Title: MicroPET reconstruction with random coincidence correction via a joint Poisson model
Authors: Chen, Tai-Been
Chen, Jyh-Cheng
Lu, Henry Horng-Shing
Liu, Ren-Shyan
統計學研究所
Institute of Statistics
Keywords: MLE-EW;FBP;OSEM;PDEM;FORE;CV;FWHM
Issue Date: 1-Jul-2008
Abstract: Positron emission tomography (PET) can provide in vivo, quantitative and functional information for diagnosis; however, PET image quality depends highly on a reconstruction algorithm. Iterative algorithms, such as the maximum likelihood expectation maximization (MLEM) algorithm, are rapidly becoming the standards for image reconstruction in emission-computed tomography. The conventional MLEM algorithm utilized the Poisson model in its system matrix, which is no longer valid for delay-subtraction of randomly corrected data. The aim of this study is to overcome this problem. The maximum likelihood estimation using the expectation maximum algorithm (MLE-EM) is adopted and modified to reconstruct microPET images using random correction from joint prompt and delay sinograms; this reconstruction method is called PDEM. The proposed joint Poisson model preserves Poisson properties without increasing the variance (noise) associated with random correction. The work here is an initial application/demonstration without applied normalization, scattering. attenuation, and arc correction. The coefficients of variation (CV) and full width at half-maximum (FWHM) values were utilized to compare the quality of reconstructed microPET images of physical phantoms acquired by filtered backprojection (FBP), ordered subsets-expected maximum (OSEM) and PDEM approaches. Experimental and simulated results demonstrate that the proposed PDEM produces better image quality than the FBP and OSEM approaches. (C) 2007 IPEM. Published by Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.medengphy.2007.05.013
http://hdl.handle.net/11536/8640
ISSN: 1350-4533
DOI: 10.1016/j.medengphy.2007.05.013
Journal: MEDICAL ENGINEERING & PHYSICS
Volume: 30
Issue: 6
Begin Page: 680
End Page: 686
Appears in Collections:Articles


Files in This Item:

  1. 000258588700002.pdf

If it is a zip file, please download the file and unzip it, then open index.html in a browser to view the full text content.