Effects of temporal modeling on the statistical uncertainty of spatiotemporal distributions estimated directly from dynamic SPECT projections

Artifacts can result when reconstructing a dynamic image sequence from inconsistent single photon emission computed tomography (SPECT) projection data acquired by a slowly rotating gantry. The artifacts can lead to biases in kinetic parameters estimated from time-activity curves generated by overlaying volumes of interest on the images. To overcome these biases in conventional image based dynamic data analysis, we have been investigating the estimation of time-activity curves and kinetic model parameters directly from dynamic SPECT projection data by modeling the spatial and temporal distribution of the radiopharmaceutical throughout the projected field of view. In the present work, we perform Monte Carlo simulations to study the effects of the temporal modeling on the statistical variability of the reconstructed spatiotemporal distributions. The simulations utilize computationally efficient methods for fully four-dimensional (4-D) direct estimation of spatiotemporal distributions and their statistical uncertainties, using a spatial segmentation and temporal B-splines. The simulation results suggest that there is benefit in modeling higher orders of temporal spline continuity. In addition, the accuracy of the time modeling can be increased substantially without unduly increasing the statistical uncertainty, by using relatively fine initial time sampling to capture rapidly changing activity distributions.