Estimating live-time for new PET scanner configurations

Abstract

We present the derivation of a live-time model for predicting count rates in computer simulations of PET scanners. Computer models are frequently used to investigate new PET scanner configurations, but they typically do not account for the count losses caused by scanner-specific electronics and processing. The live-time fraction depends strongly on the photon flux incident on the detector. We modeled the live-time of a clinical PET scanner by relating measured and simulated single photon fluxes. Our model used data from a specific scanner, but the approach is generally applicable.We applied the live-time model to partial collimation on a PET scanner; in particular, a scanner with septa positioned between every third detector ring ("2.7D" acquisition mode). The photon flux was measured and simulated for conventional acquisition modes (2D, 3D), and simulated for partial collimation (2.7D). These data were used in the model to predict live-time for partial collimation. The model was then validated against measurements in 2.7D mode. At low activity the model was very accurate at predicting the live-time fraction. Over-estimation of count-rates by the simulations lead to an uncertainly in the live-model. The uncertainty increased with activity concentration, reaching 0.9% and 2.2% at 20 kBq/mL for singles and coincidence live-time, respectively. When applied to 2.7D mode, the model predicted coincidence live-time accurate to 2.2% and 10% at 5 kBq/ mL and 20 kBq/mL in the phantom, respectively. The 2.7D singles-counting live-time was predicted to within 0.2% of the measured value for up to 20 kBq/mL in the phantom.

Effective count rates for PET scanners with reduced and extended axial field of view

“…We used the SimSET Monte Carlo simulation package (Lewellen et al 1998), and data modeling techniques previously employed and validated for estimating relative global count rates on different PET scanner configurations (MacDonald et al 2008(MacDonald et al , 2007. We simulated true, scattered (Compton and coherent), and random coincidence counts detected by a PET scanner based on a clinical scanner, and also with reduced and extended AFOV.…”