Optimization-derived blood input function using a kernel method and its evaluation with total-body PET for brain parametric imaging.

Abstract

The conventional SIME approach estimates an input function using a joint estimation together with kinetic parameters by fitting time activity curves from multiple regions of interests (ROIs). The input function is commonly parameterized with a highly nonlinear model which is difficult to estimate. The proposed kernel SIME method exploits the CA ID-IF as a priori information via a kernel representation to stabilize the SIME approach. The unknown parameters are linear and thus easier to estimate. The proposed method was evaluated using 18F-fluorodeoxyglucose studies with both computer simulations and 20 human-subject scans acquired on the uEXPLORER scanner. The effect of the number of ROIs on kernel SIME was also explored. The estimated OD-IF by kernel SIME showed a good match with the reference input function and provided more accurate estimation of kinetic parameters for both simulation and human-subject data. The kernel SIME led to the highest correlation coefficient (R=0.97) and the lowest mean absolute error (MAE=10.5%) compared to using the CA ID-IF (R=0.86, MAE=108.2%) and conventional SIME (R=0.57, MAE=78.7%) in the human-subject evaluation. Adding more ROIs improved the overall performance of the kernel SIME method. The proposed kernel SIME method shows promise to provide an accurate estimation of the blood input function and kinetic parameters for brain PET parametric imaging.