Deep Learning Image Reconstruction
GE Medical Systems, LLC.Deep Learning Image Reconstruction (DLIR) by GE Medical Systems is an AI-powered CT image reconstruction method that uses a deep neural network to produce high-quality cross-sectional images of the head and whole body. It supports multiple CT acquisition types and enhances image quality by reducing noise and artifacts, helping clinicians get clearer and more accurate imaging results.
Intended Use
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. It can be used for head, whole body, cardiac, and vascular CT applications.
Technology
Deep Learning Image Reconstruction uses a dedicated Deep Neural Network (DNN) trained on the CT scanner data to model noise propagation and remove noise, producing images with an appearance similar to traditional filtered back projection but with improved noise, low contrast detectability, spatial resolution, and artifact suppression. It integrates into the scanner’s raw data-based reconstruction chain and supports user-selectable reconstruction strengths (Low, Medium, High).
Performance
Non-clinical bench testing and a clinical reader study were performed. Bench testing compared DLIR images to ASiR-V reconstructed images using identical raw datasets focusing on low contrast detectability, image noise, spatial resolution, artifact suppression, and other image quality metrics. A clinical study with 40 retrospective cases evaluated by 6 board certified radiologists showed DLIR produced diagnostic quality images with significantly better subjective image quality than ASiR-V. The device passed design control testing with no additional hazards identified.
Predicate Devices
No predicate devices specified
Device Timeline
- 1
Submission
12/21/2021
1 month - 2
FDA Approval
2/18/2022
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