  LDCT has drawn major attention in the medical imaging field due to the
potential health risks of CT-associated X-ray radiation to patients. Reducing
the radiation dose, however, decreases the quality of the reconstructed images,
which consequently compromises the diagnostic performance. Various deep
learning techniques have been introduced to improve the image quality of LDCT
images through denoising. GANs-based denoising methods usually leverage an
additional classification network, i.e. discriminator, to learn the most
discriminate difference between the denoised and normal-dose images and, hence,
regularize the denoising model accordingly; it often focuses either on the
global structure or local details. To better regularize the LDCT denoising
model, this paper proposes a novel method, termed DU-GAN, which leverages U-Net
based discriminators in the GANs framework to learn both global and local
difference between the denoised and normal-dose images in both image and
gradient domains. The merit of such a U-Net based discriminator is that it can
not only provide the per-pixel feedback to the denoising network through the
outputs of the U-Net but also focus on the global structure in a semantic level
through the middle layer of the U-Net. In addition to the adversarial training
in the image domain, we also apply another U-Net based discriminator in the
image gradient domain to alleviate the artifacts caused by photon starvation
and enhance the edge of the denoised CT images. Furthermore, the CutMix
technique enables the per-pixel outputs of the U-Net based discriminator to
provide radiologists with a confidence map to visualize the uncertainty of the
denoised results, facilitating the LDCT-based screening and diagnosis.
Extensive experiments on the simulated and real-world datasets demonstrate
superior performance over recently published methods both qualitatively and
quantitatively.
