Deep learning super-resolution for dental CBCT using micro-CT reference and edge loss function
Micro-CT-guided super-resolution, particularly with edge optimisation, substantially improved CBCT diagnostic utility in endodontics CLINICAL SIGNIFICANCE: The super-resolution models investigated in the present work achieve acceptable results in enhancing the resolution of the roots of teeth in clinical CBCT scans. Edge-aware super-resolution deep learning models hold promise for clinical dental imaging.
Authors
- Sajitha Kalathingal (ORCID: https://orcid.org/0000-0003-1241-6516)
- Guozhong Zhou (ORCID: https://orcid.org/0009-0001-9112-6631)
- T. Malik (ORCID: https://orcid.org/0009-0008-5551-9949)
- Bowen Shen (ORCID: https://orcid.org/0000-0002-2298-6857)
- Weiwei Wu (ORCID: https://orcid.org/0000-0001-7541-0547)
- Franklin R. Tay (ORCID: https://orcid.org/0000-0003-3769-0626)
- Surong Chen
- Yan Yang (ORCID: https://orcid.org/0000-0001-6501-1366)
- Jingzhi Ma (ORCID: https://orcid.org/0009-0003-9367-9082)
- Jingyu Hu (ORCID: https://orcid.org/0000-0002-4566-5327)
- Pan Chen (ORCID: https://orcid.org/0009-0001-8184-1279)
Institutions
- Augusta University (US)
- Augusta University Health (US)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Journal of Dentistry
- Published
- 2025-11-02
- DOI
- https://doi.org/10.1016/j.jdent.2025.106209
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00