IEEE Trans Med Imaging. 2020 Dec;39(12):4249-4261. doi: 10.1109/TMI.2020.3015379. Epub 2020 Nov 30.
Synthesizing a CT image from an available MR image has recently emerged as a key goal in radiotherapy treatment planning for cancer patients. CycleGANs have achieved promising results on unsupervised MR-to-CT image synthesis; however, because they have no direct constraints between input and synthetic images, cycleGANs do not guarantee structural consistency between these two images. This means that anatomical geometry can be shifted in the synthetic CT images, clearly a highly undesirable outcome in the given application. In this paper, we propose a structure-constrained cycleGAN for unsupervised MR-to-CT synthesis by defining an extra structure-consistency loss based on the modality independent neighborhood descriptor. We also utilize a spectral normalization technique to stabilize the training process and a self-attention module to model the long-range spatial dependencies in the synthetic images. Results on unpaired brain and abdomen MR-to-CT image synthesis show that our method produces better synthetic CT images in both accuracy and visual quality as compared to other unsupervised synthesis methods. We also show that an approximate affine pre-registration for unpaired training data can improve synthesis results.
从现有的磁共振成像（MRI）合成 CT 图像，最近已成为癌症患者放射治疗计划中的一个关键目标。CycleGAN 在无监督的 MRI-to-CT 图像合成方面取得了有前景的成果；然而，由于它们在输入和合成图像之间没有直接的约束，CycleGAN 不能保证这两个图像之间的结构一致性。这意味着在合成的 CT 图像中解剖几何形状可能会发生移位，这在给定的应用中显然是极不理想的结果。在本文中，我们通过定义基于模态独立邻域描述符的额外结构一致性损失，提出了一种用于无监督 MRI-to-CT 合成的结构约束 CycleGAN。我们还利用谱归一化技术来稳定训练过程，并使用自注意力模块来模拟合成图像中的远程空间依赖关系。在未配对的脑部和腹部 MRI-to-CT 图像合成方面的结果表明，与其他无监督合成方法相比，我们的方法在准确性和视觉质量方面都能生成更好的合成 CT 图像。我们还表明，对未配对的训练数据进行近似仿射预配准可以改善合成结果。
