BreathVisionNet: A pulmonary-function-guided CNN-transformer hybrid model for expiratory CT image synthesis.

Journal: Computer methods and programs in biomedicine
PMID: 39571504

Abstract

BACKGROUND AND OBJECTIVE: Chronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can effectively assess air trapping, aiding in disease diagnosis. However, due to concerns about radiation exposure and cost, expiratory CT is not routinely performed. Recent work on synthesizing expiratory CT has primarily focused on imaging features while neglecting patient-specific pulmonary function.

Authors

  • Tiande Zhang
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
  • Haowen Pang
    Department of Oncology, The Affiliated Hospital of Southwest Medical University, Sichuan, China.
  • Yanan Wu
    School of Physics and Optoelectronic Engineering, Ludong University, Yantai, Shandong 264025, China.
  • Jiaxuan Xu
    Faculty of Engineering and Applied Science, University of Regina, Regina, SK, S4S 0A2, Canada.
  • Lingkai Liu
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
  • Shang Li
    Harbin Institute of Technology, Harbin, China.
  • Shuyue Xia
  • Rongchang Chen
    Key Laboratory of Respiratory Disease of Shenzhen, Shenzhen Institute of Respiratory Disease, Shenzhen People's Hospital (Second Affiliated Hospital of Jinan University, First Affiliated Hospital of South University of Science and Technology of China), Shenzhen, China. Electronic address: chenrc@vip.163.com.
  • Zhenyu Liang
    State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. Electronic address: 490458234@qq.com.
  • Shouliang Qi
    Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Life Science Building, 500 Zhihui Street, Hun'nan District, Shenyang, 110169, China. qisl@bmie.neu.edu.cn.

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