Development and evaluation of a deep learning model to reduce exomass-related metal artefacts in cone-beam CT: an <i>ex vivo</i> study using porcine mandibles
The developed DL model using porcine mandibles demonstrated promising performance in correcting exomass-related metal artefacts in CBCT, serving as a proof-of-principle for future applications of this approach.
Authors
- Stephanie K. Schaub (ORCID: https://orcid.org/0000-0002-5148-379X)
- Michael M. Bornstein (ORCID: https://orcid.org/0000-0002-7773-8957)
- Florentin Bieder (ORCID: https://orcid.org/0000-0001-9558-0623)
- Dorothea Dagassan‐Berndt (ORCID: https://orcid.org/0000-0003-1109-8763)
- Philippe Claude Cattin (ORCID: https://orcid.org/0000-0001-8785-2713)
- Matheus L. Oliveira (ORCID: https://orcid.org/0000-0002-8054-8759)
Institutions
- Universidade Estadual de Campinas (UNICAMP) (BR)
- University of Basel (CH)
Publication Details
- Journal
- Dentomaxillofacial Radiology
- Published
- 2024-11-26
- DOI
- https://doi.org/10.1093/dmfr/twae062
- Citations
- 8
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.95
Funders
- Fundação de Amparo à Pesquisa do Estado de São Paulo