  As one of the most commonly ordered imaging tests, computed tomography (CT)
scan comes with inevitable radiation exposure that increases the cancer risk to
patients. However, CT image quality is directly related to radiation dose, thus
it is desirable to obtain high-quality CT images with as little dose as
possible. CT image denoising tries to obtain high dose like high-quality CT
images (domain X) from low dose low-quality CTimages (domain Y), which can be
treated as an image-to-image translation task where the goal is to learn the
transform between a source domain X (noisy images) and a target domain Y (clean
images). In this paper, we propose a multi-cycle-consistent adversarial network
(MCCAN) that builds intermediate domains and enforces both local and global
cycle-consistency for edge denoising of CT images. The global cycle-consistency
couples all generators together to model the whole denoising process, while the
local cycle-consistency imposes effective supervision on the process between
adjacent domains. Experiments show that both local and global cycle-consistency
are important for the success of MCCAN, which outperformsCCADN in terms of
denoising quality with slightly less computation resource consumption.
