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Unsupervised classification for region of interest in X-ray ptychography
X-ray ptychography offers high-resolution imaging of large areas at a high computational cost due to the large volume of data provided. To address the cost issue, we propose a physics-informed unsupervised classification algorithm that is performed prior to reconstruction and removes data outside th...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10643553/ https://www.ncbi.nlm.nih.gov/pubmed/37957208 http://dx.doi.org/10.1038/s41598-023-45336-4 |
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author | Lin, Dergan Jiang, Yi Deng, Junjing Di, Zichao Wendy |
author_facet | Lin, Dergan Jiang, Yi Deng, Junjing Di, Zichao Wendy |
author_sort | Lin, Dergan |
collection | PubMed |
description | X-ray ptychography offers high-resolution imaging of large areas at a high computational cost due to the large volume of data provided. To address the cost issue, we propose a physics-informed unsupervised classification algorithm that is performed prior to reconstruction and removes data outside the region of interest (RoI) based on the multimodal features present in the diffraction patterns. The preprocessing time for the proposed method is inconsequential in contrast to the resource-intensive reconstruction process, leading to an impressive reduction in the data workload to a mere 20% of the initial dataset. This capability consequently reduces computational time dramatically while preserving reconstruction quality. Through further segmentation of the diffraction patterns, our proposed approach can also detect features that are smaller than beam size and correctly classify them as within the RoI. |
format | Online Article Text |
id | pubmed-10643553 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106435532023-11-13 Unsupervised classification for region of interest in X-ray ptychography Lin, Dergan Jiang, Yi Deng, Junjing Di, Zichao Wendy Sci Rep Article X-ray ptychography offers high-resolution imaging of large areas at a high computational cost due to the large volume of data provided. To address the cost issue, we propose a physics-informed unsupervised classification algorithm that is performed prior to reconstruction and removes data outside the region of interest (RoI) based on the multimodal features present in the diffraction patterns. The preprocessing time for the proposed method is inconsequential in contrast to the resource-intensive reconstruction process, leading to an impressive reduction in the data workload to a mere 20% of the initial dataset. This capability consequently reduces computational time dramatically while preserving reconstruction quality. Through further segmentation of the diffraction patterns, our proposed approach can also detect features that are smaller than beam size and correctly classify them as within the RoI. Nature Publishing Group UK 2023-11-13 /pmc/articles/PMC10643553/ /pubmed/37957208 http://dx.doi.org/10.1038/s41598-023-45336-4 Text en © @ UChicago Argonne, LLC, Operator of Argonne National Laboratory 2023 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Lin, Dergan Jiang, Yi Deng, Junjing Di, Zichao Wendy Unsupervised classification for region of interest in X-ray ptychography |
title | Unsupervised classification for region of interest in X-ray ptychography |
title_full | Unsupervised classification for region of interest in X-ray ptychography |
title_fullStr | Unsupervised classification for region of interest in X-ray ptychography |
title_full_unstemmed | Unsupervised classification for region of interest in X-ray ptychography |
title_short | Unsupervised classification for region of interest in X-ray ptychography |
title_sort | unsupervised classification for region of interest in x-ray ptychography |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10643553/ https://www.ncbi.nlm.nih.gov/pubmed/37957208 http://dx.doi.org/10.1038/s41598-023-45336-4 |
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