Lag-Net: Lag correction for cone-beam CT via a convolutional neural network.

Journal: Computer methods and programs in biomedicine
PMID: 40233441

Abstract

BACKGROUND AND OBJECTIVE: Due to the presence of charge traps in amorphous silicon flat-panel detectors, lag signals are generated in consecutively captured projections. These signals lead to ghosting in projection images and severe lag artifacts in cone-beam computed tomography (CBCT) reconstructions. Traditional Linear Time-Invariant (LTI) correction need to measure lag correction factors (LCF) and may leave residual lag artifacts. This incomplete correction is partly attributed to the lack of consideration for exposure dependency.

Authors

  • Chenlong Ren
    Laboratory of Image science and Technology, School of computer science and Engneering, Southeast University, Nanjing, 210096, China. Electronic address: 220232340@seu.edu.cn.
  • Shengqi Kan
    Laboratory of Image science and Technology, School of computer science and Engneering, Southeast University, Nanjing, 210096, China.
  • Wenhui Huang
    Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, PR China; School of Medicine, South China University of Technology, Guangzhou, Guangdong, PR China.
  • Yan Xi
  • Xu Ji
    Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210096, China. Electronic address: xuji@seu.edu.cn.
  • Yang Chen
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.

Keywords

External Resources

Popular Topics
Recent Journals