Measurement Data Correction for Emission Tomography

Author

Wu, Hao ; Zhang, Qingping

Author_Institution

Med. Eng. Support Center, Chinese PLA (People´´s Liberation Army)Gen. Hosp., Beijing, China

fYear

2009

fDate

11-13 June 2009

Firstpage

1

Lastpage

4

Abstract

In the problems of statistical reconstruction of emission tomography images, Bayesian reconstruction, or maximum a posteriori (MAP) method, has proved its superiority over others among all the regularization methods. To further improve the reconstruction, this paper presents a novel statistical image reconstruction method based on coupled feedback (CF) iterative model for emission tomography. This CF iterative algorithm updates the noisy emission sinogram (the measurement data of the detectors) using the latest reconstructed image. The experiments and the performance analysis confirm the virtue of the new method.

Keywords

Bayes methods; emission tomography; image reconstruction; iterative methods; maximum likelihood estimation; medical image processing; Bayesian reconstruction; coupled feedback iterative model; emission tomography images; maximum a posteriori method; measurement data correction; noisy emission sinogram; statistical reconstruction; Bayesian methods; Convergence; Feedback; Image reconstruction; Iterative algorithms; Iterative methods; Maximum likelihood detection; Maximum likelihood estimation; Pollution measurement; Positron emission tomography;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-2901-1

Electronic_ISBN

978-1-4244-2902-8

Type

conf

DOI

10.1109/ICBBE.2009.5163033

Filename

5163033