(59 days)
The Deep Learning Image Reconstruction option 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, Helical (Volumetric), and Cardiac acquisitions, for all ages.
Deep Learning Image Reconstruction 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.
Deep Learning Image Reconstruction was trained specifically on the Revolution CT/Apex platform (K163213, K133705, K19177). 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' preferences and experience for the specific clinical need.
The DLR algorithm is now being modified on the Revolution family CT systems (K133705, K163213, K19177) for improved reconstruction speed and image quality, thus triggering this premarket notification.
The provided document describes the Deep Learning Image Reconstruction (DLIR) device, its acceptance criteria, and the study conducted to prove it meets these criteria.
Here's a breakdown of the requested information:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state numerical acceptance criteria with pass/fail thresholds for each metric. Instead, it focuses on demonstrating non-inferiority or improvement compared to a predicate device (ASiR-V) and ensuring diagnostic quality. The reported device performance is qualitative, indicating "significantly better subjective image quality" and "diagnostic quality images."
However, based on the non-clinical and clinical testing sections, we can infer the performance metrics evaluated.
| Acceptance Criteria (Inferred from tests) | Reported Device Performance (Qualitative) |
|---|---|
| Image Quality Metrics (Objective - Bench Testing): | DLIR maintains performance similar to ASiR-V, with potential for improvement in noise characteristics. |
| - Low Contrast Detectability (LCD) | Evaluated. Aim to be similar to ASiR-V. |
| - Image Noise (pixel standard deviation) | Evaluated. Aim to be similar to ASiR-V. DLIR is designed to "identify and remove the noise." |
| - High-Contrast Spatial Resolution (MTF) | Evaluated. Aim to be similar to ASiR-V. |
| - Streak Artifact Suppression | Evaluated. Aim to be similar to ASiR-V. |
| - Spatial Resolution, longitudinal (FWHM slice sensitivity profile) | Evaluated. Aim to be similar to ASiR-V. |
| - Noise Power Spectrum (NPS) and Standard Deviation of noise | Evaluated. NPS plots show similar appearance to traditional FBP images. |
| - CT Number Uniformity | Evaluated. Aims to ensure consistency. |
| - CT Number Accuracy | Evaluated. Aims to ensure measurement accuracy. |
| - Contrast to Noise (CNR) ratio | Evaluated. Aims to ensure adequate contrast. |
| - Artifact analysis (metal objects, unintended motion, truncation) | Evaluated. Aims to ensure reduction or absence of artifacts. |
| - Pediatric Phantom IQ Performance Evaluation | Evaluated. Specific to pediatric imaging. |
| - Low Dose Lung Cancer Screening Protocol IQ Performance Evaluation | Evaluated. Specific to low-dose imaging protocols. |
| Subjective Image Quality (Clinical Reader Study): | "produce diagnostic quality images and have significantly better subjective image quality than the corresponding images generated with the ASiR-V reconstruction algorithm." |
| - Diagnostic Usefulness | Diagnostic quality images produced. |
| - Image Noise Texture | "Significantly better" subjective image quality. |
| - Image Sharpness | "Significantly better" subjective image quality. |
| - Image Noise Texture Homogeneity | "Significantly better" subjective image quality. |
| Safety and Effectiveness: | No additional risks/hazards, warnings, or limitations introduced. Substantially equivalent to predicate. |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: 40 retrospectively collected clinical cases.
- Data Provenance: Retrospectively collected clinical cases. The country of origin is not specified in the provided text.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
- Number of Experts: 6 board-certified radiologists.
- Qualifications of Experts: Board-certified radiologists with "expecialty areas that align with the anatomical region of each case."
4. Adjudication Method for the Test Set
The document describes a reader study where each of the 40 cases (reconstructed with both ASiR-V and DLIR) was read by 3 different radiologists independently. They provided an assessment of image quality using a 5-point Likert scale. There's no explicit mention of an adjudication process (e.g., 2+1, 3+1) if there were disagreements among the three readers, as the focus seems to be on independent assessment and overall subjective preference comparison.
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
Yes, a multi-reader multi-case (MRMC) comparative effectiveness study was done. Human readers compared images reconstructed with DLIR (AI-assisted reconstruction) against images reconstructed with ASiR-V (without DLIR).
- Effect Size: The study confirmed that DLIR (the subject device) produced diagnostic quality images and "have significantly better subjective image quality" than the corresponding images generated with the ASiR-V reconstruction algorithm. The text doesn't provide a specific numerical effect size (e.g., a specific improvement percentage or statistical metric), but it qualitatively states a "significant" improvement based on reader preference for image noise texture, image sharpness, and image noise texture homogeneity.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, extensive standalone (algorithm-only) performance testing was conducted. This is detailed in the "Additional Non-Clinical Testing" section, where DLIR and ASiR-V reconstructions of identical raw datasets were compared for various objective image quality metrics without human interpretation during these specific tests.
7. The Type of Ground Truth Used
The ground truth for the clinical reader study was established through expert consensus/assessment of image quality and preference by the participating radiologists. For the non-clinical bench testing, the ground truth was based on objective physical measurements and established phantom data with known properties.
8. The Sample Size for the Training Set
The document mentions that the Deep Neural Network (DNN) for DLIR was "trained specifically on the Revolution CT/Apex platform." However, it does not specify the sample size (number of images or cases) used for the training set.
9. How the Ground Truth for the Training Set was Established
The text states that the DNN was "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." It also notes that the DNN "models the scanned object using information obtained from extensive phantom and clinical data."
While the exact method for establishing ground truth for training isn't explicitly detailed, it implies a process where:
- Reference Images: Traditional FBP (Filtered Back Projection) and ASiR-V images likely served as reference or target outputs for the DNN, specifically regarding image appearance, noise characteristics, and spatial resolution.
- "Extensive phantom and clinical data": This data, likely corresponding to various anatomical regions, pathologies, and dose levels, was fed into the training process. The ground truth would involve teaching the network to reconstruct images that, when compared to conventionally reconstructed images (FBP/ASiR-V), exhibit desired image quality attributes (e.g., reduced noise while preserving detail).
- Noise Modeling: The training process characterized "the propagation of noise through the system" to identify and remove it, suggesting a ground truth related to accurate noise modeling and reduction.
§ 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.