Iterative reconstruction-reprojection and the expectation-maximization algorithm

Author

Ollinger, John M.

Author_Institution

Hospital of the Univ. of Pennsylvania, Philadelphia, PA, USA

Volume

9

Issue

1

fYear

1990

fDate

3/1/1990 12:00:00 AM

Firstpage

94

Lastpage

98

Abstract

The iterative reconstruction-reprojection (IRR) algorithm is a method for estimating missing projections in computed tomography. It is derived as an expectation-maximization (EM) algorithm that increases a suitable likelihood function. The constraint that the data form a consistent set of projections is loosened to require only that the means of the data form a consistent set, thereby suggesting that the algorithm is suitable for use with noisy data. Proofs of convergence to a stationary point and of monotonicity of the sequence of iterates are given. Simulations supporting these results are described

Keywords

computerised tomography; computed tomography; convergence to a stationary point; expectation-maximization algorithm; iterates sequence monotonicity; iterative reconstruction-reprojection; likelihood function; medical diagnostic imaging; missing projections estimation; simulations; Algorithm design and analysis; Computed tomography; Expectation-maximization algorithms; Helium; Image analysis; Image reconstruction; Iterative algorithms; Iterative methods; Maximum likelihood estimation; Positron emission tomography;

fLanguage

English

Journal_Title

Medical Imaging, IEEE Transactions on

Publisher

ieee

ISSN

0278-0062

Type

jour

DOI

10.1109/42.52986

Filename

52986