X-ray directional dark-field imaging using Unified Modulated Pattern Analysis
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 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.
e0273315
Smith, Ronan
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De Marco, Fabio
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Broche, Ludovic
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Zdora, Marie-christine A
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Phillips, Nicholas W.
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Boardman, Richard
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Thibault, Pierre
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29 August 2022
Smith, Ronan
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De Marco, Fabio
fe0ff311-e459-4a14-b445-16669ac6adbd
Broche, Ludovic
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Zdora, Marie-christine A
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Phillips, Nicholas W.
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Boardman, Richard
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Thibault, Pierre
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Smith, Ronan, De Marco, Fabio, Broche, Ludovic, Zdora, Marie-christine A, Phillips, Nicholas W., Boardman, Richard and Thibault, Pierre
(2022)
X-ray directional dark-field imaging using Unified Modulated Pattern Analysis.
PLoS ONE, 17 (8 August), , [e0273315].
(doi:10.1371/journal.pone.0273315).
Abstract
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.
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journal.pone.0273315
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Accepted/In Press date: 6 August 2022
Published date: 29 August 2022
Additional Information:
Funding Information:
PT received funding from the European Union’s Horizon 2020 research and innovation program (grant agreement no. 866026). NP received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 884104. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Irene Zanette for experimental assistance. We thank Vittorio Di Trapani, Sara Savatović, Marco Margini and Ginevra Lautizi for providing insightful discussion. We acknowledge the European Synchrotron Radiation Facility for provision of synchrotron radiation facilities and we would like to thank Alexander Rack for assistance in using beamline ID19.
Publisher Copyright:
© 2022 Smith et al. 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 credited.
Identifiers
Local EPrints ID: 470234
URI: http://eprints.soton.ac.uk/id/eprint/470234
ISSN: 1932-6203
PURE UUID: 90b84bfd-1c26-4cd4-b85f-f01d072899aa
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Date deposited: 05 Oct 2022 16:32
Last modified: 17 Mar 2024 02:58
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Contributors
Author:
Ronan Smith
Author:
Fabio De Marco
Author:
Ludovic Broche
Author:
Marie-christine A Zdora
Author:
Nicholas W. Phillips
Author:
Pierre Thibault
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