Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine
  PET Reconstruction
Zeyu Han Yuhan Wang Luping Zhou Peng Wang Binyu Yan Jiliu Zhou Yan Wang Dinggang Shen
To obtain high-quality positron emission tomography (PET) scans while
reducing radiation exposure to the human body, various approaches have been
proposed to reconstruct standard-dose PET (SPET) images from low-dose PET
(LPET) images. One widely adopted technique is the generative adversarial
networks (GANs), yet recently, diffusion probabilistic models (DPMs) have
emerged as a compelling alternative due to their improved sample quality and
higher log-likelihood scores compared to GANs. Despite this, DPMs suffer from
two major drawbacks in real clinical settings, i.e., the computationally
expensive sampling process and the insufficient preservation of correspondence
between the conditioning LPET image and the reconstructed PET (RPET) image. To
address the above limitations, this paper presents a coarse-to-fine PET
reconstruction framework that consists of a coarse prediction module (CPM) and
an iterative refinement module (IRM). The CPM generates a coarse PET image via
a deterministic process, and the IRM samples the residual iteratively. By
delegating most of the computational overhead to the CPM, the overall sampling
speed of our method can be significantly improved. Furthermore, two additional
strategies, i.e., an auxiliary guidance strategy and a contrastive diffusion
strategy, are proposed and integrated into the reconstruction process, which
can enhance the correspondence between the LPET image and the RPET image,
further improving clinical reliability. Extensive experiments on two human
brain PET datasets demonstrate that our method outperforms the state-of-the-art
PET reconstruction methods. The source code is available at
\url{https://github.com/Show-han/PET-Reconstruction}.