MRCON-Net: Multiscale reweighted convolutional coding neural network for low-dose CT imaging.

Journal: Computer methods and programs in biomedicine
Published Date: May 8, 2022

Abstract

BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) has become increasingly important for alleviating X-ray radiation damage. However, reducing the administered radiation dose may lead to degraded CT images with amplified mottle noise and nonstationary streak artifacts. Previous studies have confirmed that deep learning (DL) is promising for improving LDCT imaging. However, most DL-based frameworks are built intuitively, lack interpretability, and suffer from image detail information loss, which has become a general challenging issue.

Authors

  • Jin Liu
    School of Computer Science and Engineering, Central South University, Changsha, China.
  • Yanqin Kang
    College of Computer and Information, Anhui Polytechnic University, Wuhu, China; Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing, China.
  • Zhenyu Xia
    College of Computer and Information, Anhui Polytechnic University, Wuhu, China.
  • Jun Qiang
    College of Computer and Information, Anhui Polytechnic University, Wuhu, China.
  • Junfeng Zhang
    Medcine College of Pingdingshan University, Pingdingshan 476000, China.
  • Yikun Zhang
    Laboratory of Image Science and Technology, Southeast University, Nanjing, Jiangsu, China.
  • Yang Chen
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.

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