Improving CBCT quality to CT level using deep learning with generative adversarial network.

Journal: Medical physics
Published Date: May 14, 2021

Abstract

PURPOSE: To improve image quality and computed tomography (CT) number accuracy of daily cone beam CT (CBCT) through a deep learning methodology with generative adversarial network.

Authors

  • Yang Zhang
    Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Ning Yue
    Department of Radiation Oncology, Rutgers-The State University of New Jersey, New Brunswick, NJ, USA.
  • Min-Ying Su
    Department of Radiological Sciences, University of California, Irvine, CA 92697, USA.
  • Bo Liu
    Wuhan United Imaging Healthcare Surgical Technology Co., Ltd., Wuhan, China.
  • Yi Ding
    Clinical and Research Center of AIDS, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
  • Yongkang Zhou
    Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China.
  • Hao Wang
    Department of Cardiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Yu Kuang
    Department of Integrated Health Sciences, University of Nebraska, Las Vegas, NV, USA.
  • Ke Nie
    Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States.

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