Accelerated list-mode EM algorithm

Author

Reader, Andrew J. ; Manavaki, Roido ; Zhao, Sha ; Julyan, Peter J. ; Hastings, David L. ; Zweit, Jamal

Author_Institution

Dept. of Instrum. & Anal. Sci., Univ. of Manchester Inst. of Sci. & Technol., UK

Volume

49

Issue

1

fYear

2002

fDate

2/1/2002 12:00:00 AM

Firstpage

42

Lastpage

49

Abstract

List-mode data preserves all sampling information in three-dimensional (3-D) PET imaging and can reduce storage requirements for short-time frame acquisitions. List-mode expectation maximization-maximum likelihood (EM-ML), which has been implemented in a number of forms (such as the EM algorithm for list-mode maximum likelihood, the FAIR algorithm and COSEM), is an obvious choice to reconstruct from such data sets when the statistics are low. However, these methods can be slow for large quantities of list-mode data and it is desirable to accelerate them. This work investigates the use of subsets in combination with a relaxation parameter for 3-D list-mode EM reconstructions. Results show that two iterations through the list-mode data are sufficient to yield good quality reconstructions. Furthermore, if counting statistics are good, just one iteration may prove sufficient, opening the way for real-time iterative reconstruction

Keywords

image reconstruction; iterative methods; maximum likelihood estimation; medical image processing; positron emission tomography; COSEM; FAIR algorithm; PET imaging; list-mode data; list-mode expectation maximization-maximum likelihood; positron emission tomography; real-time iterative reconstruction; relaxation parameter; sampling information; short-time frame acquisitions; storage requirements; Acceleration; Cancer; Image reconstruction; Image storage; Instruments; Memory; Positron emission tomography; Reconstruction algorithms; Sampling methods; Statistics;

fLanguage

English

Journal_Title

Nuclear Science, IEEE Transactions on

Publisher

ieee

ISSN

0018-9499

Type

jour

DOI

10.1109/TNS.2002.998679

Filename

998679