Fast scaled gradient decomposition methods for maximum likelihood transmission tomography

Author

Pierro, A.R. ; Yamagishi, M.E.B.

Author_Institution

Dept. of Appl. Math., Campinas State Univ., Brazil

Volume

1

fYear

2003

fDate

17-21 Sept. 2003

Firstpage

829

Abstract

New iterative algorithms are presented for maximum likelihood (ML) and regularized maximum likelihood (MAP) reconstruction in transmission tomography (CT). The algorithms are natural extensions to CT of RAMLA, a well known method for ML reconstruction in emission computed tomography (ECT). We show that the new algorithm for ML solutions produces similar, or even better results than EM-like algorithms, but in much fewer iterations. Also, its convergence properties are better than other ordered subsets methods.

Keywords

emission tomography; image reconstruction; iterative methods; maximum likelihood estimation; medical image processing; emission computed tomography; gradient decomposition methods; iterative algorithms; maximum likelihood reconstruction; maximum likelihood transmission tomography; Bayesian methods; Computed tomography; Convergence; Electrical capacitance tomography; Equations; Image reconstruction; Iterative algorithms; Mathematics; Maximum likelihood detection; Pixel;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE

ISSN

1094-687X

Print_ISBN

0-7803-7789-3

Type

conf

DOI

10.1109/IEMBS.2003.1279893

Filename

1279893