Count rate dependence of local signal-to-noise ratio in positron emission tomography

The count rate dependence of the signal-to-noise ratio (SNR) in positron emission tomography (PET) data is frequently characterized by a global quantity such as the noise equivalent count rate (NECR). However, it has not been clear to what extent global optimization of data variance (e.g., through varying injected dose or uptake period) also optimizes SNR in local regions of interest. In this paper, we present a new methodology for predicting the dependence of local image mean and variance on count rate that can be applied when an actual measurement at only a single count rate is available, as is frequently the case in human clinical PET scanning. We define local count rates and SNR based on the methods of Huesman and Alpert et al. We demonstrate that local trues and randoms count rates can be accurately modeled from global phantom count rate curves. Using these results, we compare local and global SNR count rate responses in phantom and human examples. Except in the vicinity of the bladder, we find only small differences between the local and global response curves, suggesting that global optimization of SNR or NECR is likely generally adequate for optimizing most clinical scanning protocols, at least with respect to injected dose.