(98 days)
MotionVUE2 (Q.freeze) is a PET/CT, non- invasive image analysis software application designed to support the viewing and manipulation of medical images from PET and CT imaging modalities.
MotionVUE2 (Q.freeze) offers processing tools to optimize workflow of respiratory gated exams for PET, CT and fused images of respiratory gated datasets for simultaneous viewing in multiplanar volumes and cine loops.
MotionVUE2 (Q.freeze) allows the users to generate from their 4D-PET or 4D-PET/CT series a registered 4D-PET series used for quantification of lesions and analysis of functional activity. With MotionVUE2 (Q.freeze), users will have the possibility to compare static PET/CT, 4D-PET/CT, and registered 4DPET series including visual comparison, quantification of lesions, and analysis of functional activity.
MotionVUE2 (Q.freeze) provides registration performance of up to 2 mm of center of mass motion when motion is no larger than the diameter of the object.
MotionVUE2 (Q.freeze) provides comparable/equivalent improvement of quantification results (SUV and size) as 4D PET techniques.
MotionVUE2 (Q.freeze) can be used for features with locally concentrated activity within the entire Thorax area. This area includes the organs where PET/CT imaging has the most challenges due to respiratory motion: Lung, Liver, Pancreas.
Motion VUE2 (Q.freeze) is a modification to the existing Motion VUE application. Motion VUE is a software application that provides review tools necessary for viewing, staging, planning and monitoring disease in respiratory gated PET and CT data sets. It is intended for use on Advantage Workstation platforms.
Motion VUE2 (Q.freeze) is product delivers the Non-Rigid Registration (NRR) function (Motion Freeze function) to the existing Motion VUE application on AW. The Motion VUE 2 program delivers:
- Non-Rigid Registration of PET Gated Images
- Visualization of Registered PET Gated Images
- Visualization of Registered Average PET Gated Images
- Presentation Layouts to display the Registered and Registered Average PET Images
(Non-rigid registration includes spatial normalization, which may be used to transform a patient data set to match a standardized anatomical space).
Here's an analysis of the GE Q.Freeze software based on the provided 510(k) summary, structured to address your questions.
Device Name: GE Q.Freeze Software
510(k) Number: K113408
Date of Summary: November 18, 2011
1. Table of Acceptance Criteria and Reported Device Performance
The provided document details the stated performance goals of the Q.Freeze software without explicitly outlining a formal "acceptance criteria" table in the typical sense (e.g., pass/fail thresholds against specific metrics based on a pre-defined study protocol). However, it does state specific performance claims which can be interpreted as the criteria the device is expected to meet.
| Acceptance Criterion (Stated Performance Claim) | Reported Device Performance |
|---|---|
| Registration performance of up to 2 mm of center of mass motion when motion is no larger than the diameter of the object. | The device provides registration performance of up to 2 mm of center of mass motion when motion is no larger than the diameter of the object. (The document states this as a feature/capability rather than a result from a specific study demonstrating it.) |
| Comparable/equivalent improvement of quantification results (SUV and size) as 4D PET techniques. | The device provides comparable/equivalent improvement of quantification results (SUV and size) as 4D PET techniques. (Similar to above, this is stated as a capability without detailed evidence in this summary.) |
| Performance of the device is substantially equivalent to legally marketed predicate devices (MotionVUE K081496). | The document asserts that "O.Freeze performs as well as currently marketed devices introduces no significant change in safety or effectiveness as compared to the predicate devices, and is therefore substantially equivalent in terms of safety and effectiveness to the currently marketed MotionVue." |
Note on "Study that Proves": The 510(k) summary provided largely states the device meets these criteria implicitly through its design and comparison to predicate devices, rather than detailing a specific, comprehensive study that "proves" these claims with quantitative results from a test set. This is common in 510(k) summaries where substantial equivalence is demonstrated through design comparison and known performance characteristics of similar technologies.
2. Sample Size Used for the Test Set and Data Provenance
The provided 510(k) summary does not specify a separate test set, its sample size, or the provenance of the data (e.g., country of origin, retrospective/prospective) for validating the Q.Freeze software's performance against the stated criteria. The submission relies heavily on the device's substantial equivalence to a predicate device (MotionVUE K081496) and the inherent capabilities of "Non-Rigid Registration" technology.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications
The 510(k) summary does not mention using experts from an external test set to establish ground truth.
4. Adjudication Method for the Test Set
Since no specific test set and expert review process is described, there is no mention of an adjudication method.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
An MRMC comparative effectiveness study was not mentioned or detailed in the provided 510(k) summary. The submission focuses on the software's ability to process and register images for quantification and analysis, rather than its impact on human reader performance in a clinical setting compared to unaided reading.
6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance
The device itself, being a software application for image processing and registration, can be considered for its standalone algorithmic performance. The document states:
- "MotionVUE2 (Q.freeze) provides registration performance of up to 2 mm of center of mass motion when motion is no larger than the diameter of the object."
- "MotionVUE2 (Q.freeze) provides comparable/equivalent improvement of quantification results (SUV and size) as 4D PET techniques."
These statements represent the standalone performance claims of the algorithm itself, without human intervention in the registration process. However, the details of the study or verification that directly yielded these exact performance figures are not provided in this summary. It's implied that these are expected or demonstrated through internal testing or knowledge of the underlying technology.
7. Type of Ground Truth Used
Given the nature of the device (non-rigid registration of medical images), the "ground truth" would ideally involve:
- Highly accurate, independent measurements of motion (e.g., fiducial markers tracked by external systems) in respiratory-gated data to verify registration accuracy.
- Pathology or clinical outcomes data to validate the impact of improved quantification (SUV and size) on clinical diagnosis or prognosis.
However, the provided 510(k) summary does not explicitly state the type of ground truth used for validating the 2mm registration claim or the claim about comparable/equivalent quantification improvement. It relies on the assertion that the technology itself (Non-Rigid Registration) is well-understood and performs as described, and that its output is equivalent to existing 4D PET techniques.
8. Sample Size for the Training Set
The 510(k) summary does not provide information on the sample size used for training the Q.Freeze software, as it is a specific software application with an algorithm rather than a machine learning model that typically undergoes explicit "training" on a dataset for classification or prediction tasks. The "training" in this context would implicitly refer to the development and refinement of the NRR algorithm itself.
9. How the Ground Truth for the Training Set Was Established
As noted above, the concept of a "training set" with established ground truth as understood in machine learning is not directly applicable or described in this 510(k) summary. The Q.Freeze software is described as implementing a "Non-Rigid Registration (NRR) function," which is an established image processing technique. The ground truth for developing and validating such an algorithm would typically involve theoretical models, phantom studies, and possibly human expert review of registration accuracy on various clinical cases during the development phase. However, this information is not detailed in the provided document.
§ 892.1200 Emission computed tomography system.
(a)
Identification. An emission computed tomography system is a device intended to detect the location and distribution of gamma ray- and positron-emitting radionuclides in the body and produce cross-sectional images through computer reconstruction of the data. This generic type of device may include signal analysis and display equipment, patient and equipment supports, radionuclide anatomical markers, component parts, and accessories.(b)
Classification. Class II.