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System Matrix Analysis for Computed Tomography Imaging

In practical applications of computed tomography imaging (CT), it is often the case that the set of projection data is incomplete owing to the physical conditions of the data acquisition process. On the other hand, the high radiation dose imposed on patients is also undesired. These issues demand th...

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Detalles Bibliográficos
Autores principales: Flores, Liubov, Vidal, Vicent, Verdú, Gumersindo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4648504/
https://www.ncbi.nlm.nih.gov/pubmed/26575482
http://dx.doi.org/10.1371/journal.pone.0143202
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author Flores, Liubov
Vidal, Vicent
Verdú, Gumersindo
author_facet Flores, Liubov
Vidal, Vicent
Verdú, Gumersindo
author_sort Flores, Liubov
collection PubMed
description In practical applications of computed tomography imaging (CT), it is often the case that the set of projection data is incomplete owing to the physical conditions of the data acquisition process. On the other hand, the high radiation dose imposed on patients is also undesired. These issues demand that high quality CT images can be reconstructed from limited projection data. For this reason, iterative methods of image reconstruction have become a topic of increased research interest. Several algorithms have been proposed for few-view CT. We consider that the accurate solution of the reconstruction problem also depends on the system matrix that simulates the scanning process. In this work, we analyze the application of the Siddon method to generate elements of the matrix and we present results based on real projection data.
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spelling pubmed-46485042015-11-25 System Matrix Analysis for Computed Tomography Imaging Flores, Liubov Vidal, Vicent Verdú, Gumersindo PLoS One Research Article In practical applications of computed tomography imaging (CT), it is often the case that the set of projection data is incomplete owing to the physical conditions of the data acquisition process. On the other hand, the high radiation dose imposed on patients is also undesired. These issues demand that high quality CT images can be reconstructed from limited projection data. For this reason, iterative methods of image reconstruction have become a topic of increased research interest. Several algorithms have been proposed for few-view CT. We consider that the accurate solution of the reconstruction problem also depends on the system matrix that simulates the scanning process. In this work, we analyze the application of the Siddon method to generate elements of the matrix and we present results based on real projection data. Public Library of Science 2015-11-17 /pmc/articles/PMC4648504/ /pubmed/26575482 http://dx.doi.org/10.1371/journal.pone.0143202 Text en © 2015 Flores et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Flores, Liubov
Vidal, Vicent
Verdú, Gumersindo
System Matrix Analysis for Computed Tomography Imaging
title System Matrix Analysis for Computed Tomography Imaging
title_full System Matrix Analysis for Computed Tomography Imaging
title_fullStr System Matrix Analysis for Computed Tomography Imaging
title_full_unstemmed System Matrix Analysis for Computed Tomography Imaging
title_short System Matrix Analysis for Computed Tomography Imaging
title_sort system matrix analysis for computed tomography imaging
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4648504/
https://www.ncbi.nlm.nih.gov/pubmed/26575482
http://dx.doi.org/10.1371/journal.pone.0143202
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