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，并被越来越多地用于医学成像中的图像到图像翻译任务。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，我们基于监督式RegGAN方法提出了一种新颖的频率感知图像到图像翻译框架，称为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空间几何学的已知特性来指导网络训练过程。通过将我们的方法与RegGAN相结合，可同时减轻错位数据训练和频率偏差带来的影响。我们在公开的BraTS数据集上进行了评估，在从T1加权MRI图像合成T2加权图像时，我们的方法在定量和定性指标上均优于基线方法。为了解每个改进对最终性能的影响，我们提供了详细的消融研究。所提出的方法为提升医学领域图像到图像翻译与合成的性能迈出了重要一步，并在图像处理与生成领域的其他应用中展现出前景。