Development and evaluation of a deep learning model to reduce exomass-related metal artefacts in cone-beam CT: an ex vivo study using porcine mandibles.

Journal: Dento maxillo facial radiology
PMID: 39589904

Abstract

OBJECTIVES: To develop and evaluate a deep learning (DL) model to reduce metal artefacts originating from the exomass in cone-beam CT (CBCT) of the jaws.

Authors

  • Matheus L Oliveira
    Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil.
  • Susanne Schaub
    Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, 4123, Switzerland.
  • Dorothea Dagassan-Berndt
    Department of Oral Health & Medicine,University Center for Dental Medicine Basel UZB, University of Basel, Basel, 4058, Switzerland.
  • Florentin Bieder
    Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, 4123, Switzerland.
  • Philippe C Cattin
    Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland.
  • Michael M Bornstein
    Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland.

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