Quantitative whole-body parametric PET imaging incorporating a generalized Patlak model

Recently, we proposed a dynamic multi-bed PET imaging and analysis framework enabling clinically feasible whole-body parametric imaging. The standard Patlak linear graphical analysis allows for efficient modeling of whole-body tracer kinetics by directly estimating the uptake rate constant K_i and blood distribution volume V, based on a common two-compartment kinetic model. However, this model does not account for reversible uptake (e.g. dephosphorylation in FDG), thus underestimating K_i in this case, a finding observed in a number of published FDG or similar tracer studies. We propose a novel generalized PET parametric imaging framework enabling truly quantitative whole-body Patlak imaging including in regions exhibiting reversibility. For this purpose: a) an extended non-linear Patlak model has been utilized, enriched with the net efflux rate constant k_loss, (b) a basis function method has been applied to linearize the estimation process through a computationally efficient algorithm, and (c) a hybrid K_i imaging technique is introduced based on the Patlak correlation-coefficient to enhance robustness to noise. Our evaluation included both simulated and real subject clinical studies. A set of published kinetic parameter values and the XCAT phantom were employed to generate realistic simulation data for 2 dynamic 7-bed acquisition protocols (0-45min and 30-90min post-injection). Quantitative analysis on the K_i images suggests superior quantitative performance of the generalized Patlak in comparison to the standard Patlak imaging in both acquisitions, even when k_loss is comparable to K_i. In addition, validation on three dynamic whole-body patient datasets demonstrated clinical feasibility and increased focal uptake with potential for enhanced diagnosis and treatment response monitoring.