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A Convergence Proof of MLEM and MLEM-3 with Fixed Background

Tijdschriftbijdrage - Tijdschriftartikel

Maximum Likelihood Expectation-Maximization (MLEM) is a popular algorithm to reconstruct the activity image in Positron Emission Tomography (PET). This paper introduces a 'fundamental equality' for the MLEM complete data from which two key properties easily follow that allows us to: (i) prove in an elegant and compact way the convergence of MLEM for a forward model with fixed background (i.e., counts such as random and scatter coincidences); and (ii) generalize this proof for the MLEM-3 algorithm. Moreover we give necessary and sufficient conditions for the solution to be unique.

Tijdschrift:  IEEE Trans Med Imaging
ISSN: 0278-0062
Issue: 3
Volume: 38
Pagina's: 721-729
Jaar van publicatie:2019
Trefwoorden:Convergence, Data models, Image reconstruction, Image Reconstruction, Maximum likelihood estimation, Positron emission tomography, Positron Emission Tomography (PET)
CSS-citation score:1
Toegankelijkheid:Closed