  Recently, deep learning approaches have been extensively studied for low-dose
CT denoising thanks to its superior performance despite the fast computational
time. In particular, cycleGAN has been demonstrated as a powerful unsupervised
learning scheme to improve the low-dose CT image quality without requiring
matched high-dose reference data. Unfortunately, one of the main limitations of
the cycleGAN approach is that it requires two deep neural network generators at
the training phase, although only one of them is used at the inference phase.
The secondary auxiliary generator is needed to enforce the cycle-consistency,
but the additional memory requirement and increases of the learnable parameters
are the main huddles for cycleGAN training. To address this issue, here we
propose a novel cycleGAN architecture using a single switchable generator. In
particular, a single generator is implemented using adaptive instance
normalization (AdaIN) layers so that the baseline generator converting a
low-dose CT image to a routine-dose CT image can be switched to a generator
converting high-dose to low-dose by simply changing the AdaIN code. Thanks to
the shared baseline network, the additional memory requirement and weight
increases are minimized, and the training can be done more stably even with
small training data. Experimental results show that the proposed method
outperforms the previous cycleGAN approaches while using only about half the
parameters.
