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

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

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. The L&S reconstruction method was compared with maximum likelihood expectation maximization (MLEM) method, low-rank penalty method and sparse penalty method.

The L&S reconstruction method had the smallest MSE and well maintained the feature information. The polar map created by L&S method was the most similar with the reference actual polar map.

L&S reconstruction method is better than the other three methods in both visual and quantitative analysis of the PET images.