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 $\pm$ 1218.37.

翻译：[translated abstract in Chinese] 图像到图像翻译在医学领域广受欢迎，用于将图像从一个域转换到另一个域。通过域变换进行医学图像合成具有显著优势，能够增强给定类别图像有限的医学数据集。从学习角度来看，该过程通过内在拓宽模型对更多样化视觉数据的暴露，使其学习更泛化的特征，从而提升模型的数据导向鲁棒性。在生成额外神经影像的情况下，获取不可识别的医学数据并扩充较小的带注释数据集尤为有利。本研究提出开发一种CycleGAN模型，用于将神经影像从一个场强转换到另一个场强（例如，从3特斯拉转换到1.5特斯拉）。该模型与基于DCGAN架构的模型进行了对比。CycleGAN能够以合理的精度生成合成图像和重建图像。从源域（3特斯拉）到目标域（1.5特斯拉）的映射函数实现了最优性能，平均PSNR值为25.69 ± 2.49 dB，平均MAE值为2106.27 ± 1218.37。