Hidden-data spaces for maximum-likelihood PET reconstruction

Author

Fessler, Jeffrey A.

Author_Institution

Div. of Nucl. Med., Michigan, Ann Arbor, MI, USA

fYear

1992

fDate

25-31 Oct 1992

Firstpage

898

Abstract

The author shows that expectation-maximization (EM) algorithms based on smaller complete data spaces will typically converge faster. As an example, he compares the two maximum-likelihood (ML) image reconstruction algorithms of D. G. Politte and D. L. Snyder (1991) which are based on measurement models that account for attenuation and accidental coincidences in positron-emission tomography (PET)

Keywords

computerised tomography; image reconstruction; medical image processing; radioisotope scanning and imaging; accidental coincidences; attenuation; complete data spaces; convergence; expectation-maximization algorithms; hidden-data spaces; maximum-likelihood PET reconstruction; maximum-likelihood image reconstruction algorithms; measurement models; medical diagnostic imaging; nuclear medicine; positron-emission tomography; Attenuation measurement; Convergence; Density measurement; Image converters; Image reconstruction; Maximum likelihood estimation; Nuclear medicine; Parameter estimation; Positron emission tomography; US Department of Energy;

fLanguage

English

Publisher

ieee

Conference_Titel

Nuclear Science Symposium and Medical Imaging Conference, 1992., Conference Record of the 1992 IEEE

Conference_Location

Orlando, FL

Print_ISBN

0-7803-0884-0

Type

conf

DOI

10.1109/NSSMIC.1992.301014

Filename

301014