Multimodality Bayesian algorithm for image reconstruction in positron emission tomography

Abstract

The use of anatomical information to improve the quality of reconstructed images in Positron Emission Tomography (PET) has been extensively studied. A common strategy has been to include spatial smoothing within boundaries defined from the anatomical data. The authors present an alternate method for the incorporation of anatomical information into PET image reconstruction, wherein they use segmented Magnetic Resonance (MR) images to assign tissue composition to PET image pixels. The authors model the image as a sum of activities for each tissue type, weighted by the assigned tissue composition. The reconstruction is performed as a maximum a posteriori (MAP) estimation of the activities of each tissue type. Two prior functions, defined for tissue type activities, are considered. The algorithm is tested in realistic simulations employing a full physical model of the PET scanner.