Metal artefact reduction in the oral cavity using deep learning reconstruction algorithm in ultra-high-resolution computed tomography: a phantom study.

Journal: Dento maxillo facial radiology
Published Date: Apr 29, 2021

Abstract

OBJECTIVES: This study aimed to improve the impact of the metal artefact reduction (MAR) algorithm for the oral cavity by assessing the effect of acquisition and reconstruction parameters on an ultra-high-resolution CT (UHRCT) scanner.

Authors

  • Yuki Sakai
    Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
  • Erina Kitamoto
    Department of Oral and Maxillofacial Radiology, Faculty of Dental Science, Kyushu University, Fukuoka, Japan.
  • Kazutoshi Okamura
    Department of Oral and Maxillofacial Radiology, Faculty of Dental Science, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan. okamura@rad.dent.kyushu-u.ac.jp.
  • Masato Tatsumi
    Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
  • Takashi Shirasaka
    Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
  • Ryoji Mikayama
    Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
  • Masatoshi Kondo
    Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
  • Hiroshi Hamasaki
    Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
  • Toyoyuki Kato
    Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
  • Kazunori Yoshiura
    Department of Oral and Maxillofacial Radiology, Faculty of Dental Science, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.

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