Deep learning improves image quality and radiomics reproducibility for high-speed four-dimensional computed tomography reconstruction.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PMID: 35257852

Abstract

BACKGROUND AND PURPOSE: Hybrid iterative reconstruction (HIR) is the most commonly used algorithm for four-dimensional computed tomography (4DCT) reconstruction due to its high speed. However, the image quality is worse than that of model-based iterative reconstruction (MIR). Different reconstruction methods affect the stability of radiomics features. Herein, we developed a deep learning method to improve the quality and radiomics reproducibility of the high-speed reconstruction.

Authors

  • Bining Yang
    National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
  • Xinyuan Chen
    National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
  • Siqi Yuan
    School of Arts and Communication, Beijing Normal University, Beijing, China.
  • Yuxiang Liu
    National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; School of Physics and Technology, Wuhan University, Wuhan, China.
  • Jianrong Dai
    National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Kuo Men
    State Key Laboratory of Advanced Materials for Smart Sensing, GRINM Group Co., Ltd., Beijing 100088, China.

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