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.
翻译:本文提出了一种创新的、不受监督的超分辨率(SR)方法,用于将临床CT进行超常反应,使其进入微型CT的分辨率($=m$(MU$CT) 。准确的非侵入性诊断肺癌通常使用临床CT数据。由于临床CT的分辨率限制(大约0.5美元 0.5 乘以0.5 乘以0.5 毫米=3美元),很难获得足够的病理学信息,如在alveoli 一级入侵地区。另一方面, $mu$xCT扫描使获得大量分辨率高得多的肺部血清样本(50 乘50 乘以50 mu=m=m%3美元或更高)。因此,临床CT的超解性诊断通常使用肺癌数据(0.5 0.5 乘以0. 0. 0. 0. 0. 0. 毫米=0. 毫米 毫米=3美元) 。 典型的SR(HR) 的定量图像需要匹配的临床CT CT 和 0. 0. CT DNA 机能 改进 。