  Deep learning-based Generative Models have the potential to convert
low-resolution CT images into high-resolution counterparts without long
acquisition times and increased radiation exposure in thin-slice CT imaging.
However, procuring appropriate training data for these Super-Resolution (SR)
models is challenging. Previous SR research has simulated thick-slice CT images
from thin-slice CT images to create training pairs. However, these methods
either rely on simplistic interpolation techniques that lack realism or
sinogram reconstruction, which require the release of raw data and complex
reconstruction algorithms. Thus, we introduce a simple yet realistic method to
generate thick CT images from thin-slice CT images, facilitating the creation
of training pairs for SR algorithms. The training pairs produced by our method
closely resemble real data distributions (PSNR=49.74 vs. 40.66, p$<$0.05). A
multivariate Cox regression analysis involving thick slice CT images with lung
fibrosis revealed that only the radiomics features extracted using our method
demonstrated a significant correlation with mortality (HR=1.19 and HR=1.14,
p$<$0.005). This paper represents the first to identify and address the
challenge of generating appropriate paired training data for Deep
Learning-based CT SR models, which enhances the efficacy and applicability of
SR models in real-world scenarios.
