g., static field strength, acoustic noise, SAR, dB/dt).

8. The sample size for the training set

Not applicable. This document does not describe a machine learning algorithm that requires a training set. The software changes are related to system control, improved user interface, and new or modified acquisition sequences.

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

Not applicable, as no training set for a machine learning algorithm is discussed.

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