PET-Diffusion: Unsupervised PET Enhancement Based on the Latent Diffusion Model

Positron emission tomography (PET) is an advanced nuclear imaging technique with an irreplaceable role in neurology and oncology studies, but its accessibility is often limited by the radiation hazards inherent in imaging. To address this dilemma, PET enhancement methods have been developed by improving the quality of low-dose PET (LPET) images to standard-dose PET (SPET) images. However, previous PET enhancement methods rely heavily on the paired LPET and SPET data which are rare in clinic. Thus, in this paper, we propose an unsupervised PET enhancement (uPETe) framework based on the latent diffusion model, which can be trained only on SPET data. Specifically, our SPET-only uPETe consists of an encoder to compress the input SPET/LPET images into latent representations, a latent diffusion model to learn/estimate the distribution of SPET latent representations, and a decoder to recover the latent representations into SPET images. Moreover, from the theory of actual PET imaging, we improve the latent diffusion model of uPETe by 1) adopting PET image compression for reducing the computational cost of diffusion model, 2) using Poisson diffusion to replace Gaussian diffusion for making the perturbed samples closer to the actual noisy PET, and 3) designing CT-guided cross-attention for incorporating additional CT images into the inverse process to aid the recovery of structural details in PET. With extensive experimental validation, our uPETe can achieve superior performance over state-of-the-art methods, and shows stronger generalizability to the dose changes of PET imaging. The code of our implementation is available at https://github.com/jiang-cw/PET-diffusion .