Search Results

PET Digital Gating generates a respiratory signal which can be used to automate motion correction of PET images with respiratory motion. Images that are motion-corrected using PET Digital Gating are intended to aid physicians in: detection; localization; evaluation; diagnosis; staging; monitoring; and/or follow up of disease, abnormality, and or function; therapy planning, monitoring, and guidance; radiotherapy treatment planning; and for Nuclear Medicine interventional procedures. PET Digital Gating maybe used with PET radiopharmaceuticals approved by the regulatory authority in the country of use, in patients of all ages, with a wide range of sizes, body habitus, and extent/type of disease.

Areas of the body most impacted by respiratory motion are the chest, abdomen, and pelvis. Disease types in which respiratory motion may have a significant impact, if uncorrected, include:

-Lung Cancer (e.g. Small Cell and Non-Small Cell);

-Liver Cancer;

• -Colorectal Cancer;
• -Lymphoma (e.g. Hodgkin's and Non-Hodgkin's);
• -Cancers that have metastasized to the liver;
• -Cancers in the Thorax; and
• -Heart Disease

The new PET Digital Gating option (aka Data Driven Gating or DDG) is a software-only device created within an update to the existing PET/CT acquisition and processing software. DDG provides the capability to derive a respiratory signal from the acquired PET data as an alternative to existing device-based respiratory gations that are available on the predicate device, GE's Discovery MI (K161574). The respiratory triggers generated by PET Digital Gating are used in a manner identical to those generated by device-based respiratory gating systems such as the reference device, Varian's "RPM Respiratory Gating System" (K102024).

PET Digital Gating provides the analogous respiratory triggers as the device-based systems, without the use of an external respiratory gating device and is an alternative to any existing external device. PET Digital Gating may be used both retrospectively on previously acquired exams, or prospectively where it operates during the acquisition. As is the case for existing device-based respiratory gating systems, PET Digital Gating's triggers may be used with GE's existing motion compensation techniques (gated, Q.Freeze, Q.Static).

Device-based systems rely on external body motion to determine the respiratory waveform. With PET Digital Gating the respiratory motion determined from internal patient anatomical movement.

PET Digital Gating uses an algorithm that incorporates a principal components analysis (PCA) to compute the spatial-temporal variation of PET list data. Principal components analysis is a data processing technique to find a mathematical basis for a dataset where the basis vectors are ordered to explain the maximum variation within the data. The PCA computes the basis vectors (eigenvectors) of the input data variation. The largest principal components are used along with the input data to generate 1-dimensional eigenvectors. These eigenvectors are subsequently used along with the list data to generate respiratory waveforms.

A fast Fourier transforms of the waveforms are used to determine an "R-value" that is used as the characterization metric for the respiratory motion identified in the dataset.

The R value threshold is user configurable. When DDG is used prospectively in conjunction with Q.Static motion correction, the R value threshold can be set for each bed position along with a "base" (no motion) acquisition time and a "Q.Static" (motion detected) acquisition time. Then, for each bed position, PET Digital Gating will evaluate the R value near the end of the base acquisition time, and if it is greater than the preset R value threshold, extend the acquisition time to the Q.Static acquisition time and generate the respiratory triggers to be used. If the evaluated R value is below the preset value, the acquisition completes and the next bed position is acquired.

The GE Medical Systems PET Digital Gating device aims to provide automated motion correction for PET images affected by respiratory motion. The primary study presented to demonstrate substantial equivalence and meet acceptance criteria involves engineering bench testing using a respiratory motion phantom and analysis of representative clinical examples.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria in a table format. Instead, it describes the testing performed and the conclusion drawn from it: that the device is "as safe and effective the legally marketed predicate device." The performance reported is that the device successfully generates respiratory triggers and allows for motion correction, analogous to device-based systems, but using internal patient anatomical movement.

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

• Test Set Sample Size: The document mentions "a representative sample of each of the current GE PET/CT scanner platforms" for the engineering bench testing. For the clinical examples, it states "representative clinical examples." Specific numbers for either are not provided.
• Data Provenance: The engineering bench testing used a "commercially available respiratory motion phantom." The "representative clinical examples" are not specified for their country of origin or whether they were retrospective or prospective, though it does state that "retrospective application of PET Digital Gating was used" for evaluation.

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

The document does not specify the number of experts or their qualifications used to establish ground truth for the test set.

4. Adjudication Method for the Test Set

The document does not describe an adjudication method for the test set. The validation relies on engineering bench testing with a phantom and comparison to clinical examples, rather than a multi-reader assessment.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not performed or reported in this document. The study described focuses on the technical capability of the device to generate motion correction signals, not on direct human reader performance with or without AI assistance.

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

Yes, a standalone performance assessment was conducted through the engineering bench testing. This testing evaluated the algorithm's ability to "determine the respiratory waveform" and "generate respiratory triggers" using a phantom, indicating an algorithm-only evaluation of its core functionality. The analysis of "representative clinical examples" also implies a standalone evaluation of the algorithm's output (motion-corrected images) using retrospective data.

7. The Type of Ground Truth Used

• For Engineering Bench Testing: The ground truth was established by the precisely controlled and known motion of the "commercially available respiratory motion phantom," which is designed to move inserts with varying speed and amplitude linked to "clinically determined, patient-specific respiratory motion waveforms." This is an engineered/simulated ground truth.
• For Clinical Examples: The type of ground truth used for "representative clinical examples" is not explicitly defined, but the quantitative analysis would likely compare the motion-corrected images generated by the device against a clinical expectation or standard for motion reduction, potentially based on qualitative assessment or SUV/volume measurements from previous methods, though this is not detailed.

8. The Sample Size for the Training Set

The document does not provide a sample size for the training set. It describes the algorithm (PCA) and its function, but not the data used to train it.

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

The document does not describe how the ground truth for the training set was established, as details about a training set are not provided. The algorithm (Principal Component Analysis) is described as a "data processing technique to find a mathematical basis for a dataset," which is typically unsupervised or uses specific characteristics of the input data rather than explicitly labeled ground truth in the traditional sense of classification.

Ask a specific question about this device