Image-to-image translation has gained popularity in the medical field to
transform images from one domain to another. Medical image synthesis via domain
transformation is advantageous in its ability to augment an image dataset where
images for a given class is limited. From the learning perspective, this
process contributes to data-oriented robustness of the model by inherently
broadening the model's exposure to more diverse visual data and enabling it to
learn more generalized features. In the case of generating additional
neuroimages, it is advantageous to obtain unidentifiable medical data and
augment smaller annotated datasets. This study proposes the development of a
CycleGAN model for translating neuroimages from one field strength to another
(e.g., 3 Tesla to 1.5). This model was compared to a model based on DCGAN
architecture. CycleGAN was able to generate the synthetic and reconstructed
images with reasonable accuracy. The mapping function from the source (3 Tesla)
to target domain (1.5 Tesla) performed optimally with an average PSNR value of
25.69 $\pm$ 2.49 dB and an MAE value of 2106.27 +/- 1218.37.

该研究提出了使用 CycleGAN 模型将神经图像从一种场强转化为另一种的方法，并将其与基于 DCGAN 结构的模型进行了比较，结果表明 CycleGAN 能够以较高的准确度生成合成和重构图像。

MRI 图像翻译的 CycleGAN 模型

CycleGAN Models for MRI Image Translation

Fusing structural-functional images of the brain has shown great potential to
analyze the deterioration of Alzheimer's disease (AD). However, it is a big
challenge to effectively fuse the correlated and complementary information from
multimodal neuroimages. In this paper, a novel model termed cross-modal
transformer generative adversarial network (CT-GAN) is proposed to effectively
fuse the functional and structural information contained in functional magnetic
resonance imaging (fMRI) and diffusion tensor imaging (DTI). The CT-GAN can
learn topological features and generate multimodal connectivity from multimodal
imaging data in an efficient end-to-end manner. Moreover, the swapping
bi-attention mechanism is designed to gradually align common features and
effectively enhance the complementary features between modalities. By analyzing
the generated connectivity features, the proposed model can identify AD-related
brain connections. Evaluations on the public ADNI dataset show that the
proposed CT-GAN can dramatically improve prediction performance and detect
AD-related brain regions effectively. The proposed model also provides new
insights for detecting AD-related abnormal neural circuits.

提出了一种名为交叉模态变换生成对抗网络（CT-GAN）的新模型，能够从功能磁共振成像（fMRI）和扩散张量成像（DTI）中有效地融合功能和结构信息，并通过分析生成的连接特征来识别与阿尔茨海默病相关的大脑连接。评估结果表明，该模型能够显著提高预测性能，有效地检测与阿尔茨海默病相关的大脑区域，并为检测与该病相关的异常神经回路提供了新的见解。