In this work, we developed a novel text-guided image synthesis technique which could generate realistic tau PET images from textual descriptions and the subject's MR image. The generated tau PET images have the potential to be used in examining relations between different measures and also increasing the public availability of tau PET datasets. The method was based on latent diffusion models. Both textual descriptions and the subject's MR prior image were utilized as conditions during image generation. The subject's MR image can provide anatomical details, while the text descriptions, such as gender, scan time, cognitive test scores, and amyloid status, can provide further guidance regarding where the tau neurofibrillary tangles might be deposited. Preliminary experimental results based on clinical [18F]MK-6240 datasets demonstrate the feasibility of the proposed method in generating realistic tau PET images at different clinical stages.

翻译：在本研究中，我们开发了一种新颖的文本引导图像合成技术，能够根据文本描述和受试者的MR图像生成逼真的tau PET图像。生成的tau PET图像有望用于检验不同测量指标之间的关系，并提高tau PET数据集的公开可用性。该方法基于潜在扩散模型，在图像生成过程中同时利用文本描述和受试者的MR先验图像作为条件。受试者的MR图像可提供解剖细节，而文本描述（如性别、扫描时间、认知测试评分和淀粉样蛋白状态）则可进一步指导tau神经原纤维缠结可能沉积的位置。基于临床[18F]MK-6240数据集的初步实验结果证明了该方法在不同临床阶段生成逼真tau PET图像的可行性。