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Noise and artifacts are intrinsic to low dose CT (LDCT) data acquisition, andwill significantly affect the imaging performance. Perfect noise removal andimage restoration is intractable in the context of LDCT due to the statisticaland technical uncertainties. In this paper, we apply the generative adversarialnetwork (GAN) framework with a visual attention mechanism to deal with thisproblem in a data-driven/machine learning fashion. Our main idea is to injectvisual attention knowledge into the learning process of GAN to provide apowerful prior of the noise distribution. By doing this, both the generator anddiscriminator networks are empowered with visual attention information so theywill not only pay special attention to noisy regions and surrounding structuresbut also explicitly assess the local consistency of the recovered regions. Ourexperiments qualitatively and quantitatively demonstrate the effectiveness ofthe proposed method with clinic CT images.

Visual Attention Network for Low Dose CT