  Noise and artifacts are intrinsic to low dose CT (LDCT) data acquisition, and
will significantly affect the imaging performance. Perfect noise removal and
image restoration is intractable in the context of LDCT due to the statistical
and technical uncertainties. In this paper, we apply the generative adversarial
network (GAN) framework with a visual attention mechanism to deal with this
problem in a data-driven/machine learning fashion. Our main idea is to inject
visual attention knowledge into the learning process of GAN to provide a
powerful prior of the noise distribution. By doing this, both the generator and
discriminator networks are empowered with visual attention information so they
will not only pay special attention to noisy regions and surrounding structures
but also explicitly assess the local consistency of the recovered regions. Our
experiments qualitatively and quantitatively demonstrate the effectiveness of
the proposed method with clinic CT images.
