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Backprojection Wiener deconvolution for computed tomographic reconstruction
Analytical CT reconstruction is popular in practice because of its computational efficiency, but it suffers from low reconstruction quality when an insufficient number of projections are used. To address this issue, this paper presents a new analytical method of backprojection Wiener deconvolution (...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6298675/ https://www.ncbi.nlm.nih.gov/pubmed/30562345 http://dx.doi.org/10.1371/journal.pone.0207907 |
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author | Wang, Zhenglin Cai, Jinhai Guo, William Donnelley, Martin Parsons, David Lee, Ivan |
author_facet | Wang, Zhenglin Cai, Jinhai Guo, William Donnelley, Martin Parsons, David Lee, Ivan |
author_sort | Wang, Zhenglin |
collection | PubMed |
description | Analytical CT reconstruction is popular in practice because of its computational efficiency, but it suffers from low reconstruction quality when an insufficient number of projections are used. To address this issue, this paper presents a new analytical method of backprojection Wiener deconvolution (BPWD). BPWD executes backprojection first, and then applies a Wiener deconvolution to the whole backprojected image. The Wiener filter is derived from a ramp filter, enabling the proposed approach to perform reconstruction and denoising simultaneously. The use of a filter after backprojection does not differentiate between real sampled projections and interpolated ones, introducing reconstruction errors. Therefore a weighted ramp filter was applied to increase the contribution of real sampled projections in the reconstruction, thus improving reconstruction quality. Experiments on synthetic data and real phase-contrast x-ray images showed that the proposed approach yields better reconstruction quality compared to the classical filtered backprojection (FBP) method, with comparable reconstruction speed. |
format | Online Article Text |
id | pubmed-6298675 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-62986752018-12-28 Backprojection Wiener deconvolution for computed tomographic reconstruction Wang, Zhenglin Cai, Jinhai Guo, William Donnelley, Martin Parsons, David Lee, Ivan PLoS One Research Article Analytical CT reconstruction is popular in practice because of its computational efficiency, but it suffers from low reconstruction quality when an insufficient number of projections are used. To address this issue, this paper presents a new analytical method of backprojection Wiener deconvolution (BPWD). BPWD executes backprojection first, and then applies a Wiener deconvolution to the whole backprojected image. The Wiener filter is derived from a ramp filter, enabling the proposed approach to perform reconstruction and denoising simultaneously. The use of a filter after backprojection does not differentiate between real sampled projections and interpolated ones, introducing reconstruction errors. Therefore a weighted ramp filter was applied to increase the contribution of real sampled projections in the reconstruction, thus improving reconstruction quality. Experiments on synthetic data and real phase-contrast x-ray images showed that the proposed approach yields better reconstruction quality compared to the classical filtered backprojection (FBP) method, with comparable reconstruction speed. Public Library of Science 2018-12-18 /pmc/articles/PMC6298675/ /pubmed/30562345 http://dx.doi.org/10.1371/journal.pone.0207907 Text en © 2018 Wang 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 (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wang, Zhenglin Cai, Jinhai Guo, William Donnelley, Martin Parsons, David Lee, Ivan Backprojection Wiener deconvolution for computed tomographic reconstruction |
title | Backprojection Wiener deconvolution for computed tomographic reconstruction |
title_full | Backprojection Wiener deconvolution for computed tomographic reconstruction |
title_fullStr | Backprojection Wiener deconvolution for computed tomographic reconstruction |
title_full_unstemmed | Backprojection Wiener deconvolution for computed tomographic reconstruction |
title_short | Backprojection Wiener deconvolution for computed tomographic reconstruction |
title_sort | backprojection wiener deconvolution for computed tomographic reconstruction |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6298675/ https://www.ncbi.nlm.nih.gov/pubmed/30562345 http://dx.doi.org/10.1371/journal.pone.0207907 |
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