Deep Learning Reconstruction in CT Abdominal Imaging
Deep Learning Reconstruction (DLR) is rapidly becoming the standard for CT image reconstruction, superseding older hybrid iterative reconstruction (HIR) methods. DLR offers critical benefits, including lower noise, reduced radiation dose, a more natural image texture, and, often, improved spatial resolution. These technical advantages significantly enhance the visualization of clinically-relevant structures and lesions across multiple abdominal organs (e.g., liver, spleen, kidneys, pancreas, musculature, and vessels) and are particularly valuable for patients with high BMI.
This session will cover the core technological principles and clinical evidence supporting DLR, using practical clinical cases to clearly demonstrate its superiority over other reconstruction techniques.
Workshop recording from ECR 2026
Dr. Ewoud Smit
Radiologist
Radboud University Medical Center Nijmegen, the NetherlandsCourse Information
At the end of this lecture, delegates will:
- Understand the technological and working principles of deep learning reconstruction
- Discuss how deep learning reconstruction can help improve image quality and reduce dose in abdominal CT imaging.
- Discuss the behavior of deep learning reconstruction in ultra-low dose settings.
- Experience with real patient cases the differences between deep learning reconstruction and hybrid iterative reconstruction
This educational talk was created on 4th March 2026. All information contained in this session was correct at the time of distribution.
Disclaimer: Appearing on the Canon Medical Academy does not represent a commercial partnership or interest from the speaker. The views herein do not represent the views of Canon Medical Systems Ltd.
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