This paper presents a super-resolution (SR) method with unpaired training dataset of clinical CT and micro CT volumes. For obtaining very detailed information such as cancer invasion from pre-operative clinical CT volumes of lung cancer patients, SR of clinical CT volumes to $\m$}CT level is desired. While most SR methods require paired low- and high- resolution images for training, it is infeasible to obtain paired clinical CT and {\mu}CT volumes. We propose a SR approach based on CycleGAN, which could perform SR on clinical CT into $\mu$CT level. We proposed new loss functions to keep cycle consistency, while training without paired volumes. Experimental results demonstrated that our proposed method successfully performed SR of clinical CT volume of lung cancer patients into $\mu$CT level.

中文翻译：

---

使用微 CT 体积使用改进的 CycleGAN 超分辨率临床 CT 体积

本文提出了一种超分辨率 (SR) 方法，该方法具有临床 CT 和微 CT 体积的未配对训练数据集。为了从肺癌患者的术前临床 CT 体积中获得非常详细的信息，例如癌症侵袭，需要将临床 CT 体积的 SR 提高到 $\m$}CT 水平。虽然大多数 SR 方法需要成对的低分辨率和高分辨率图像进行训练，但获得成对的临床 CT 和 {\mu}CT 体积是不可行的。我们提出了一种基于 CycleGAN 的 SR 方法，该方法可以将临床 CT 上的 SR 执行到 $\mu$CT 级别。我们提出了新的损失函数来保持循环一致性，同时在没有配对卷的情况下进行训练。实验结果表明，我们提出的方法成功地将肺癌患者的临床 CT 体积进行了 SR 到 $\mu$CT 级别。