Deep learning enables time-efficient soft tissue enhancement in CBCT: Proof-of-concept study for dentomaxillofacial applications.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PMID: 38016216

Abstract

PURPOSE: The use of iterative and deep learning reconstruction methods, which would allow effective noise reduction, is limited in cone-beam computed tomography (CBCT). As a consequence, the visibility of soft tissues is limited with CBCT. The study aimed to improve this issue through time-efficient deep learning enhancement (DLE) methods.

Authors

  • Sampo Ylisiurua
    Research Unit of Health Sciences and Technology, University of Oulu, Oulu 90220, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90220, Finland. Electronic address: sampo.ylisiurua@oulu.fi.
  • Annina Sipola
    Medical Research Center, University of Oulu and Oulu University Hospital, Oulu 90220, Finland; Department of Dental Imaging, Oulu University Hospital, Oulu 90220, Finland; Research Unit of Oral Health Sciences, University of Oulu, Oulu 90220, Finland. Electronic address: annina.sipola@oulu.fi.
  • Miika T Nieminen
    Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
  • Mikael A K Brix
    Research Unit of Health Sciences and Technology, University of Oulu, Oulu 90220, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90220, Finland; Medical Research Center, University of Oulu and Oulu University Hospital, Oulu 90220, Finland. Electronic address: mikael.brix@oulu.fi.

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