Bidirectional Condition Diffusion Probabilistic Models for PET Image Denoising

Abstract

Low-count positron emission tomography (PET) imaging is an effective way to reduce the radiation risk of PET at the cost of a low-signal-to-noise ratio. Our study aims to denoise low-count PET images in an unsupervised mode since the mainstream methods rely on paired data, which is not always feasible in clinical practice. We adopt the diffusion probabilistic model in consideration of its strong generation ability. Our model consists of two stages. In the training stage, we learn a score function network via evidence lower-bound (ELBO) optimization. In the sampling stage, the trained score function and low-count image are employed to generate the corresponding high-count image under two handcrafted conditions. One is based on restoration in latent space, and the other is based on noise insertion in latent space. Thus, our model is named the bidirectional condition diffusion probabilistic model (BC-DPM). The Zubal phantom and real patient whole-body data are utilized to evaluate our model. The experiments show that our model achieves better performance in both qualitative and quantitative respects compared to several traditional and recently proposed learning-based methods.