Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Lin, Dergan, Jiang, Yi, Deng, Junjing, Di, Zichao Wendy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
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
_version_ 1785147127301668864
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
work_keys_str_mv AT lindergan unsupervisedclassificationforregionofinterestinxrayptychography
AT jiangyi unsupervisedclassificationforregionofinterestinxrayptychography
AT dengjunjing unsupervisedclassificationforregionofinterestinxrayptychography
AT dizichaowendy unsupervisedclassificationforregionofinterestinxrayptychography