Metal artifact reduction on cervical CT images by deep residual learning.

Journal: Biomedical engineering online
Published Date: Nov 27, 2018

Abstract

BACKGROUND: Cervical cancer is the fifth most common cancer among women, which is the third leading cause of cancer death in women worldwide. Brachytherapy is the most effective treatment for cervical cancer. For brachytherapy, computed tomography (CT) imaging is necessary since it conveys tissue density information which can be used for dose planning. However, the metal artifacts caused by brachytherapy applicators remain a challenge for the automatic processing of image data for image-guided procedures or accurate dose calculations. Therefore, developing an effective metal artifact reduction (MAR) algorithm in cervical CT images is of high demand.

Authors

  • Xia Huang
    College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China.
  • Jian Wang
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Fan Tang
    Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China.
  • Tao Zhong
    Guangdong Provincial Key Laboratory of Chiral Molecule and Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University Guangzhou 510006 P. R. China wengj2@mail.sysu.edu.cn lugui@mail.sysu.edu.cn.
  • Yu Zhang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.

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