Share Email Print

Proceedings Paper

Low-dose CT via deep CNN with skip connection and network-in-network
Author(s): Chenyu You; Linfeng Yang; Yi Zhang; Ge Wang
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

A major challenge in computed tomography (CT) is how to minimize patient radiation exposure without compromising image quality and diagnostic performance. The use of deep convolutional (Conv) neural networks for noise reduction in Low-Dose CT (LDCT) images has recently shown a great potential in this important application. In this paper, we present a highly efficient and effective neural network model for LDCT image noise reduction. Specifically, to capture local anatomical features we integrate Deep Convolutional Neural Networks (CNNs) and Skip connection layers for feature extraction. Also, we introduce parallelized 1 × 1 CNN, called Network in Network, to lower the dimensionality of the output from the previous layer, achieving faster computational speed at less feature loss. To optimize the performance of the network, we adopt a Wasserstein generative adversarial network (WGAN) framework. Quantitative and qualitative comparisons demonstrate that our proposed network model can produce images with lower noise and more structural details than state-of-the-art noise-reduction methods.

Paper Details

Date Published: 10 September 2019
PDF: 6 pages
Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111131W (10 September 2019); doi: 10.1117/12.2534960
Show Author Affiliations
Chenyu You, Stanford Univ. (United States)
Linfeng Yang, Stanford Univ. (United States)
Yi Zhang, Sichuan Univ. (China)
Ge Wang, Rensselaer Polytechnic Institute (United States)

Published in SPIE Proceedings Vol. 11113:
Developments in X-Ray Tomography XII
Bert Müller; Ge Wang, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?