Spatiotemporal scatter models for dynamic SPECT

Dynamic single photon emission computed tomography (SPECT) data acquisition and quantitative kinetic data analysis provide unique information that can enable improved discrimination between healthy and diseased tissue, compared to conventional static imaging. Previously, we modeled time courses of activity within segmented SPECT volumes of interest and developed algorithms to estimate kinetic model parameters directly from dynamic projection data. We now propose two methods for modeling and estimating scatter jointly with tracer kinetic models. The goal is to reduce bias in kinetic parameter estimates by properly accounting for scatter. These methods exploit the fact that the scatter distribution from a volume of interest is spatially smooth and has the same temporal kinetics as unscattered events from the volume. The first method treats scattered events as if they originate from scatter sites distributed in image space. For each volume of interest, the distribution of scatter sites is modeled with a smooth spatial function and events from this effective scatter source distribution (ESSD) are forward-projected along with unscattered events from the volume. Thus, the projector only needs to model non-scatter effects. The second method bypasses modeling an ESSD in image space and simply models the spatial projection of scatter to be a smooth function in projection space. Computer simulations of a dynamic ^99mTc-teboroxime cardiac SPECT scan show that unscattered and scattered events from the blood pool, myocardium, and liver have distinct spatiotemporal signatures and that it is feasible to jointly estimate scatter amplitudes and time-activity curves for volumes of interest directly from projection data. This suggests that joint estimation of scatter, blood input function, and compartmental model parameters is a well-posed problem and can lead to reduced bias in kinetic parameter estimates