Semi-supervised spatial-frequency transformer for metal artifact reduction in maxillofacial CT and evaluation with intraoral scan.

Journal: European journal of radiology
Published Date: Mar 31, 2025

Abstract

PURPOSE: To develop a semi-supervised domain adaptation technique for metal artifact reduction with a spatial-frequency transformer (SFTrans) model (Semi-SFTrans), and to quantitatively compare its performance with supervised models (Sup-SFTrans and ResUNet) and traditional linear interpolation MAR method (LI) in oral and maxillofacial CT.

Authors

  • Yuanlin Li
    Department of Oral Maxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai 200011, China.
  • Chenglong Ma
    Chohotech Inc, Hangzhou, China.
  • Zilong Li
    School of Information Engineering, Xuzhou University of Technology, Xuzhou 221018, China.
  • Zhen Wang
    Department of Otolaryngology, Longgang Otolaryngology hospital & Shenzhen Key Laboratory of Otolaryngology, Shenzhen Institute of Otolaryngology, Shenzhen, Guangdong, China.
  • Jing Han
    Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education; School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China.
  • Hongming Shan
  • Jiannan Liu
    Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

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