Dynamic PET Image Reconstruction Using a Spatial-Temporal Edge-Preserving Prior

Dynamic positron emission tomography (PET) imaging provides important quantitative information of physiological and biochemical processes in humans and animals. However, due to short-time acquisitions to obtain a time sequence of images for parametric imaging, the signal-to-noise ratio of measurement data in each time frame is often very low, which leads the dynamic PET image reconstruction to be a challenging task. And some noticeable errors are inevitable transferred to the voxel-wise kinetic parameter imaging from the associative noisy TAC measurements. To tackle this problem, maximum a posteriori (MAP) statistical reconstruction methods are widely used by incorporating some prior information. Conventional priors focus on local neighborhoods in individual image frames and subsequently penalize inter-voxel intensity differences through different penalty functions such as the quadratic membrane smoothing prior and non-quadratic edge-preserving prior, failing to explore the temporal information of dynamic PET data. In this paper, we design a spatial-temporal edge-preserving (STEP) prior model under the framework of bilateral filter by considering both the spatial local neighborhoods and the temporal kinetic information. Experimental results via a computer simulation study demonstrate that the present dynamic PET reconstruction method with the STEP prior can achieve noticeable gains than the conventional Huber prior in term of signal-to-noise and bias-variance evaluations for the parametric images.