Unpaired low-dose computed tomography image denoising using a progressive cyclical convolutional neural network.

Journal: Medical physics
Published Date: Mar 10, 2023

Abstract

BACKGROUND: Reducing the radiation dose from computed tomography (CT) can significantly reduce the radiation risk to patients. However, low-dose CT (LDCT) suffers from severe and complex noise interference that affects subsequent diagnosis and analysis. Recently, deep learning-based methods have shown superior performance in LDCT image-denoising tasks. However, most methods require many normal-dose and low-dose CT image pairs, which are difficult to obtain in clinical applications. Unsupervised methods, on the other hand, are more general.

Authors

  • Qing Li
    Department of Internal Medicine, University of Michigan Ann Arbor, MI 48109, USA.
  • Runrui Li
    College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
  • Saize Li
    College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
  • Tao Wang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yubin Cheng
    College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
  • Shuming Zhang
  • Wei Wu
    Department of Pharmacy, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Juanjuan Zhao
    Guanlan Networks (Hangzhou) Co, Ltd, Hangzhou, Zhejiang, China.
  • Yan Qiang
    College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
  • Long Wang

Keywords

External Resources

Popular Topics
Recent Journals