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The Deep Learning Image Reconstruction for Gemstone Spectral Imaging option is a deep learning based CT reconstruction method intended to produce cross-sectional images by computer reconstruction of dual energy X-ray transmission data acquired with Gemstone Spectral Imaging, for all ages. Deep Learning Image Reconstruction for Gemstone Spectral Imaging can be used for whole body, vascular, and contrast enhanced head CT applications.

Deep Learning Image Reconstruction for Gemstone Spectral Imaging (DLIR-GSI) is the next step in CT reconstruction advancement. Like its predicate device (DLIR), DLIR-GSI is an image reconstruction method that uses a dedicated Convolution Neural Network (CNN) that has been designed and trained specifically to reconstruct CT GSI Images to give an image appearance similar to traditional FBP images while maintaining or improving the performance of ASiR-V. The DLIR-GSI can generate monochromatic images (MC), material decomposition images (MD), and virtual unenhanced images (VUE). Multiple MD images such as lodine, Water, Calcium, Hydroxyapatite (HAP), Fat, Uric Acid can be prescribed by the user and generated by the subject device. DLIR-GSI demonstrates same or better Imaging performance as compared to ASiR-V in the following areas: low contrast detectability (LCD), image noise, contrast to noise ratio (CNR), high contrast spatial resolution, CT number accuracy, MD quantification accuracy and metal artifact reduction. Reconstruction times with DLIR-GSI support a normal throughput for routine CT.

The device is marketed as Deep Learning Image Reconstruction for Gemstone Spectral Imaging and the images produced are branded as "TrueFidelity™ CT Images".

Deep Learning Image Reconstruction for Gemstone Spectral Imaging is compatible with dual energy scan modes using the standard kernel and was trained specifically on the Revolution CT family of systems (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 DLR-GSI: Low, Medium, or High. The strength selection will vary with individual users' preference for the specific clinical need.

As compared to the predicate device, the intended use of Deep Learning Image Reconstruction for Gemstone Spectral Imaging does not change (head and whole-body CT image reconstruction). Both algorithms are designed to produce cross-sectional images of the head and body by computer reconstruction of X-ray transmission data, for all ages.

Acceptance Criteria and Study Details for Deep Learning Image Reconstruction for Gemstone Spectral Imaging (DLIR-GSI)

1. Table of Acceptance Criteria and Reported Device Performance:

The study concluding that the device meets the acceptance criteria is based on:

• Bench Testing: Performed on the identical raw datasets obtained on GE's Revolution CT family of systems, applying both DLIR-GSI and ASiR-V reconstructions for comparison.
• Clinical Reader Study: A retrospective study involving radiologists evaluating images reconstructed with both ASiR-V and DLIR-GSI.

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

• Test Set Sample Size:
  • Main Reader Study: 40 retrospectively collected cases.
  • Additional Clinical Evaluation: 7 additional retrospectively collected clinically challenging 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:

• Main Reader Study: 5 board-certified radiologists.
  • Qualifications: Expertise in the specialty areas that align with the anatomical region of each case. (e.g., three readers for body/extremity, three for contrast-enhanced head/neck, one qualified for both). Specific years of experience are not mentioned.
• Additional Clinical Evaluation: 1 board-certified radiologist.
  • Qualifications: Expertise in the specialty area that aligns with all cases containing small, low-contrast objects. Specific years of experience are not mentioned.

4. Adjudication Method for the Test Set:

• Main Reader Study: Each image was read by 3 different radiologists. The radiologists provided an assessment of image quality using a 5-point Likert scale.
  • Adjudication Method: Implicitly, a consensus or agreement among the 3 readers would have been used for the assessment of diagnostic quality and noise texture preference. The document states, "The result of this reader study confirmed that the DLIR-GSI (the subject device) produce diagnostic quality images and have preferred noise texture than the reference device ASiR-V," suggesting that the collective findings of the readers led to this confirmation. Explicit details of a 2+1 or 3+1 adjudication are not provided.
• Additional Clinical Evaluation: A single board-certified radiologist evaluated the 7 challenging cases. No adjudication method was applicable as there was only one reader.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• Yes, an MRMC study was performed (the "Clinical Testing" described).
• Effect Size (Improvement with AI vs. without AI assistance): The document states that readers confirmed DLIR-GSI produced "diagnostic quality images" and had "preferred noise texture" compared to ASiR-V (considered without AI assistance in this context, or a lesser AI-powered reconstruction). The additional evaluation confirmed "adequately visualized for diagnostic use" in challenging cases. However, specific quantitative effect sizes (e.g., a percentage improvement in diagnostic accuracy, a specific change in AUC, or a numerical metric of improvement in reader performance) are not provided in the given text.

6. Standalone (Algorithm Only) Performance Study:

• Yes, a standalone performance study was done through engineering bench testing. This testing compared DLIR-GSI (algorithm only) against ASiR-V (reference/control algorithm) using identical raw datasets. Metrics like LCD, image noise, CNR, spatial resolution, CT number accuracy, material decomposition accuracy, iodine detection, and metal artifact reduction were evaluated directly from the reconstructed images without human interpretation.

7. Type of Ground Truth Used:

• Bench Testing: The ground truth for metrics like LCD, image noise, spatial resolution, CT number accuracy, etc., would have been based on physical phantom measurements and known parameters of the phantoms used in the engineering tests.
• Clinical Reader Study: The ground truth for image quality and diagnostic usability was established by expert consensus/interpretation from the board-certified radiologists. The text doesn't mention pathology or outcomes data as the primary ground truth for the reader study, but rather the radiologists' assessment of diagnostic quality and visualization.

8. Sample Size for the Training Set:

• The document states that the neural network was "trained specifically to reconstruct CT GSI Images" using "single energy acquired images on the CT Scanner" (for the predicate) and "dual energy acquired images on the CT Scanner" (for the proposed device). It also mentions "information obtained from extensive phantom and clinical data" was used for noise characteristics.
• However, the specific sample size (number of images or cases) for the training set is NOT provided in the text.

9. How the Ground Truth for the Training Set Was Established:

• The text implies that the neural network was trained to produce an "image appearance similar to traditional FBP images while maintaining or improving the performance of ASiR-V." This suggests that the "ground truth" for training was implicitly the characteristics of high-quality CT images, likely leveraging existing FBP and ASiR-V reconstructed images from "extensive phantom and clinical data."
• For noise modeling, ground truth was based on "characterization of the photon statistics as it propagates through the preprocessing and calibration imaging chain" and using a trained neural network that "models the scanned object using information obtained from extensive phantom and clinical data."
• Specific details on how the ground truth was rigorously established for the training data (e.g., expert annotations, pathology correlation, quantitative metrics derived from known phantoms) are NOT explicitly described. The training appears to be focused on matching or improving upon established reconstruction methods using a large dataset.

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