Self-supervised denoising of projection data for low-dose cone-beam CT.

Journal: Medical physics
Published Date:

Abstract

BACKGROUND: Convolutional neural networks (CNNs) have shown promising results in image denoising tasks. While most existing CNN-based methods depend on supervised learning by directly mapping noisy inputs to clean targets, high-quality references are often unavailable for interventional radiology such as cone-beam computed tomography (CBCT).

Authors

  • Kihwan Choi
  • Seung Hyoung Kim
    Department of Radiology, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Sungwon Kim
    Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, Republic of Korea.