Nonlocal total variation based dynamic PET image reconstruction with low-rank constraints

Abstract

Faced with the low signal-to-noise ratio of measurements due to the short data acquisition time between adjacent frames, reconstruction techniques for dynamic positron emission tomography (dPET) images have been under development for decades. In this paper, a novel reconstruction method of dPET is presented. We first enforce a joint low-rank model to simultaneously exploit the spatiotemporal priors. Considering that PET images suffer from noise actually, a nonlocal total variation (TV) constraint is incorporated in the low-rank framework because it is able to preserve fine structures and remove the staircase effect by using image redundancies. We solve the objective function by using the augmented Lagrangian multiplier method with variable splitting. We then validate the effectiveness of the proposed method with Monte Carlo-simulated datasets and real patient data. The proposed approach gives superior reconstruction results compared with the maximum likelihood-expectation maximization and low-rank TV methods.