Principles of image reconstruction using positron emission tomography and maximum likelihood estimation algorithms

Please use this identifier to cite or link to this item: https://open.uns.ac.rs/handle/123456789/13382

Title:	Principles of image reconstruction using positron emission tomography and maximum likelihood estimation algorithms
Authors:	Bezbradica M. Trpovski, Željen
Issue Date:	1-Dec-2008
Journal:	9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008 Proceedings
Abstract:	Positron Emission Tomography (PET) is used extensively in image acquisition for medical purposes. It is necessary to perform an evaluation of parameters that are required for image reconstruction based on the measured data. The most commonly used method for estimation of PET parameters is Expectation Maximization algorithm. About a decade ago a new SAGE algorithm was developed and soon It began to be used in image reconstruction. Principles and examples of these algorithms as well as of PET are described in this paper. ©2008 IEEE.
URI:	https://open.uns.ac.rs/handle/123456789/13382
ISBN:	9781424429042
DOI:	10.1109/NEUREL.2008.4685592
Appears in Collections:	FTN Publikacije/Publications

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