Dual-domain fusion deep convolutional neural network for low-dose CT denoising.

Journal: Journal of X-ray science and technology
Published Date: Jan 1, 2023

Abstract

BACKGROUND: In view of the underlying health risks posed by X-ray radiation, the main goal of the present research is to achieve high-quality CT images at the same time as reducing x-ray radiation. In recent years, convolutional neural network (CNN) has shown excellent performance in removing low-dose CT noise. However, previous work mainly focused on deepening and feature extraction work on CNN without considering fusion of features from frequency domain and image domain.

Authors

  • Zhiyuan Li
    School of Clinical Medicine, General Hospital of Ningxia Medical University, Yinchuan, China.
  • Yi Liu
    Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China.
  • Yang Chen
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.
  • Huazhong Shu
    Lab of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China. shu.list@seu.edu.cn.
  • Jing Lu
    Department of Nephrology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Zhiguo Gui
    State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China.

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