(144 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 lmages 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 family of systems (K163213, K133705). 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.
Acceptance Criteria and Device Performance for Deep Learning Image Reconstruction (K183202)
The Deep Learning Image Reconstruction (DLIR) device, developed by GE Medical Systems, LLC, was evaluated for substantial equivalence to its predicate device, ASiR-V, as part of its 510(k) submission (K183202). The acceptance criteria for the DLIR are implicitly defined by its performance being equivalent to or better than the predicate device across various image quality metrics relevant to CT imaging.
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria for DLIR are based on maintaining the performance of ASiR-V in key imaging characteristics while achieving an image appearance similar to traditional FBP images. The "reported device performance" refers to the demonstrated performance of DLIR relative to ASiR-V during non-clinical and clinical testing, confirming it met the unspoken expectation of non-inferiority or improvement.
| Image Quality Metric | Acceptance Criteria (Implicitly: Non-inferior to ASiR-V) | Reported Device Performance (DLIR vs. ASiR-V) |
|---|---|---|
| Image Noise (pixel standard deviation) | Performance equivalent to ASiR-V, as measured using head and body uniform phantoms. | Preserved the performance of ASiR-V. Engineering bench testing confirmed equivalent noise levels. |
| Low Contrast Detectability (LCD) | Performance equivalent to ASiR-V, as measured using head and body MITA/FDA low contrast phantoms and a model observer. | Preserved the performance of ASiR-V. Engineering bench testing demonstrated equivalent LCD. |
| High-Contrast Spatial Resolution (MTF) | Performance equivalent to ASiR-V, as measured using a quality assurance phantom with a tungsten wire. | Preserved the performance of ASiR-V. Engineering bench testing confirmed equivalent MTF. |
| Streak Artifact Suppression | Performance equivalent to ASiR-V, as measured using an oval uniform polyethylene phantom with embedded high attenuation objects. | Preserved the performance of ASiR-V. Engineering bench testing showed equivalent streak artifact suppression. |
| Spatial Resolution, longitudinal (FWHM) | Performance equivalent to ASiR-V. | Demonstrated equivalent performance in engineering bench testing. |
| Noise Power Spectrum (NPS) | Image appearance similar to traditional FBP images, while maintaining ASiR-V performance. | Engineering bench testing, specifically NPS plots, confirmed the device generated images with an appearance similar to traditional FBP images while maintaining ASiR-V performance. |
| CT Number Uniformity | Performance equivalent to ASiR-V. | Demonstrated equivalent performance in engineering bench testing. |
| CT Number Accuracy | Performance equivalent to ASiR-V. | Demonstrated equivalent performance in engineering bench testing. |
| Contrast to Noise (CNR) ratio | Performance equivalent to ASiR-V. | Demonstrated equivalent performance in engineering bench testing. |
| Artifact analysis (metal, motion, truncation) | Performance equivalent to ASiR-V. | Demonstrated equivalent performance in engineering bench testing. |
| Diagnostic Quality (Clinical Reader Study) | Images produced are of diagnostic quality, and no new hazards or unexpected results are identified. | Reader study indicated that images were of diagnostic quality, and radiologists rated performance highly across noise texture, sharpness, and noise texture homogeneity, supporting substantial equivalence and performance claims. A final evaluation by a board-certified radiologist confirmed diagnostic quality in abdominal and pelvis regions. |
2. Sample Size for the Test Set and Data Provenance
The clinical reader study used 60 retrospectively collected clinical cases. The raw data from these cases were reconstructed with both ASiR-V and Deep Learning Image Reconstruction. The data provenance is not explicitly stated in terms of country of origin but is implied to be from standard clinical practice given the retrospective collection of cases.
3. Number of Experts and Qualifications for Ground Truth Establishment (Test Set)
Nine board-certified radiologists were used for the clinical reader study (test set). Their qualifications included:
- Expertise in specialty areas aligning with the anatomical region of each case.
- Three radiologists specialized in body and extremity anatomy.
- Three radiologists specialized in head/neck anatomy.
- Three radiologists specialized in cardiac/vascular anatomy.
A single board-certified radiologist performed a final evaluation of low contrast and small lesions in the abdominal and pelvis region.
4. Adjudication Method (Test Set)
Each image in the clinical reader study was read by 3 different radiologists independently. These radiologists provided an assessment of image quality related to diagnostic use according to a a 5-point Likert Scale. There is no explicit mention of an adjudication process (e.g., 2+1, 3+1), but for the direct comparison part, readers were asked to compare ASiR-V and DLIR images directly.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
Yes, a multi-reader multi-case (MRMC) study was performed. The study involved 9 radiologists reading 60 cases reconstructed with both ASiR-V and DLIR.
The exact effect size of how much human readers improve with AI (DLIR) vs. without AI (ASiR-V, as it's also an advanced reconstruction) assistance is not explicitly quantified in terms of specific metrics like diagnostic accuracy improvement or reading time reduction. However, the study's results are stated to "support substantial equivalence and performance claims." Readers were also asked to directly compare ASIR-V and Deep Learning Image Reconstruction images according to three key metrics of image quality preference – image noise texture, image sharpness, and image noise texture homogeneity, implying a preference or at least equivalence for DLIR.
6. Standalone (Algorithm Only) Performance
Yes, standalone (algorithm only) performance was done as part of the engineering bench testing. This included objective measurements of various image quality metrics using identical raw datasets on a GE Revolution CT, then applying DLIR or ASiR-V reconstruction. The results from this testing demonstrated the algorithm's performance in:
- Low Contrast Detectability (LCD)
- Image Noise (pixel standard deviation)
- High-Contrast Spatial Resolution (MTF)
- Streak Artifact Suppression
- Spatial Resolution, longitudinal (FWHM)
- Low Contrast Detectability/resolution (statistical)
- Noise Power Spectrum (NPS) and Standard Deviation of noise
- CT Number Uniformity
- CT Number Accuracy
- Contrast to Noise (CNR) ratio
- Artifact analysis
7. Type of Ground Truth Used
- Non-Clinical Testing: Ground truth for non-clinical testing was established via physical phantoms (e.g., MITA/FDA low contrast phantoms, uniform phantoms, quality assurance phantoms with tungsten wire, oval uniform polyethylene phantoms with embedded objects) and model observers for objective measurements.
- Clinical Testing: Ground truth for the clinical reader study was based on expert consensus/opinion from board-certified radiologists using a 5-point Likert scale for image quality assessment and for direct comparison of image quality preference attributes. A final evaluation by one board-certified radiologist confirmed diagnostic quality against established clinical standards.
8. Sample Size for the Training Set
The document states that Deep Learning Image Reconstruction was trained specifically on the Revolution CT family of systems (K163213, K133705). It also mentions that the Deep Neural Network (DNN) "models the scanned object using information obtained from extensive phantom and clinical data." However, an exact sample size (number of images or cases) for the training set is not provided in the provided text.
9. How the Ground Truth for the Training Set was Established
The ground truth for the training set, which involved "extensive phantom and clinical data," was established through the inherent characteristics of CT imaging data. For example, for phantom data, the known physical properties and structures within the phantoms serve as ground truth. For clinical data, the "ground truth" for the training process would implicitly be derived from high-quality, typically higher-dose or reference-standard reconstructions (e.g., traditional FBP or fully iterative reconstructions) that the DNN aims to emulate or improve upon, often by learning to remove noise while preserving diagnostic information. The DNN was designed 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.
§ 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.