title: A study of non-invasive Patlak quantification for whole-body dynamic FDG-PET studies of mice.
abstract: Physiological changes in dynamic PET images can be quantitatively estimated by kinetic modeling technique. The process of PET quantification usually requires an input function in the form of a plasma-time activity curve (PTAC), which is generally obtained by invasive arterial blood sampling. However, invasive arterial blood sampling poses many challenges especially for small animal studies, due to the subjects' limited blood volume and small blood vessels. A simple non-invasive quantification method based on Patlak graphical analysis (PGA) has been recently proposed to use a reference region to derive the relative influx rate for a target region without invasive blood sampling, and evaluated by using the simulation data of human brain FDG-PET studies. In this study, the non-invasive Patlak (nPGA) method was extended to whole-body dynamic small animal FDG-PET studies. The performance of nPGA was systematically investigated by using experimental mouse studies and computer simulations. The mouse studies showed high linearity of relative influx rates between the nPGA and PGA for most pairs of reference and target regions, when an appropriate underlying kinetic model was used. The simulation results demonstrated that the accuracy of the nPGA method was comparable to that of the PGA method, with a higher reliability for most pairs of reference and target regions. The results proved that the nPGA method could provide a non-invasive and indirect way for quantifying the FDG kinetics of tumor in small animal studies.
authors.affiliation: School of Medicine, Shanghai Jiaotong University, Shanghai, China.
authors.forename: Xiujuan
authors.initials: X
authors.lastname: Zheng
authors.forename: Lingfeng
authors.initials: L
authors.lastname: Wen
authors.forename: Shu-Jung
authors.initials: SJ
authors.lastname: Yu
authors.forename: Sung-Cheng
authors.initials: SC
authors.lastname: Huang
authors.forename: David Dagan
authors.initials: DD
authors.lastname: Feng