Impact of time-of-flight image reconstruction in PET parametric imaging

Abstract

Kinetic parameter estimation in dynamic PET imaging requires reconstruction of multiple time frames. Due to the limited counting statistics and reduced signal-to-noise ratio (SNR) in each frame, parametric maps suffer from reduced accuracy and precision. Therefore image reconstruction strategies improving upon the SNR are particularly important in the context of parametric imaging. Time of flight image reconstruction has been shown to improve the SNR and increase the effective sensitivity. However so far the benefit of TOF has only been demonstrated in static imaging applications with potentially substantial benefits when used in dynamic pharmacokinetic imaging applications. Using traditional dynamic 3D as well as direct 4D image reconstruction algorithms we evaluate the benefit of TOF on kinetic parameter estimation using various TOF resolutions, kinetic models and count levels. Initial data suggest that both bias and variance in the kinetic parameters are reduced, with improvements depending on the kinetic model and becoming more significant at increased TOF resolutions. Incorporating TOF within direct 4D image reconstruction and combining the respecting SNR gains results in substantial improvements in parametric maps compared to traditional post reconstruction kinetic analysis.