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.

翻译：基于深度学习的生成模型能够在无需长采集时间和增加辐射暴露的情况下，将低分辨率CT图像转换为高分辨率图像。然而，为这些超分辨率模型获取合适的训练数据具有挑战性。此前超分辨率研究通过从薄层CT图像模拟厚层CT图像来构建训练对，但这些方法要么依赖缺乏真实性的简单插值技术，要么需要原始数据发布和复杂重建算法的正弦图重建方法。因此，我们提出一种简单而真实的方法，从薄层CT图像生成厚层CT图像，为超分辨率算法构建训练对。该方法生成的训练对与真实数据分布高度接近（PSNR=49.74 vs. 40.66，p<0.05）。针对肺纤维化厚层CT图像的多变量Cox回归分析表明，仅使用本文方法提取的影像组学特征与死亡率显著相关（HR=1.19和HR=1.14，p<0.005）。本文首次识别并解决了基于深度学习的CT超分辨率模型训练对数据生成的挑战，从而提升了超分辨率模型在实际场景中的有效性和适用性。