Penalized direct estimation using partial dynamic data

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 between kinetic images can be used for improving the features of binding potential image. 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.


(a) Ground-truth, and binding potential images (k3/k4) using (b) the NLS after reconstruction (OSEM with Gaussian smoothing FWHM 4 mm), (c) penalized direct estimation using partial dynamic data, (d) penalized direct estimation using full dynamic data, (e) the proposed method using partial dynamic data and (f) the proposed method using full dynamic data.

K. Kim, Y. D. Son, G. El Fakhri and Q. Li, Penalized direct estimation using joint similarity of kinetic images with partial dynamic data, Society of Nuclear Medicine and Molecular Imaging (SNMMI), June. 2017


K. Kim, G. El Fakhri and Q. Li, Direct Parametric Imaging using Partial Dynamic Data, IEEE Nuclear Science Symposium and Medical Imaging Conference (NSSMIC), Nov. 2016.