  CT image denoising 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). Recently, cycle-consistent adversarial
denoising network (CCADN) has achieved state-of-the-art results by enforcing
cycle-consistent loss without the need of paired training data. Our detailed
analysis of CCADN raises a number of interesting questions. For example, if the
noise is large leading to significant difference between domain $X$ and domain
$Y$, can we bridge $X$ and $Y$ with an intermediate domain $Z$ such that both
the denoising process between $X$ and $Z$ and that between $Z$ and $Y$ are
easier to learn? As such intermediate domains lead to multiple cycles, how do
we best enforce cycle-consistency? Driven by these questions, we propose a
multi-cycle-consistent adversarial network (MCCAN) that builds intermediate
domains and enforces both local and global cycle-consistency. 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 outperforms the
state-of-the-art.
