GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands.
Division of Image Processing, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands.
Sensors (Basel). 2023 Jan 17;23(3):1089. doi: 10.3390/s23031089.
Research exploring CycleGAN-based synthetic image generation has recently accelerated in the medical community due to its ability to leverage unpaired images effectively. However, a commonly established drawback of the CycleGAN, the introduction of artifacts in generated images, makes it unreliable for medical imaging use cases. In an attempt to address this, we explore the effect of structure losses on the CycleGAN and propose a generalized frequency-based loss that aims at preserving the content in the frequency domain. We apply this loss to the use-case of cone-beam computed tomography (CBCT) translation to computed tomography (CT)-like quality. Synthetic CT (sCT) images generated from our methods are compared against baseline CycleGAN along with other existing structure losses proposed in the literature. Our methods () quantitatively and qualitatively improve over the baseline CycleGAN () across all investigated metrics and are more robust than existing methods. Furthermore, no observable artifacts or loss in image quality were observed. Finally, we demonstrated that sCTs generated using our methods have superior performance compared to the original CBCT images on selected downstream tasks.
研究探索基于 CycleGAN 的合成图像生成最近在医学领域得到了加速，因为它能够有效地利用未配对的图像。然而，CycleGAN 一个常见的缺陷是在生成的图像中引入伪影，这使得它在医学成像应用中不可靠。为了解决这个问题，我们探讨了结构损失对 CycleGAN 的影响，并提出了一种基于广义频率的损失，旨在保留频域中的内容。我们将该损失应用于锥束 CT (CBCT)到 CT 样质量的转换的用例。我们的方法生成的合成 CT (sCT)图像与基线 CycleGAN 以及文献中提出的其他现有结构损失进行了比较。我们的方法在所有研究的指标上都优于基线 CycleGAN ()，并且比现有方法更稳健。此外，没有观察到伪影或图像质量损失。最后，我们证明了在选定的下游任务中，使用我们的方法生成的 sCT 在性能上优于原始的 CBCT 图像。
