Key points are not available for this paper at this time.
Normalization in positron emission tomography (PET) is the process of ensuring that all lines of response joining detectors in coincidence have the same effective sensitivity. In three-dimensional (3D) PET, normalization is complicated by the presence of a large proportion of scattered coincidences, and by the fact that cameras operating in 3D mode encounter a very wide range of count-rates. In this work a component-based normalization model is presented which separates the normalization of true and scattered coincidences and accounts for variations in normalization effects with count-rate. The effects of the individual components in the model on reconstructed images are investigated, and it is shown that only a subset of these components has a significant effect on reconstructed image quality.
Badawi et al. (1999) studied this question.