  This paper proposes a novel, unsupervised super-resolution (SR) approach for
performing the SR of a clinical CT into the resolution level of a micro CT
($\mu$CT). The precise non-invasive diagnosis of lung cancer typically utilizes
clinical CT data. Due to the resolution limitations of clinical CT (about $0.5
\times 0.5 \times 0.5$ mm$^3$), it is difficult to obtain enough pathological
information such as the invasion area at alveoli level. On the other hand,
$\mu$CT scanning allows the acquisition of volumes of lung specimens with much
higher resolution ($50 \times 50 \times 50 \mu {\rm m}^3$ or higher). Thus,
super-resolution of clinical CT volume may be helpful for diagnosis of lung
cancer. Typical SR methods require aligned pairs of low-resolution (LR) and
high-resolution (HR) images for training. Unfortunately, obtaining paired
clinical CT and $\mu$CT volumes of human lung tissues is infeasible.
Unsupervised SR methods are required that do not need paired LR and HR images.
In this paper, we create corresponding clinical CT-$\mu$CT pairs by simulating
clinical CT images from $\mu$CT images by modified CycleGAN. After this, we use
simulated clinical CT-$\mu$CT image pairs to train an SR network based on
SRGAN. Finally, we use the trained SR network to perform SR of the clinical CT
images. We compare our proposed method with another unsupervised SR method for
clinical CT images named SR-CycleGAN. Experimental results demonstrate that the
proposed method can successfully perform SR of clinical CT images of lung
cancer patients with $\mu$CT level resolution, and quantitatively and
qualitatively outperformed conventional method (SR-CycleGAN), improving the
SSIM (structure similarity) form 0.40 to 0.51.
