(28 days)
The Deep Learning Image Reconstruction software is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, (Volumetric), and Cardiac acquisitions, for all ages.
Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications.
Deep Learning Image Reconstruction is an image reconstruction method that uses a dedicated Deep Neural Network (DNN) that has been designed and trained specifically to generate CT Images to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression.
The images produced are branded as "TrueFidelity™ CT Images". Reconstruction times with Deep Learning Image Reconstruction support a normal throughput for routine CT.
The deep learning technology is integrated into the scanner's existing raw data-based image reconstruction chain to produce DICOM compatible "TrueFidelity™ CT Images".
The system allows user selection of three strengths of Deep Learning Image Recon: Low, Medium or High. The strength selection will vary with individual users' preference for the specific clinical need.
The DLR algorithm was modified on the Revolution CT/Apex platform for improved reconstruction speed and image quality and cleared in February 2022 with K213999. The same modified DLIR is now being ported to Revolution EVO (K131576) /Revolution Maxima (K192686), Revolution Ascend (K203169, K213938) and Discovery CT750 HD family CT systems including Discovery CT750 HD, Revolution Frontier and Revolution Discovery CT (K120833).
The provided text describes that the Deep Learning Image Reconstruction software was tested for substantial equivalence to a predicate device (K213999). The study performed was largely an engineering bench testing, comparing various image quality metrics between images reconstructed with Deep Learning Image Reconstruction (DLIR) and ASiR-V from the same raw datasets.
Here's a breakdown of the requested information based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The text indicates that the device aims to maintain the performance of ASiR-V in specific areas while offering an image appearance similar to traditional FBP images. The "acceptance criteria" can be inferred from the list of image quality metrics evaluated, with the performance goal being comparable or improved relative to ASiR-V.
| Acceptance Criteria (Implied Goal: Performance comparable to or better than ASiR-V) | Reported Device Performance (Implied: Met acceptance criteria, no adverse findings) |
|---|---|
| Image noise (pixel standard deviation) | DLIR maintains ASiR-V performance. |
| Low contrast detectability (LCD) | Evaluation performed. (Implied: Met acceptance criteria) |
| High-contrast spatial resolution (MTF) | Evaluation performed. (Implied: Met acceptance criteria) |
| Streak artifact suppression | DLIR maintains ASiR-V performance. |
| Spatial Resolution, longitudinal (FWHM slice sensitivity profile) | Evaluation performed. (Implied: Met acceptance criteria) |
| Noise Power Spectrum (NPS) and Standard Deviation of noise | Evaluation performed (NPS plots similar to FBP). (Implied: Met acceptance criteria) |
| CT Number Uniformity | Evaluation performed. (Implied: Met acceptance criteria) |
| CT Number Accuracy | Evaluation performed. (Implied: Met acceptance criteria) |
| Contrast to Noise (CNR) ratio | Evaluation performed. (Implied: Met acceptance criteria) |
| Artifact analysis (metal objects, unintended motion, truncation) | Evaluation performed. (Implied: Met acceptance criteria) |
| Pediatric Phantom IQ Performance Evaluation | Evaluation performed. (Implied: Met acceptance criteria) |
| Low Dose Lung Cancer Screening Protocol IQ Performance Evaluation | Evaluation performed. (Implied: Met acceptance criteria) |
| Image appearance (NPS plots similar to traditional FBP) | Designed to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images. |
| No additional risks/hazards, warnings, or limitations | No additional hazards were identified, and no unexpected test results were observed. |
| Maintains normal throughput for routine CT | Reconstruction times with Deep Learning Image Reconstruction support a normal throughput for routine CT. |
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size: The text states "the identical raw datasets obtained on GEHC's Revolution Ascend, Revolution Frontier and Discovery CT750 HD systems". However, the number of cases or specific sample size for these datasets is not explicitly stated.
- Data Provenance: The raw datasets were "obtained on GEHC's Revolution Ascend, Revolution Frontier and Discovery CT750 HD systems". The country of origin is not specified, and it is stated that the study used retrospective raw datasets (i.e., existing data, not newly acquired data for the study).
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
The provided text focuses on engineering bench testing and image quality metrics. It does not mention the use of experts to establish ground truth for the test set or their qualifications. The evaluation primarily relies on quantitative image quality metrics.
4. Adjudication Method for the Test Set
Since experts were not explicitly used to establish ground truth, there is no mention of an adjudication method for the test set in the provided text.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and its effect size
An MRMC comparative effectiveness study was not performed according to the provided text. The study focused on technical image quality comparisons at the algorithm level, not 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 evaluation was done. The study described is primarily a standalone evaluation of the algorithm's image quality output (e.g., noise, resolution, artifacts, detectability) when compared to images reconstructed with ASiR-V from the same raw data.
7. The Type of Ground Truth Used
The "ground truth" for the test set was essentially:
- Quantitative Image Quality Metrics: Performance relative to ASiR-V for metrics like image noise, LCD, spatial resolution, streak artifact suppression, CT uniformity, CT number accuracy, CNR, spatial resolution (longitudinal), NPS, and artifact analysis.
- Reference Image Appearance: The stated goal was an image appearance similar to traditional FBP images shown on axial NPS plots.
There is no mention of pathology, expert consensus on clinical findings, or outcomes data being used as ground truth for this particular substantial equivalence study.
8. The Sample Size for the Training Set
The text states that the Deep Neural Network (DNN) is "trained on the CT scanner" and models the scanned object using "information obtained from extensive phantom and clinical data." However, the specific sample size for the training set is not provided.
9. How the Ground Truth for the Training Set Was Established
The ground truth for the training set is implicitly established through the "extensive phantom and clinical data" mentioned as being used to train the DNN. The text indicates the DNN is trained to model noise propagation and identify noise characteristics to remove it, and to generate images with an appearance similar to traditional FBP while maintaining ASiR-V performance. This suggests the training involves learning from "ground truth" as defined by:
- Reference Image Quality: Likely images reconstructed with proven methods (e.g., FBP, ASiR-V) or images from phantoms with known properties.
- Noise Characteristics: The DNN is trained to understand and model noise.
However, the specific methodology for establishing this ground truth for the training data (e.g., expert annotation, simulated data, pathology confirmed disease) is not detailed in the provided text.
§ 892.1750 Computed tomography x-ray system.
(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.