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Parameter estimation in Bayesian reconstruction of SPECT images: An aid in nuclear medicine diagnosis
Author(s) -
López Antonio,
Molina Rafael,
Katsaggelos Aggelos K.,
Rodriguez Antonio,
López José M.,
Llamas José M.
Publication year - 2004
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.20003
Subject(s) - hyperparameter , computer science , artificial intelligence , bayesian probability , prior probability , gaussian , single photon emission computed tomography , pattern recognition (psychology) , algorithm , machine learning , nuclear medicine , physics , medicine , quantum mechanics
Despite the adequacy of Bayesian methods to reconstruct nuclear medicine SPECT (single‐photon emission computed tomography) images, they are rarely used in everyday medical practice. This is primarily because of their computational cost and the need to appropriately select the prior model hyperparameters. We propose a simple procedure for the estimation of these hyperparameters and the reconstruction of the original image and test the procedure on both synthetic and real SPECT images. The experimental results demonstrate that the proposed hyperparameter estimation method produces satisfactory reconstructions. Although we have used generalized Gaussian Markov random fields (GGMRF) as prior models, the proposed estimation method can be applied to any priors with convex potential and tractable partition function with respect to the scale hyperparameter. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 21–27, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20003