  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 $1\times 1$ 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.

Computer Vision and Pattern Recognition;Machine Learning