  For whole-body (WB) kinetic modeling based on a typical PET scanner, a
multi-pass multi-bed scanning protocol is necessary given the limited axial
field-of-view. Such a protocol introduces loss of early-dynamics in
time-activity curves (TACs) and sparsity in TAC measurements, inducing
uncertainty in parameter estimation when using least-squares estimation (LSE)
(i.e., common standard) especially for kinetic microparameters. We present a
method to reliably estimate microparameters, enabling accurate parametric
imaging, on regular-axial field-of-view PET scanners. Our method, denoted
parameter combination-driven estimation (PCDE), relies on generation of
reference truth TAC database, and subsequently selecting, the best parameter
combination as the one arriving at TAC with highest total similarity score
(TSS), focusing on general image quality, overall visibility, and tumor
detectability metrics. Our technique 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), and 2) use of
multiple aspects of TACs in selection of best fits. To evaluate our method
against conventional LSE, we plotted tradeoff curves for noise and bias. In
addition, the overall SNR and spatial noise were calculated and compared.
Furthermore, CNR and TBR were also calculated. We also tested our proposed
method on patient data (18F-DCFPyL PET scans) to further verify clinical
applicability. Significantly improved general image quality performance was
verified in microparametric images (e.g. noise-bias performance). The overall
visibility and tumor detectability were also improved. Finally, for our patient
studies, improved overall visibility and tumor detectability were demonstrated
in micoparametric images, compared to use of conventional parameter estimation.
