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The choice of an autocorrelation length in dark-field lung imaging

Respiratory diseases are one of the most common causes of death, and their early detection is crucial for prompt treatment. X-ray dark-field radiography (XDFR) is a promising tool to image objects with unresolved micro-structures such as lungs. Using Talbot-Lau XDFR, we imaged inflated porcine lungs...

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Autores principales: Spindler, Simon, Etter, Dominik, Rawlik, Michał, Polikarpov, Maxim, Romano, Lucia, Shi, Zhitian, Jefimovs, Konstantins, Wang, Zhentian, Stampanoni, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9932147/
https://www.ncbi.nlm.nih.gov/pubmed/36792717
http://dx.doi.org/10.1038/s41598-023-29762-y
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author Spindler, Simon
Etter, Dominik
Rawlik, Michał
Polikarpov, Maxim
Romano, Lucia
Shi, Zhitian
Jefimovs, Konstantins
Wang, Zhentian
Stampanoni, Marco
author_facet Spindler, Simon
Etter, Dominik
Rawlik, Michał
Polikarpov, Maxim
Romano, Lucia
Shi, Zhitian
Jefimovs, Konstantins
Wang, Zhentian
Stampanoni, Marco
author_sort Spindler, Simon
collection PubMed
description Respiratory diseases are one of the most common causes of death, and their early detection is crucial for prompt treatment. X-ray dark-field radiography (XDFR) is a promising tool to image objects with unresolved micro-structures such as lungs. Using Talbot-Lau XDFR, we imaged inflated porcine lungs together with Polymethylmethacrylat (PMMA) microspheres (in air) of diameter sizes between 20 and 500 [Formula: see text] over an autocorrelation range of 0.8–5.2 [Formula: see text] . The results indicate that the dark-field extinction coefficient of porcine lungs is similar to that of densely-packed PMMA spheres with diameter of [Formula: see text] , which is approximately the mean alveolar structure size. We evaluated that, in our case, the autocorrelation length would have to be limited to [Formula: see text] in order to image [Formula: see text] -thick lung tissue without critical visibility reduction (signal saturation). We identify the autocorrelation length to be the critical parameter of an interferometer that allows to avoid signal saturation in clinical lung dark-field imaging.
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spelling pubmed-99321472023-02-17 The choice of an autocorrelation length in dark-field lung imaging Spindler, Simon Etter, Dominik Rawlik, Michał Polikarpov, Maxim Romano, Lucia Shi, Zhitian Jefimovs, Konstantins Wang, Zhentian Stampanoni, Marco Sci Rep Article Respiratory diseases are one of the most common causes of death, and their early detection is crucial for prompt treatment. X-ray dark-field radiography (XDFR) is a promising tool to image objects with unresolved micro-structures such as lungs. Using Talbot-Lau XDFR, we imaged inflated porcine lungs together with Polymethylmethacrylat (PMMA) microspheres (in air) of diameter sizes between 20 and 500 [Formula: see text] over an autocorrelation range of 0.8–5.2 [Formula: see text] . The results indicate that the dark-field extinction coefficient of porcine lungs is similar to that of densely-packed PMMA spheres with diameter of [Formula: see text] , which is approximately the mean alveolar structure size. We evaluated that, in our case, the autocorrelation length would have to be limited to [Formula: see text] in order to image [Formula: see text] -thick lung tissue without critical visibility reduction (signal saturation). We identify the autocorrelation length to be the critical parameter of an interferometer that allows to avoid signal saturation in clinical lung dark-field imaging. Nature Publishing Group UK 2023-02-15 /pmc/articles/PMC9932147/ /pubmed/36792717 http://dx.doi.org/10.1038/s41598-023-29762-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Spindler, Simon
Etter, Dominik
Rawlik, Michał
Polikarpov, Maxim
Romano, Lucia
Shi, Zhitian
Jefimovs, Konstantins
Wang, Zhentian
Stampanoni, Marco
The choice of an autocorrelation length in dark-field lung imaging
title The choice of an autocorrelation length in dark-field lung imaging
title_full The choice of an autocorrelation length in dark-field lung imaging
title_fullStr The choice of an autocorrelation length in dark-field lung imaging
title_full_unstemmed The choice of an autocorrelation length in dark-field lung imaging
title_short The choice of an autocorrelation length in dark-field lung imaging
title_sort choice of an autocorrelation length in dark-field lung imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9932147/
https://www.ncbi.nlm.nih.gov/pubmed/36792717
http://dx.doi.org/10.1038/s41598-023-29762-y
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