Dynamic PET image reconstruction using deep physiology prior

Dynamic positron emission tomography (PET) imaging can provide information about metabolic changes over time, and is widely used in clinical diagnosis and cancer treatment. However, the existing deep learning methods for PET image reconstruction mainly focus on the static mapping paradigm between sinogram and radioactivity concentration distribution, which ignores the inherent dynamic activation process of tracers. In this paper, we establish a physiological model based deep learning framework for dynamic PET image reconstruction using deep physiology prior. First, the objective functions of our physiological model are combined with static mapping and the dynamic activation process of tracers. Then, a data-driven Adaptive Kalman Inspired Network (AKIN) is adopted to solve the proposed objective functions. Specifically, the AKIN consists of three components: a Prediction Net is employed for directly predicting the prior estimation; a Projection Net is employed for predicting the current estimation of the observations based on the prior estimation, furthermore, a Kalman Gain Net (KNet) is employed for adaptively learning the gain coefficient. The experiment of simulation data demonstrates that the proposed method has substantial noise reduction in temporal and spatial domains, outperforming other methods like maximum likelihood expectation maximization, kernel expectation maximization method and DeepPET.