[Reconstruction of dynamic positron emission tomographic images by exploiting low rank and sparse penalty].

Objective: To propose a new method for dynamic positron emission tomographic (PET) image reconstruction using low rank and sparse penalty (L&S).

Methods: The L&S reconstruction model was established and the split Bregman method was used to solve the optimal cost function. The one-tissue compartment model was used to simulate a set of PET 82Rb myocardial perfusion image.