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On-demand correction for X-ray response non-uniformity in microstrip detectors by a data-driven approach

A statistical approach, which was previously developed to correct scattering data for X-ray response non-uniformity (XRNU) in microstrip detectors, has been improved to significantly reduce the correcting time. The improved algorithm has succeeded in increasing the utilization rate of data acquired...

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Detalles Bibliográficos
Autores principales: Kato, Kenichi, Shigeta, Kazuya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7467352/
https://www.ncbi.nlm.nih.gov/pubmed/32876591
http://dx.doi.org/10.1107/S1600577520008929
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author Kato, Kenichi
Shigeta, Kazuya
author_facet Kato, Kenichi
Shigeta, Kazuya
author_sort Kato, Kenichi
collection PubMed
description A statistical approach, which was previously developed to correct scattering data for X-ray response non-uniformity (XRNU) in microstrip detectors, has been improved to significantly reduce the correcting time. The improved algorithm has succeeded in increasing the utilization rate of data acquired for reference intensity to 98%. As a result, the correcting time was reduced from half a day to half an hour, which was shorter than the typical measuring time of a sample. Moreover, the present approach was found to yield better correction results than the previous one. The data-driven approach enabled the on-demand correction for XRNU according to the detector and experimental settings. The present study will encourage the correction of scattering data for XRNU in area detectors.
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spelling pubmed-74673522020-09-15 On-demand correction for X-ray response non-uniformity in microstrip detectors by a data-driven approach Kato, Kenichi Shigeta, Kazuya J Synchrotron Radiat Research Papers A statistical approach, which was previously developed to correct scattering data for X-ray response non-uniformity (XRNU) in microstrip detectors, has been improved to significantly reduce the correcting time. The improved algorithm has succeeded in increasing the utilization rate of data acquired for reference intensity to 98%. As a result, the correcting time was reduced from half a day to half an hour, which was shorter than the typical measuring time of a sample. Moreover, the present approach was found to yield better correction results than the previous one. The data-driven approach enabled the on-demand correction for XRNU according to the detector and experimental settings. The present study will encourage the correction of scattering data for XRNU in area detectors. International Union of Crystallography 2020-08-11 /pmc/articles/PMC7467352/ /pubmed/32876591 http://dx.doi.org/10.1107/S1600577520008929 Text en © Kato and Shigeta 2020 http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.http://creativecommons.org/licenses/by/4.0/
spellingShingle Research Papers
Kato, Kenichi
Shigeta, Kazuya
On-demand correction for X-ray response non-uniformity in microstrip detectors by a data-driven approach
title On-demand correction for X-ray response non-uniformity in microstrip detectors by a data-driven approach
title_full On-demand correction for X-ray response non-uniformity in microstrip detectors by a data-driven approach
title_fullStr On-demand correction for X-ray response non-uniformity in microstrip detectors by a data-driven approach
title_full_unstemmed On-demand correction for X-ray response non-uniformity in microstrip detectors by a data-driven approach
title_short On-demand correction for X-ray response non-uniformity in microstrip detectors by a data-driven approach
title_sort on-demand correction for x-ray response non-uniformity in microstrip detectors by a data-driven approach
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7467352/
https://www.ncbi.nlm.nih.gov/pubmed/32876591
http://dx.doi.org/10.1107/S1600577520008929
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