Recently, deep learning approaches using CycleGAN have been demonstrated as a powerful unsupervised learning scheme for low-dose CT denoising. 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 requirements and the increase in the number of learnable parameters are major hurdles for successful CycleGAN training. Despite the use of additional generator, CycleGAN only translates between two domains, so it is not possible to investigate the intermediate level of denoising. To address this issue, here we propose a novel tunable CycleGAN architecture using a single 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. Furthermore, by interpolating the AdaIN codes between the two domains, we can investigate various intermediate level of denoising results. Experimental results show that the proposed method outperforms the previous CycleGAN approaches while using only about half the parameters, and provide tunable denoising features that may be potentially useful in clinical environment.

中文翻译：

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基于AdaIN的可调节CycleGAN用于高效无监督低剂量CT降噪

近来，使用CycleGAN的深度学习方法已被证明是用于低剂量CT降噪的强大的无监督学习方案。不幸的是，CycleGAN方法的主要局限之一是在训练阶段需要两个深度神经网络生成器，尽管在推理阶段仅使用其中一个。需要辅助辅助生成器来增强周期一致性，但是额外的内存需求和可学习参数的数量增加是成功进行CycleGAN训练的主要障碍。尽管使用了额外的生成器，CycleGAN仅在两个域之间进行转换，所以无法研究降噪的中间水平。为了解决这个问题，我们在这里提出一本小说可调的使用单个生成器的CycleGAN体系结构。特别是，使用自适应实例规范化（AdaIN）层实现单个生成器，以便可以将通过将低剂量CT图像转换为常规剂量CT图像的基线生成器切换为通过以下方式将高剂量转换为低剂量的生成器：只需更改AdaIN代码即可。多亏了共享的基准网络，最小化了额外的内存需求和权重增加，并且即使使用较小的训练数据也可以更稳定地进行训练。此外，通过在两个域之间插入AdaIN码，我们可以研究各种中间水平的降噪结果。实验结果表明，所提出的方法在仅使用大约一半参数的情况下，优于先前的CycleGAN方法，