Abstract
In Positron Emission Tomography (PET) images have to be reconstructed from moisy projection data. The noise on the PET data can be modeled by a Poison distribution. In this paper, we present the results of using the simulated annealing technique to reconstruct PET images. Various parameter settings of the simulated annealing algorithm are discussed and optimized. The reconstructed images are of good quality and high contrast, in comparison to other reconstruction techniques. (authors). 11 refs., 2 figs.
Sundermann, E;
Lemahieu, I;
Desmedt, P
[1]
- Department of Electronics and Information Systems, University of Ghent, St. Pietersnieuwstraat 41, B-9000 Ghent, Belgium (Belgium)
Citation Formats
Sundermann, E, Lemahieu, I, and Desmedt, P.
Simulated annealing image reconstruction for positron emission tomography.
Cyprus: N. p.,
1994.
Web.
Sundermann, E, Lemahieu, I, & Desmedt, P.
Simulated annealing image reconstruction for positron emission tomography.
Cyprus.
Sundermann, E, Lemahieu, I, and Desmedt, P.
1994.
"Simulated annealing image reconstruction for positron emission tomography."
Cyprus.
@misc{etde_574101,
title = {Simulated annealing image reconstruction for positron emission tomography}
author = {Sundermann, E, Lemahieu, I, and Desmedt, P}
abstractNote = {In Positron Emission Tomography (PET) images have to be reconstructed from moisy projection data. The noise on the PET data can be modeled by a Poison distribution. In this paper, we present the results of using the simulated annealing technique to reconstruct PET images. Various parameter settings of the simulated annealing algorithm are discussed and optimized. The reconstructed images are of good quality and high contrast, in comparison to other reconstruction techniques. (authors). 11 refs., 2 figs.}
place = {Cyprus}
year = {1994}
month = {Dec}
}
title = {Simulated annealing image reconstruction for positron emission tomography}
author = {Sundermann, E, Lemahieu, I, and Desmedt, P}
abstractNote = {In Positron Emission Tomography (PET) images have to be reconstructed from moisy projection data. The noise on the PET data can be modeled by a Poison distribution. In this paper, we present the results of using the simulated annealing technique to reconstruct PET images. Various parameter settings of the simulated annealing algorithm are discussed and optimized. The reconstructed images are of good quality and high contrast, in comparison to other reconstruction techniques. (authors). 11 refs., 2 figs.}
place = {Cyprus}
year = {1994}
month = {Dec}
}