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Multicriteria maximum likelihood neural network approach to positron emission tomography
Author(s) -
Wang Yuanmei,
Heng Pheng Ann
Publication year - 2000
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.10002
Subject(s) - positron emission tomography , iterative reconstruction , imaging phantom , computer science , positron , smoothness , artificial intelligence , algorithm , projection (relational algebra) , artificial neural network , scanner , computer vision , physics , mathematics , nuclear medicine , optics , nuclear physics , medicine , mathematical analysis , electron
The emerging technology of positron emission image reconstruction is introduced in this paper as a multicriteria optimization problem. We show how selected families of objective functions may be used to reconstruct positron emission images. We develop a novel neural network approach to positron emission imaging problems. We also studied the most frequently used image reconstruction methods, namely, maximum likelihood under the framework of single performance criterion optimization. Finally, we introduced some of the results obtained by various reconstruction algorithms using computer‐generated noisy projection data from a chest phantom and real positron emission tomography (PET) scanner data. Comparison of the reconstructed images indicated that the multicriteria optimization method gave the best in error, smoothness (suppression of noise), gray value resolution, and ghost‐free images. © 2001 John Wiley & Sons, Inc. Int J Imaging Syst Technol 11, 361–364, 2000