Randoms variance reduction in 3D PET

In positron emission tomography (PET), random coincidence events must be re moved from the measured signal in order to obtain quantitatively accurate d ata. The most widely implemented technique for estimating the number of ran dom coincidences on a particular line of response is the delayed coincidenc e channel method. Estimates obtained in this way are subject to Poisson noi se, which then propagates into the final image when the estimates are subtr acted from the prompt signal. However, this noise may be reduced if varianc e reduction techniques similar to those used in normalization of PET detect ors are applied to the randoms estimates prior to use. We have investigated the effects of randoms variance reduction on noise-equ ivalent count (NEC) rates on a whole-body PET camera operating in 3D mode. NEC rates were calculated using a range of phantoms representative of situa tions that might be encountered clinically. We have also investigated the properties of three randoms variance reductio n methods (based on algorithms previously used for normalization) in terms of their systematic accuracy and their variance reduction efficacy, both in phantom studies and in vivo. Those algorithms investigated that do not mak e assumptions about the spatial distribution of random coincidences give th e best estimates of the randoms distribution. With the camera used, which h as a limited axial extent (10.8 cm) and a large ring diameter (102 cm), the gains in image signal-to-noise ratio obtained with this technique ranged f rom similar to 5% to similar to 15%, depending on object size, activity dis tribution and the amount of activity in the field of view. Larger gains wou ld be expected if this technique were to be employed on cameras of greater axial extent and smaller ring diameter.