Penalized Parametric PET Image Estimation Using Local Linear Fitting

Abstract

Positron emission tomography (PET) is a powerful imaging tool for quantifying physiological information. Particularly, parametric PET imaging has been increasingly investigated because the voxel-wise spatial distribution of tracer kinetics can be achieved, which is suitable for identifying heterogeneous tracer uptake. Our goal is to provide a fast and accurate parametric imaging method by utilizing the full-dose image. In this article, we propose a novel penalized parametric imaging in which dynamic images are reconstructed frame by frame, and then voxel-wise kinetic parameters are iteratively estimated by a kinetic-domain penalty using a local linear fitting (LLF) that utilizes a full-dose static image as a prior in the penalty. In our optimization, a dual-domain approximation is derived to efficiently split suboptimizations for image and kinetic parameter separately, which can simplify suboptimizations including the nonlinear fitting of the two-tissue compartment model (2-TCM). In addition, we further investigate the feasibility of image-driven input function extraction by comparing with the standard blood sampling. We evaluated the performance of the proposed method using simulation and patient study with [18F]MK6240 tau scan data of two subjects, and results are compared with the conventional method and the Logan graphical model. We demonstrate that the proposed method outperforms the conventional parametric imaging methods.