Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network

IEEE Trans Med Imaging. 2018 Jun;37(6):1358-1369. doi: 10.1109/TMI.2018.2823756.

Abstract

Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Neural Networks, Computer*
  • Phantoms, Imaging
  • Radiation Dosage*
  • Tomography, X-Ray Computed / methods*