Multimodality Bayesian algorithm for image reconstruction in positron emission tomography: a tissue composition model.
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. We present an alternative method for the incorporation of anatomical information into PET image reconstruction, in which we use segmented magnetic resonance (MR) images to assign tissue composition to PET image pixels. We 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.
Sastry
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Physical Sciences Laboratory, Division of Computer Research and Technology, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA.
Carson
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D000465:Algorithms; D000544:Alzheimer Disease / Q000000981:diagnostic imaging / Q000473:pathology; D001499:Bayes Theorem; D001921:Brain / Q000000981:diagnostic imaging / Q000473:pathology; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D014055:Tomography, Emission-Computed