Generative adversarial networks (GANs) have shown remarkable success in generating realistic images and are increasingly used in medical imaging for image-to-image translation tasks. However, GANs tend to suffer from a frequency bias towards low frequencies, which can lead to the removal of important structures in the generated images. To address this issue, we propose a novel frequency-aware image-to-image translation framework based on the supervised RegGAN approach, which we call fRegGAN. The framework employs a K-space loss to regularize the frequency content of the generated images and incorporates well-known properties of MRI K-space geometry to guide the network training process. By combine our method with the RegGAN approach, we can mitigate the effect of training with misaligned data and frequency bias at the same time. We evaluate our method on the public BraTS dataset and outperform the baseline methods in terms of both quantitative and qualitative metrics when synthesizing T2-weighted from T1-weighted MR images. Detailed ablation studies are provided to understand the effect of each modification on the final performance. The proposed method is a step towards improving the performance of image-to-image translation and synthesis in the medical domain and shows promise for other applications in the field of image processing and generation.

翻译：生成对抗网络在生成逼真图像方面取得了显著成功，并越来越多地应用于医学图像翻译任务中。然而，生成对抗网络存在偏向低频的频率偏差问题，这可能导致生成图像中重要结构的丢失。为解决这一问题，我们基于有监督的RegGAN方法提出了一种新颖的频率感知图像翻译框架，命名为fRegGAN。该框架采用K空间损失对生成图像的频率内容进行正则化，并利用MRI K空间几何的已知特性来引导网络训练过程。通过将我们的方法与RegGAN相结合，可同时缓解使用未对齐数据训练带来的影响以及频率偏差问题。我们在公开的BraTS数据集上评估了该方法，在从T1加权MR图像合成T2加权图像时，无论定量指标还是定性指标均优于基线方法。通过详细消融研究，我们理解了各改进对最终性能的影响。所提出的方法致力于提升医学领域图像翻译与合成的性能，并在图像处理与生成的其他应用中展现出潜力。