A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning.

Journal: Japanese journal of radiology
PMID: 38536558

Abstract

PURPOSE: Radiotherapy planning incorporating functional lung images has the potential to reduce pulmonary toxicity. Free-breathing 4DCT-derived ventilation image (CTVI) may help quantify lung function. This study introduces a novel deep-learning model directly translating planning CT images into CTVI. We investigated the accuracy of generated images and the impact on functional avoidance planning.

Authors

  • Zhen Hou
    Institute of Medical Information & Library, Chinese Academy of Medical Sciences, Beijing, China.
  • Youyong Kong
    Lab of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China. kongyouyong@seu.edu.cn.
  • Junxian Wu
    School of Computer Science and Engineering, Southeast University, Nanjing, 210000, Jiangsu, China.
  • Jiabing Gu
    School of Medicine and Life Sciences, University of Jinan Shandong Academy of Medical Sciences, Jinan, Shandong, China.
  • Juan Liu
    Key State Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan 430072, PR China. Electronic address: liujuan@whu.edu.cn.
  • Shanbao Gao
    The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210000, Jiangsu, China.
  • Yicai Yin
    The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210000, Jiangsu, China.
  • Ling Zhang
  • Yongchao Han
    The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210000, Jiangsu, China.
  • Jian Zhu
  • Shuangshuang Li
    Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen 518060, China. Electronic address: lishuangshuang2016@email.szu.edu.cn.

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