TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent
  Diffusion Models
Se-In Jang Cristina Lois Emma Thibault J. Alex Becker Yafei Dong Marc D. Normandin Julie C. Price Keith A. Johnson Georges El Fakhri Kuang Gong
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.