TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models
This work addresses the need for increased public availability of tau PET datasets and examining relations between measures in neuroimaging, though it appears incremental as it applies existing diffusion models to a new domain.
The authors tackled the problem of generating realistic tau PET images from textual descriptions and MR images using a latent diffusion model, demonstrating feasibility across different clinical stages with preliminary results.
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.