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Efficient CT Image Reconstruction in a GPU Parallel Environment
Computed tomography is nowadays an indispensable tool in medicine used to diagnose multiple diseases. In clinical and emergency room environments, the speed of acquisition and information processing are crucial. CUDA is a software architecture used to work with NVIDIA graphics processing units. In t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Grapho Publications, LLC
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7138519/ https://www.ncbi.nlm.nih.gov/pubmed/32280749 http://dx.doi.org/10.18383/j.tom.2020.00011 |
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author | Valencia Pérez, Tomás A. Hernández López, Javier M. Moreno-Barbosa, Eduardo de Celis Alonso, Benito Palomino Merino, Martín R. Castaño Meneses, Victor M. |
author_facet | Valencia Pérez, Tomás A. Hernández López, Javier M. Moreno-Barbosa, Eduardo de Celis Alonso, Benito Palomino Merino, Martín R. Castaño Meneses, Victor M. |
author_sort | Valencia Pérez, Tomás A. |
collection | PubMed |
description | Computed tomography is nowadays an indispensable tool in medicine used to diagnose multiple diseases. In clinical and emergency room environments, the speed of acquisition and information processing are crucial. CUDA is a software architecture used to work with NVIDIA graphics processing units. In this paper a methodology to accelerate tomographic image reconstruction based on maximum likelihood expectation maximization iterative algorithm and combined with the use of graphics processing units programmed in CUDA framework is presented. Implementations developed here are used to reconstruct images with clinical use. Timewise, parallel versions showed improvement with respect to serial implementations. These differences reached, in some cases, 2 orders of magnitude in time while preserving image quality. The image quality and reconstruction times were not affected significantly by the addition of Poisson noise to projections. Furthermore, our implementations showed good performance when compared with reconstruction methods provided by commercial software. One of the goals of this work was to provide a fast, portable, simple, and cheap image reconstruction system, and our results support the statement that the goal was achieved. |
format | Online Article Text |
id | pubmed-7138519 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Grapho Publications, LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-71385192020-04-11 Efficient CT Image Reconstruction in a GPU Parallel Environment Valencia Pérez, Tomás A. Hernández López, Javier M. Moreno-Barbosa, Eduardo de Celis Alonso, Benito Palomino Merino, Martín R. Castaño Meneses, Victor M. Tomography Research Article Computed tomography is nowadays an indispensable tool in medicine used to diagnose multiple diseases. In clinical and emergency room environments, the speed of acquisition and information processing are crucial. CUDA is a software architecture used to work with NVIDIA graphics processing units. In this paper a methodology to accelerate tomographic image reconstruction based on maximum likelihood expectation maximization iterative algorithm and combined with the use of graphics processing units programmed in CUDA framework is presented. Implementations developed here are used to reconstruct images with clinical use. Timewise, parallel versions showed improvement with respect to serial implementations. These differences reached, in some cases, 2 orders of magnitude in time while preserving image quality. The image quality and reconstruction times were not affected significantly by the addition of Poisson noise to projections. Furthermore, our implementations showed good performance when compared with reconstruction methods provided by commercial software. One of the goals of this work was to provide a fast, portable, simple, and cheap image reconstruction system, and our results support the statement that the goal was achieved. Grapho Publications, LLC 2020-03 /pmc/articles/PMC7138519/ /pubmed/32280749 http://dx.doi.org/10.18383/j.tom.2020.00011 Text en © 2020 The Authors. Published by Grapho Publications, LLC http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Valencia Pérez, Tomás A. Hernández López, Javier M. Moreno-Barbosa, Eduardo de Celis Alonso, Benito Palomino Merino, Martín R. Castaño Meneses, Victor M. Efficient CT Image Reconstruction in a GPU Parallel Environment |
title | Efficient CT Image Reconstruction in a GPU Parallel Environment |
title_full | Efficient CT Image Reconstruction in a GPU Parallel Environment |
title_fullStr | Efficient CT Image Reconstruction in a GPU Parallel Environment |
title_full_unstemmed | Efficient CT Image Reconstruction in a GPU Parallel Environment |
title_short | Efficient CT Image Reconstruction in a GPU Parallel Environment |
title_sort | efficient ct image reconstruction in a gpu parallel environment |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7138519/ https://www.ncbi.nlm.nih.gov/pubmed/32280749 http://dx.doi.org/10.18383/j.tom.2020.00011 |
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