Direct parametric imaging of reversible tracers using partial dynamic data

Abstract

Direct parametric estimation in positron emission tomography (PET) has been developed to compute the voxel-based kinetic parameters in the reconstruction process, obtaining more accurate physiological information of tracer uptake. Although the direct parametric imaging can achieve accurate kinetic analysis, the long acquisition time is still painful, particularly for sick and old patients. To address this issue, we explore the feasibility to estimate voxel-based kinetic parameters using partial dynamic data, specifically the first and last 10 minutes of a typical dynamic scan. To improve the quality of the direct parametric imaging with partial dynamic data, we propose a novel penalized direct estimation method containing log-likelihood, ridge regression and patch-based joint similarity penalty of kinetic images, in which the structural similarity weight of K1 can be used for improving the features in other kinetic images (k2 ∼ k4). In our optimization, the alternating direction method of multipliers (ADMM) with a separable quadratic surrogate (SQS) is exploited. We validate the proposed method using a brain phantom, and demonstrate that the proposed method outperforms the conventional direct estimation methods even using partial dynamic data.