(96 days)
SubtlePET is an image processing software intended for use by radiologists and nuclear medicine physicians for transfer, storage, and noise reduction of fluorodeoxyglucose (FDG), amyloid, 18F-DOPA, 18F-DCFPyL, Ga-68 Dotatate, and Ga-68 PSMA radiotracer PET images.
The SubtlePET image processing software reduces noise to increase image quality using a deep neural network-based algorithm.
The software employs a convolutional network-based method in a pixel's neighborhood to generate the value for each pixel. Using a residual learning approach, the software predicts the noise components and structural components. The software separates these components, which enhances the structure while simultaneously reducing the noise.
The workflow of the product can be easily adapted to existing radiology departmental workflow. The product acts as a DICOM node that receives DICOM 3.0 digital medical image data from the modality or another DICOM source, processes the data and then forwards the enhanced study to the selected destination. This destination can be any DICOM node, typically either the PACS system or a specific workstation.
Here's a breakdown of the acceptance criteria and study information for SubtlePET, based on the provided document:
Acceptance Criteria and Device Performance
| Acceptance Criteria Objective | Reported Device Performance |
|---|---|
| Noise reduction to increase image quality in PET scans. | Significant average increase in quantitative metrics for all cases, demonstrating that the software reduced noise in PET scans. |
Study Information
2. Sample size used for the test set and data provenance:
- The document states that the noise reduction bench test utilized "representative cases of human data." However, it does not specify the exact sample size used for this test set.
- The data provenance is described as "human data already gathered under the auspices of IRB-approved clinical protocols." This indicates the data is retrospective and was collected according to ethical guidelines. The country of origin is not explicitly stated.
3. Number of experts used to establish the ground truth for the test set and qualifications of those experts:
- The document does not provide information regarding the number of experts used or their qualifications for establishing ground truth specifically for the test set.
4. Adjudication method for the test set:
- The document does not specify an adjudication method used for the test set.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance:
- The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study or any effect size related to human reader improvement with/without AI assistance. The performance data focuses on quantitative metrics of noise reduction.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, a standalone performance assessment was conducted. The "Noise reduction bench test utilizing representative cases of human data" and the reported "significant average increase in quantitative metrics" describe the algorithm's performance independent of a human reader in a diagnostic workflow.
7. The type of ground truth used:
- The document implies a "reference standard" or "gold standard" for noise reduction based on the quantitative metrics. However, it does not explicitly state what this ground truth was (e.g., a "true" noise-free image, or a statistically derived reference). It focuses on the algorithm's ability to reduce noise relative to the input image, rather than diagnosing a condition against a pathology report.
8. The sample size for the training set:
- The document does not specify the sample size used for the training set of the deep neural network.
9. How the ground truth for the training set was established:
- The document does not explicitly describe how ground truth was established for the training set. It mentions the software uses a "deep neural network-based algorithm" that employs a "convolutional network-based method" and a "residual learning approach" to predict noise and structural components. This suggests the training would involve pairs of noisy and "cleaner" or target images, but the exact method for generating or establishing the "cleaner" ground truth is not detailed.
§ 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.