Low-dose CT via deep CNN with skip connection and network-in-network

Chenyu You; Linfeng Yang; Yi Zhang; Ge Wang

doi:10.1117/12.2534960

10 September 2019 Low-dose CT via deep CNN with skip connection and network-in-network

Chenyu You, Linfeng Yang, Yi Zhang, Ge Wang

Proceedings Volume 11113, Developments in X-Ray Tomography XII; 111131W (2019) https://doi.org/10.1117/12.2534960
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

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.

Citation Download Citation

Chenyu You, Linfeng Yang, Yi Zhang, and Ge Wang "Low-dose CT via deep CNN with skip connection and network-in-network", Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111131W (10 September 2019); https://doi.org/10.1117/12.2534960

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