Direct 4D Patlak Reconstruction in Dynamic FDG PET Imaging with Population-based Input Function

In dynamic PET, direct parametric reconstruction is advantageous over conventional kinetic modeling method in noise reduction. Direct reconstruction with accurate input function has been investigated. However, clinical application of dynamic PET is limited by technical complication in measuring the input function from patient blood samples. Population-based input function (PBIF) is an alternative approach and has been demonstrated to be feasible in regions of interest (ROI) -based kinetic analysis. The objective of this work is to investigate the performance of direct 4D Patlak reconstruction in dynamic FDG PET imaging with PBIF.A direct 4D Patlak reconstruction strategy with PBIF was proposed. A nested technique was used to accelerate the convergence. Dynamic images and unscaled parametric images were generated from direct Patlak reconstruction with unscaled PBIF. The scaling factors estimated from dynamic images were used after the direct reconstruction due to the linear relationship between dynamic images and kinetic parameters. Phantom simulation, preclinical scans and ongoing clinical scans were performed to compare the indirect and direct reconstruction with PBIF. The Ki images generated from direct analysis were less noisy than those generated from indirect analysis. In phantom studies, the directly reconstructed images at convergence shows less noise and comparable bias. For both simulated and experimental data, similar scaled input functions were obtained in the indirect and direct analysis with PBIF. With shorter acquisition time, more iterations are required to reach convergence, while the advantage of direct reconstruction is more remarkable.Our proposed direct reconstruction strategy with PBIF was demonstrated to be feasible. Compared with Ki images generated from indirect analysis, directly reconstructed Ki images showed better performance in terms of less variation and comparable bias.