Arterial spin labeling (ASL) is a relatively new MRI technique that can measure cerebral blood flow, which is of great importance for the diagnosis of dementia diseases. Besides, this valuable imaging modality does not need exogenous tracers and has no radiation, which makes it favorable for elder patients. However, ASL data does lack in many contemporary image-based dementia diseases datasets, which include popular ADNI-1/GO/2/3 datasets. In order to supplement the valuable ASL data, a new Generative adversarial network (GAN)-based model is proposed to synthesize ASL images in this study. This new model is unique, as the popular variational auto-encoder (VAE) has been utilized as the generator of the GAN-based model. Hence, a new VAE-GAN architecture is introduced in this study. In order to demonstrate its superiority, dozens of experiments have been conducted. Experimental results demonstrate that, this new VAE-GAN model is superior to other state-of-the-art ASL image synthesis methods, and the accuracy improvement after incorporating synthesized ASL images from the new model can be as high as 42.41% in dementia diagnosis tasks.

动脉自旋标记（ASL）是一种相对较新的 MRI 技术，可以测量脑血流量，对痴呆症的诊断具有重要意义。此外，这种有价值的成像方式不需要外源示踪剂，也没有辐射，这使得它有利于老年患者。然而，许多当代基于图像的痴呆疾病数据集确实缺乏 ASL 数据，其中包括流行的 ADNI-1/GO/2/3 数据集。为了补充有价值的 ASL 数据，本研究提出了一种新的基于生成对抗网络 (GAN) 的模型来合成 ASL 图像。这个新模型是独一无二的，因为流行的变分自动编码器 (VAE) 已被用作基于 GAN 的模型的生成器。因此，本研究引入了一种新的 VAE-GAN 架构。为了彰显其优越性，已经进行了数十次实验。实验结果表明，这种新的 VAE-GAN 模型优于其他最先进的 ASL 图像合成方法，并且将新模型的合成 ASL 图像合并到痴呆症诊断中的准确率提高高达 42.41%任务。