Deep learning for dentomaxillofacial cone-beam computed tomography image quality enhancement: A pilot study
Deep learning-based super-resolution demonstrates considerable potential for enhancing CBCT image quality, especially in scenarios involving blur and downscaling. These results suggest possible applications in low-dose imaging protocols and improved clinical decision-making.
Authors
- Mina Iranparvar Alamdari (ORCID: https://orcid.org/0000-0002-9113-1932)
- Falk Schwendicke (ORCID: https://orcid.org/0000-0003-1223-1669)
- Ali Nazari
- Ruben Pauwels (ORCID: https://orcid.org/0000-0002-9462-7546)
- Hossein Mohammad‐Rahimi (ORCID: https://orcid.org/0000-0002-4971-5926)
- Parisa Motie (ORCID: https://orcid.org/0000-0001-5598-3575)
- Reza Abbasi (ORCID: https://orcid.org/0009-0003-0552-3820)
- Mehdi Hosseinzadeh (ORCID: https://orcid.org/0000-0001-9488-369X)
- Seyed Mohammad Yousef Najafi
Institutions
- Isfahan University of Medical Sciences (IR)
- Sharif University of Technology (IR)
- Aarhus University (DK)
- Shahid Beheshti University of Medical Sciences (IR)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Imaging Science in Dentistry
- Published
- 2025-01-01
- DOI
- https://doi.org/10.5624/isd.20250023
- Citations
- 1
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36