Title :
Multimodality Bayesian algorithm for image reconstruction in positron emission tomography
Author :
Sastry, S. ; VanMeter, J.W. ; Carson, Richard E.
Author_Institution :
Div. of Comput. Res. & Technol., Nat. Inst. of Health, Bethesda, MD, USA
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
Keywords :
Bayes methods; algorithm theory; image reconstruction; medical image processing; positron emission tomography; PET image pixels; PET image reconstruction; PET scanner physical model; anatomical information; assigned tissue composition; maximum a posteriori estimation; medical diagnostic imaging; multimodality Bayesian algorithm; segmented magnetic resonance images; spatial smoothing; Aging; Bayesian methods; Detectors; Image reconstruction; Image segmentation; Laboratories; Neuroscience; Physics computing; Pixel; Positron emission tomography;
Conference_Titel :
Nuclear Science Symposium and Medical Imaging Conference Record, 1995., 1995 IEEE
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-3180-X
DOI :
10.1109/NSSMIC.1995.501912
