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Learned Primal Dual Reconstruction for PET

We have adapted, implemented and trained the Learned Primal Dual algorithm suggested by Adler and Öktem and evaluated its performance in reconstructing projection data from our PET scanner. Learned Primal Dual reconstructions are compared to Maximum Likelihood Expectation Maximisation (MLEM) reconst...

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Detalles Bibliográficos
Autores principales: Guazzo, Alessandro, Colarieti-Tosti, Massimiliano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8707496/
https://www.ncbi.nlm.nih.gov/pubmed/34940715
http://dx.doi.org/10.3390/jimaging7120248
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author Guazzo, Alessandro
Colarieti-Tosti, Massimiliano
author_facet Guazzo, Alessandro
Colarieti-Tosti, Massimiliano
author_sort Guazzo, Alessandro
collection PubMed
description We have adapted, implemented and trained the Learned Primal Dual algorithm suggested by Adler and Öktem and evaluated its performance in reconstructing projection data from our PET scanner. Learned Primal Dual reconstructions are compared to Maximum Likelihood Expectation Maximisation (MLEM) reconstructions. Different strategies for training are also compared. Whenever the noise level of the data to reconstruct is sufficiently represented in the training set, the Learned Primal Dual algorithm performs well on the recovery of the activity concentrations and on noise reduction as compared to MLEM. The algorithm is also shown to be robust against the appearance of artefacts, even when the images that are to be reconstructed present features were not present in the training set. Once trained, the algorithm reconstructs images in few seconds or less.
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spelling pubmed-87074962021-12-25 Learned Primal Dual Reconstruction for PET Guazzo, Alessandro Colarieti-Tosti, Massimiliano J Imaging Article We have adapted, implemented and trained the Learned Primal Dual algorithm suggested by Adler and Öktem and evaluated its performance in reconstructing projection data from our PET scanner. Learned Primal Dual reconstructions are compared to Maximum Likelihood Expectation Maximisation (MLEM) reconstructions. Different strategies for training are also compared. Whenever the noise level of the data to reconstruct is sufficiently represented in the training set, the Learned Primal Dual algorithm performs well on the recovery of the activity concentrations and on noise reduction as compared to MLEM. The algorithm is also shown to be robust against the appearance of artefacts, even when the images that are to be reconstructed present features were not present in the training set. Once trained, the algorithm reconstructs images in few seconds or less. MDPI 2021-11-24 /pmc/articles/PMC8707496/ /pubmed/34940715 http://dx.doi.org/10.3390/jimaging7120248 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Guazzo, Alessandro
Colarieti-Tosti, Massimiliano
Learned Primal Dual Reconstruction for PET
title Learned Primal Dual Reconstruction for PET
title_full Learned Primal Dual Reconstruction for PET
title_fullStr Learned Primal Dual Reconstruction for PET
title_full_unstemmed Learned Primal Dual Reconstruction for PET
title_short Learned Primal Dual Reconstruction for PET
title_sort learned primal dual reconstruction for pet
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8707496/
https://www.ncbi.nlm.nih.gov/pubmed/34940715
http://dx.doi.org/10.3390/jimaging7120248
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