Anatomical information based panel PET image reconstruction using nonlocal means regularization

In recent years, panel PET is an interesting field in medical imaging. But the major obstacle of panel PET is the limited view problem. Images reconstructed by traditional algorithms will suffer serious artifacts. Time-of-flight (TOF) information can be incorporated into the image reconstruction to remove the artifacts. However, the best system timing resolution available in current commercial PET scanners is about 500 ps, which is not precise enough to achieve satisfactory images. In this paper, a list mode reconstruction algorithm coupled with non-local means regularizer was formulated to improve the panel PET image quality. It incorporates the anatomical information into the non-local means regularizer and modifies the reconstructed image while it is updating. The results show that our proposed method could remove the distortions effectively without TOF information.