Anatomy-guided brain PET imaging incorporating a joint prior model

Abstract

We proposed a maximum a posterior (MAP) framework for incorporating information from coregistered anatomical images into PET image reconstruction through a novel anato-functional joint prior. The characteristic of the utilized hyperbolic potential function is determinate by the voxel intensity differences within the anatomical image, while the penalization is computed based on voxel intensity differences in reconstructed PET images. Using realistic simulated 18 FDG PET scan data, we optimized the performance of the proposed MAP reconstruction with the joint prior (JP-MAP), and compared its performance with conventional 3D MLEM and 3D MAP reconstructions. The proposed JP-MAP reconstruction algorithm resulted in quantitatively enhanced reconstructed images, as demonstrated in extensive FDG PET simulation study. The proposed method was also tested on a 20 min Florbetapir patient study performed on the highresolution research tomograph. It was shown to outperform conventional methods in visual as well as quantitative accuracy assessment (in terms of regional noise versus activity value performance). The JP-MAP method was also compared with another MR-guided MAP reconstruction method, utilizing the Bowsher prior, and was seen to result in some quantitative enhancements especially in the case of MR-PET mis-registrations, and a definitive improvement in computational performance.