  Deep neural networks (DNN) are commonly used to denoise and sharpen X-ray
computed tomography (CT) images with the goal of reducing patient X-ray dosage
while maintaining reconstruction quality. However, naive application of
DNN-based methods can result in image texture that is undesirable in clinical
applications. Alternatively, generative adversarial network (GAN) based methods
can produce appropriate texture, but naive application of GANs can introduce
inaccurate or even unreal image detail. In this paper, we propose a texture
matching generative adversarial network (TMGAN) that enhances CT images while
generating an image texture that can be matched to a target texture. We use
parallel generators to separate anatomical features from the generated texture,
which allows the GAN to be trained to match the desired texture without
directly affecting the underlying CT image. We demonstrate that TMGAN generates
enhanced image quality while also producing image texture that is desirable for
clinical application.
