Graphical analysis is employed in the research setting to provide quantitative estimation of PET tracer kinetics from
dynamic images at a single bed. Recently, we proposed a multi-bed dynamic acquisition framework enabling clinically
feasible whole-body parametric PET imaging by employing post-reconstruction parameter estimation. In addition, by
incorporating linear Patlak modeling within the system matrix, we enabled direct 4D reconstruction in order to
effectively circumvent noise amplification in dynamic whole-body imaging. However, direct 4D Patlak reconstruction
exhibits a relatively slow convergence due to the presence of non-sparse spatial correlations in temporal kinetic analysis.
In addition, the standard Patlak model does not account for reversible uptake, thus underestimating the influx rate Ki. We
have developed a novel whole-body PET parametric reconstruction framework in the STIR platform, a widely employed
open-source reconstruction toolkit, a) enabling accelerated convergence of direct 4D multi-bed reconstruction, by
employing a nested algorithm to decouple the temporal parameter estimation from the spatial image update process, and
b) enhancing the quantitative performance particularly in regions with reversible uptake, by pursuing a non-linear
generalized Patlak 4D nested reconstruction algorithm.
A set of published kinetic parameters and the XCAT phantom were employed for the simulation of dynamic multi-bed
acquisitions. Quantitative analysis on the Ki images demonstrated considerable acceleration in the convergence of the nested 4D whole-body Patlak algorithm. In addition, our simulated and patient whole-body data in the postreconstruction
domain indicated the quantitative benefits of our extended generalized Patlak 4D nested reconstruction for
tumor diagnosis and treatment response monitoring.