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X-ray directional dark-field imaging using Unified Modulated Pattern Analysis
X-ray directional dark-field imaging is a recent technique that can reveal a sample’s small-scale structural properties which are otherwise invisible in a conventional imaging system. In particular, directional dark-field can detect and quantify the orientation of anisotropic structures. Here, we pr...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9423625/ https://www.ncbi.nlm.nih.gov/pubmed/36037163 http://dx.doi.org/10.1371/journal.pone.0273315 |
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author | Smith, Ronan De Marco, Fabio Broche, Ludovic Zdora, Marie-Christine Phillips, Nicholas W. Boardman, Richard Thibault, Pierre |
author_facet | Smith, Ronan De Marco, Fabio Broche, Ludovic Zdora, Marie-Christine Phillips, Nicholas W. Boardman, Richard Thibault, Pierre |
author_sort | Smith, Ronan |
collection | PubMed |
description | X-ray directional dark-field imaging is a recent technique that can reveal a sample’s small-scale structural properties which are otherwise invisible in a conventional imaging system. In particular, directional dark-field can detect and quantify the orientation of anisotropic structures. Here, we present an algorithm that allows for the extraction of a directional dark-field signal from X-ray speckle-based imaging data. The experimental setup is simple, as it requires only the addition of a diffuser to a full-field microscope setup. Sandpaper is an appropriate diffuser material in the hard x-ray regime. We propose an approach to extract the mean scattering width, directionality, and orientation from the recorded speckle images acquired with the technique. We demonstrate that our method can detect and quantify the orientation of fibres inside a carbon fibre reinforced polymer (CFRP) sample within one degree of accuracy and show how the accuracy depends on the number of included measurements. We show that the reconstruction parameters can be tuned to increase or decrease accuracy at the expense of spatial resolution. |
format | Online Article Text |
id | pubmed-9423625 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-94236252022-08-30 X-ray directional dark-field imaging using Unified Modulated Pattern Analysis Smith, Ronan De Marco, Fabio Broche, Ludovic Zdora, Marie-Christine Phillips, Nicholas W. Boardman, Richard Thibault, Pierre PLoS One Research Article X-ray directional dark-field imaging is a recent technique that can reveal a sample’s small-scale structural properties which are otherwise invisible in a conventional imaging system. In particular, directional dark-field can detect and quantify the orientation of anisotropic structures. Here, we present an algorithm that allows for the extraction of a directional dark-field signal from X-ray speckle-based imaging data. The experimental setup is simple, as it requires only the addition of a diffuser to a full-field microscope setup. Sandpaper is an appropriate diffuser material in the hard x-ray regime. We propose an approach to extract the mean scattering width, directionality, and orientation from the recorded speckle images acquired with the technique. We demonstrate that our method can detect and quantify the orientation of fibres inside a carbon fibre reinforced polymer (CFRP) sample within one degree of accuracy and show how the accuracy depends on the number of included measurements. We show that the reconstruction parameters can be tuned to increase or decrease accuracy at the expense of spatial resolution. Public Library of Science 2022-08-29 /pmc/articles/PMC9423625/ /pubmed/36037163 http://dx.doi.org/10.1371/journal.pone.0273315 Text en © 2022 Smith et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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 Smith, Ronan De Marco, Fabio Broche, Ludovic Zdora, Marie-Christine Phillips, Nicholas W. Boardman, Richard Thibault, Pierre X-ray directional dark-field imaging using Unified Modulated Pattern Analysis |
title | X-ray directional dark-field imaging using Unified Modulated Pattern Analysis |
title_full | X-ray directional dark-field imaging using Unified Modulated Pattern Analysis |
title_fullStr | X-ray directional dark-field imaging using Unified Modulated Pattern Analysis |
title_full_unstemmed | X-ray directional dark-field imaging using Unified Modulated Pattern Analysis |
title_short | X-ray directional dark-field imaging using Unified Modulated Pattern Analysis |
title_sort | x-ray directional dark-field imaging using unified modulated pattern analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9423625/ https://www.ncbi.nlm.nih.gov/pubmed/36037163 http://dx.doi.org/10.1371/journal.pone.0273315 |
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