Phantom-based performance comparison of two commercial deep learning CT reconstruction algorithms with super- and normal-resolution settings
The performance of a new deep learning super-resolution image reconstruction algorithm (SR-DLR) was compared to a normal-resolution (NR)-DLR algorithm using an image-quality phantom for an abdominal energy-integrating detector CT protocol. SR-DLR with a 1,024<sup>2</sup> matrix improved spatial resolution and detectability of simulated abdominal lesions compared to NR-DLR with a 512<sup>2</sup> matrix. Using SR-DLR, therefore, presents numerous prospects for improving abdominal CT images and a high potential for reducing the radiation doses.
Authors
- Djamel Dabli (ORCID: https://orcid.org/0000-0003-1003-1196)
- Maxime Pastor (ORCID: https://orcid.org/0009-0005-0156-6190)
- Joël Greffier (ORCID: https://orcid.org/0000-0002-1735-5129)
- C. Roy
- Jean-Paul Beregi
Institutions
- Université de Montpellier (FR)
- Hôpitaux Universitaires de Strasbourg (FR)
- Hôpital Civil, Strasbourg (FR)
- Université de Nîmes (FR)
Publication Details
- Journal
- European Radiology Experimental
- Published
- 2026-01-26
- DOI
- https://doi.org/10.1186/s41747-025-00670-2
- Primary Topic
- Radiation Dose and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00