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Reconstruction of PET Images Using Cross-Entropy and Field of Experts

The reconstruction of positron emission tomography data is a difficult task, particularly at low count rates because Poisson noise has a significant influence on the statistical uncertainty of positron emission tomography (PET) measurements. Prior information is frequently used to improve image qual...

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Detalles Bibliográficos
Autores principales: Mejia, Jose, Ochoa, Alberto, Mederos, Boris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514193/
https://www.ncbi.nlm.nih.gov/pubmed/33266799
http://dx.doi.org/10.3390/e21010083
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author Mejia, Jose
Ochoa, Alberto
Mederos, Boris
author_facet Mejia, Jose
Ochoa, Alberto
Mederos, Boris
author_sort Mejia, Jose
collection PubMed
description The reconstruction of positron emission tomography data is a difficult task, particularly at low count rates because Poisson noise has a significant influence on the statistical uncertainty of positron emission tomography (PET) measurements. Prior information is frequently used to improve image quality. In this paper, we propose the use of a field of experts to model a priori structure and capture anatomical spatial dependencies of the PET images to address the problems of noise and low count data, which make the reconstruction of the image difficult. We reconstruct PET images by using a modified MXE algorithm, which minimizes a objective function with the cross-entropy as a fidelity term, while the field of expert model is incorporated as a regularizing term. Comparisons with the expectation maximization algorithm and a iterative method with a prior penalizing relative differences showed that the proposed method can lead to accurate estimation of the image, especially with acquisitions at low count rate.
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spelling pubmed-75141932020-11-09 Reconstruction of PET Images Using Cross-Entropy and Field of Experts Mejia, Jose Ochoa, Alberto Mederos, Boris Entropy (Basel) Article The reconstruction of positron emission tomography data is a difficult task, particularly at low count rates because Poisson noise has a significant influence on the statistical uncertainty of positron emission tomography (PET) measurements. Prior information is frequently used to improve image quality. In this paper, we propose the use of a field of experts to model a priori structure and capture anatomical spatial dependencies of the PET images to address the problems of noise and low count data, which make the reconstruction of the image difficult. We reconstruct PET images by using a modified MXE algorithm, which minimizes a objective function with the cross-entropy as a fidelity term, while the field of expert model is incorporated as a regularizing term. Comparisons with the expectation maximization algorithm and a iterative method with a prior penalizing relative differences showed that the proposed method can lead to accurate estimation of the image, especially with acquisitions at low count rate. MDPI 2019-01-18 /pmc/articles/PMC7514193/ /pubmed/33266799 http://dx.doi.org/10.3390/e21010083 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mejia, Jose
Ochoa, Alberto
Mederos, Boris
Reconstruction of PET Images Using Cross-Entropy and Field of Experts
title Reconstruction of PET Images Using Cross-Entropy and Field of Experts
title_full Reconstruction of PET Images Using Cross-Entropy and Field of Experts
title_fullStr Reconstruction of PET Images Using Cross-Entropy and Field of Experts
title_full_unstemmed Reconstruction of PET Images Using Cross-Entropy and Field of Experts
title_short Reconstruction of PET Images Using Cross-Entropy and Field of Experts
title_sort reconstruction of pet images using cross-entropy and field of experts
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514193/
https://www.ncbi.nlm.nih.gov/pubmed/33266799
http://dx.doi.org/10.3390/e21010083
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