PET image reconstruction using kernel method

Image reconstruction from low-count PET projection data is challenging because the inverse problem is ill-posed. Inspired by the kernel methods for machine learning, this paper proposes a kernel based method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by maximum likelihood or penalized likelihood image reconstruction. Computer simulation shows that the proposed approach can achieve a higher signal-to-noise ratio for dynamic PET image reconstruction than the conventional maximum likelihood method with and without post-reconstruction denoising.