Multi-sequence MR image-based synthetic CT generation using a generative adversarial network for head and neck MRI-only radiotherapy.

Journal: Medical physics
Published Date: Feb 26, 2020

Abstract

PURPOSE: The purpose of this study is to investigate the effect of different magnetic resonance (MR) sequences on the accuracy of deep learning-based synthetic computed tomography (sCT) generation in the complex head and neck region.

Authors

  • Mengke Qi
    Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.
  • Yongbao Li
  • Aiqian Wu
    Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.
  • Qiyuan Jia
    Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.
  • Bin Li
    Department of Magnetic Resonance Imaging (MRI), Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
  • Wenzhao Sun
    Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, Guangdong, China.
  • Zhenhui Dai
    Department of Radiation Oncology, Guangdong Province Traditional Medical Hospital, Guangzhou, 510000, Guangdong, China.
  • Xingyu Lu
    Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.
  • Linghong Zhou
    Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.
  • Xiaowu Deng
    Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, Guangdong, China.
  • Ting Song

Keywords

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