Novel Method to Estimate Kinetic Microparameters from Dynamic Whole-Body Imaging in Regular-Axial Field-of-View PET Scanners

For whole-body (WB) kinetic modeling based on a typical PET scanner, a multipass multibed scanning protocol is necessary because of the limited axial field of view. Such a protocol introduces loss of early dynamics of the time-activity curve (TAC) and sparsity in TAC measurements, inducing uncertainty in parameter estimation when using prevalent least squares estimation (LSE) (i.e., common standard) especially for kinetic microparameters. We developed and investigated a method to estimate microparameters enabling parametric imaging, by focusing on general image qualities, overall visibility, and tumor detectability. Our method, denoted parameter combination-driven estimation (PCDE), has two distinctive characteristics: 1) improved probability of having one-on-one mapping between early and late dynamics in TACs (the former missing from typical protocols) at the cost of the precision of the estimated parameter, and 2) utilization of multiple aspects of TAC in selection of best fits. To compare the general image quality of the two methods, we plotted tradeoff curves for noise and bias. We also evaluated the impact of different iteration numbers of the OSEM reconstruction algorithm on the tradeoff curves. In addition, for overall visibility, the overall signal-to-noise ratio (SNR) and spatial noise were calculated and compared. Furthermore, the contrast-to-noise ratio (CNR) and relative error of the tumor-to-background ratio were calculated. Furthermore, we implemented and tested the proposed method on patient datasets to further verify clinical applicability. Overall, improved general image quality was verified in microparametric images (i.e., reduction in overall NRMSE). The overall visibility and tumor detectability were also improved. For our patient study, improved overall visibility and tumor detectability were demonstrated in micoparametric images.