(88 days)
The Phase Conjugate Symmetry (PCS) Option reduces the acquistion time by approximately 40% without impacting the resolution. The indications for use are the same as for standard MR Imaging.
The Phase Conjugate Symmetry (PCS) Option is a software feature which reduces the acquistion time by reducing the number of phase encodings needed to create an image.
This document describes a medical device, the "Phase Conjugate Symmetry Option for Outlook," which is a software feature for MRI systems. The primary claim of this device is to reduce MRI acquisition time by approximately 40% without impacting resolution.
Here's an analysis of the provided information concerning acceptance criteria and the study proving the device meets them:
1. A table of acceptance criteria and the reported device performance:
The document doesn't explicitly state "acceptance criteria" in a quantitative performance metric sense (e.g., target SNR reduction, specific resolution values). Instead, it relies on a comparison to a predicate device (DVP Software K895151) and asserts equivalence in performance.
| Parameter | Acceptance Criteria (Implied by Predicate Equivalence) | Reported Device Performance (Phase Conjugate Symmetry Option for Outlook) |
|---|---|---|
| General Use | Operator selectable, usable with all sequences (with stated exceptions for predicate) | Operator selectable, can be used with all sequences (with stated exception) |
| Reconstruction Algo | Same as predicate (Symmetry properties of the Fourier transform used to synthesize | Same as predicate |
| Acquisition Time | Reduced as compared to a "normal" acquisition | Reduces acquisition time by approximately 40% |
| SNR | Reduced in a mathematically predictable manner (same as predicate) | Same as predicate |
| Resolution | No Impact (same as predicate) | Same as predicate (no impact) |
| Intended Use | Same as MR | Same as MR |
2. Sample size used for the test set and the data provenance:
The provided document does not mention a specific test set, patient data, or human expert evaluations. The demonstration of safety and effectiveness relies on a comparison to a predicate device's technology and performance characteristics. No clinical study involving patient data or image analysis by experts is described.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
Not applicable. No test set involving human experts is described.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
Not applicable. No test set requiring expert adjudication 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 device is a software feature for an MRI system, not an AI-assisted diagnostic tool. No MRMC study is mentioned.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
The "study" described is essentially a technical comparison of the software's characteristics and expected physical effects (e.g., SNR reduction, resolution impact) to a previously cleared predicate device. It confirms that the underlying mathematical principles and expected outcomes regarding image properties are the same as the predicate. This is a form of standalone evaluation of the algorithm's effect on image characteristics, but it's not described as a formal quantitative study with specific metrics.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
The "ground truth" used for this submission is the established performance and characteristics of the predicate device (DVP Software K895151). The claim is that the new device's software feature operates on the same principles and yields equivalent results. This is a reliance on a "truth by analogy" rather than a direct independent ground truth acquisition for the new device.
8. The sample size for the training set:
Not applicable. This is a software feature, not a machine learning model requiring a training set in the conventional sense. Its functionality is based on established mathematical principles of Fourier transforms.
9. How the ground truth for the training set was established:
Not applicable, as there is no training set for a machine learning model.
§ 892.1000 Magnetic resonance diagnostic device.
(a)
Identification. A magnetic resonance diagnostic device is intended for general diagnostic use to present images which reflect the spatial distribution and/or magnetic resonance spectra which reflect frequency and distribution of nuclei exhibiting nuclear magnetic resonance. Other physical parameters derived from the images and/or spectra may also be produced. The device includes hydrogen-1 (proton) imaging, sodium-23 imaging, hydrogen-1 spectroscopy, phosphorus-31 spectroscopy, and chemical shift imaging (preserving simultaneous frequency and spatial information).(b)
Classification. Class II (special controls). A magnetic resonance imaging disposable kit intended for use with a magnetic resonance diagnostic device only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.