Multimodality Bayesian algorithm for image reconstruction in positron emission tomography: a tissue composition model

IEEE Transactions on Medical Imaging
S Sastry, R E Carson

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. 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.

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Citations

Oct 9, 2009·International Journal of Biomedical Imaging·S BenameurJ-P Soucy
Feb 12, 2009·Journal of Digital Imaging·John KornakMichael W Weiner
Oct 11, 2011·Physics in Medicine and Biology·Jinxiu Cheng-Liao, Jinyi Qi
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Aug 17, 2019·Frontiers in Neuroscience·Yuankai Zhu, Xiaohua Zhu
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Feb 17, 2015·Physics in Medicine and Biology·Lijun LuArman Rahmim
Jun 16, 2021·Medical Physics·Zhaoheng XieJinyi Qi

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