Innovations in Chest CT: Clinical Impact of UHR Deep Learning Reconstruction and Photon Counting
CT has been evolving rapidly in the past few years. Advancements in hardware and software have made possible the achievement of both ultra-high resolution (UHR) and low noise, at no dose expense. This has important applications in chest CT, where low-dose and ultra-low-dose requirements are especially important, and where improvements in spatial resolution may yield more accurate detection and characterization of lung nodules and lung diseases.
This symposium will present the most recent advancements and technological innovations, with a focus on how UHR deep learning reconstruction and CZT-based photon-counting detector CT may impact the diagnosis and management of lung nodules and interstitial lung diseases.
Speaker
Prof. Annemiek Snoeckx
Radiologist
Head of Imaging Department, Antwerp University Hospital, Edegem, Belgium
Prof. Mathias Prokop
Professor of Radiology and Chairman of the Department of Medical Imaging
Radboud University Medical Center, Netherlands
Prof. Cornelia M. Schaefer-Prokop
Radiologist
Head of Imaging Department, Meander Medical Centre, Radboud University Medical Center, Amersfoort, NetherlandsCourse Information
At the end of this lecture, delegates will:
- Learn the latest technological advancements in CT hardware and software, including UHR deep learning reconstruction and CZT-based photon-counting detector CT
- Learn how deep learning can increase the spatial resolution of conventional CT and how it can potentially impact diagnostic performance
- Understand the benefits of CZT-based photon-counting detector CT coupled with deep learning reconstruction in lung diseases
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This educational talk was created on 14th June 2025. 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.
