Autonomous Polycrystalline Material Decomposition for Hyperspectral Neutron Tomography
Hyperspectral neutron tomography is an effective method for analyzing crystalline material samples with complex compositions in a non-destructive manner. Since the counts in the hyperspectral neutron radiographs directly depend on the neutron cross-sections, materials may exhibit contrasting neutron...
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| creator | Chowdhury, Mohammad Samin Nur Yang, Diyu Tang, Shimin Venkatakrishnan, Singanallur V Bilheux, Hassina Z Buzzard, Gregery T Bouman, Charles A |
| description | Hyperspectral neutron tomography is an effective method for analyzing
crystalline material samples with complex compositions in a non-destructive
manner. Since the counts in the hyperspectral neutron radiographs directly
depend on the neutron cross-sections, materials may exhibit contrasting neutron
responses across wavelengths. Therefore, it is possible to extract the unique
signatures associated with each material and use them to separate the
crystalline phases simultaneously.
We introduce an autonomous material decomposition (AMD) algorithm to
automatically characterize and localize polycrystalline structures using Bragg
edges with contrasting neutron responses from hyperspectral data. The algorithm
estimates the linear attenuation coefficient spectra from the measured
radiographs and then uses these spectra to perform polycrystalline material
decomposition and reconstructs 3D material volumes to localize materials in the
spatial domain. Our results demonstrate that the method can accurately estimate
both the linear attenuation coefficient spectra and associated reconstructions
on both simulated and experimental neutron data. |
| doi_str_mv | 10.48550/arxiv.2302.13921 |
| format | Article |
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crystalline material samples with complex compositions in a non-destructive
manner. Since the counts in the hyperspectral neutron radiographs directly
depend on the neutron cross-sections, materials may exhibit contrasting neutron
responses across wavelengths. Therefore, it is possible to extract the unique
signatures associated with each material and use them to separate the
crystalline phases simultaneously.
We introduce an autonomous material decomposition (AMD) algorithm to
automatically characterize and localize polycrystalline structures using Bragg
edges with contrasting neutron responses from hyperspectral data. The algorithm
estimates the linear attenuation coefficient spectra from the measured
radiographs and then uses these spectra to perform polycrystalline material
decomposition and reconstructs 3D material volumes to localize materials in the
spatial domain. Our results demonstrate that the method can accurately estimate
both the linear attenuation coefficient spectra and associated reconstructions
on both simulated and experimental neutron data.</description><identifier>DOI: 10.48550/arxiv.2302.13921</identifier><language>eng</language><creationdate>2023-02</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2302.13921$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2302.13921$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Chowdhury, Mohammad Samin Nur</creatorcontrib><creatorcontrib>Yang, Diyu</creatorcontrib><creatorcontrib>Tang, Shimin</creatorcontrib><creatorcontrib>Venkatakrishnan, Singanallur V</creatorcontrib><creatorcontrib>Bilheux, Hassina Z</creatorcontrib><creatorcontrib>Buzzard, Gregery T</creatorcontrib><creatorcontrib>Bouman, Charles A</creatorcontrib><title>Autonomous Polycrystalline Material Decomposition for Hyperspectral Neutron Tomography</title><description>Hyperspectral neutron tomography is an effective method for analyzing
crystalline material samples with complex compositions in a non-destructive
manner. Since the counts in the hyperspectral neutron radiographs directly
depend on the neutron cross-sections, materials may exhibit contrasting neutron
responses across wavelengths. Therefore, it is possible to extract the unique
signatures associated with each material and use them to separate the
crystalline phases simultaneously.
We introduce an autonomous material decomposition (AMD) algorithm to
automatically characterize and localize polycrystalline structures using Bragg
edges with contrasting neutron responses from hyperspectral data. The algorithm
estimates the linear attenuation coefficient spectra from the measured
radiographs and then uses these spectra to perform polycrystalline material
decomposition and reconstructs 3D material volumes to localize materials in the
spatial domain. Our results demonstrate that the method can accurately estimate
both the linear attenuation coefficient spectra and associated reconstructions
on both simulated and experimental neutron data.</description><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj7lOxDAUAN1QoIUPoNr8QIKPxEe5Wo5FWo4i2jZ6-ABLSRzZDiJ_T1iophhppEHohuCqlk2DbyF--6-KMkwrwhQll-i0m3MYwxDmVLyFftFxSRn63o-2eIZso4e-uLM6DFNIPvswFi7E4rBMNqbJ6hxX_2LnHFfTrp2PCNPncoUuHPTJXv9zg9qH-3Z_KI-vj0_73bEELkgpFOdKC-o4MCaJNFY5LIQEJfQ7dtxgg7nRYGtLqGucNtIRSupa4poqydkGbf-y57Fuin6AuHS_g915kP0Ai5RNPw</recordid><startdate>20230227</startdate><enddate>20230227</enddate><creator>Chowdhury, Mohammad Samin Nur</creator><creator>Yang, Diyu</creator><creator>Tang, Shimin</creator><creator>Venkatakrishnan, Singanallur V</creator><creator>Bilheux, Hassina Z</creator><creator>Buzzard, Gregery T</creator><creator>Bouman, Charles A</creator><scope>GOX</scope></search><sort><creationdate>20230227</creationdate><title>Autonomous Polycrystalline Material Decomposition for Hyperspectral Neutron Tomography</title><author>Chowdhury, Mohammad Samin Nur ; Yang, Diyu ; Tang, Shimin ; Venkatakrishnan, Singanallur V ; Bilheux, Hassina Z ; Buzzard, Gregery T ; Bouman, Charles A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a671-79669c72f6a33818de9f0778a97cb0f6d0d06dcae4e12f5fcd8f1214480429863</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Chowdhury, Mohammad Samin Nur</creatorcontrib><creatorcontrib>Yang, Diyu</creatorcontrib><creatorcontrib>Tang, Shimin</creatorcontrib><creatorcontrib>Venkatakrishnan, Singanallur V</creatorcontrib><creatorcontrib>Bilheux, Hassina Z</creatorcontrib><creatorcontrib>Buzzard, Gregery T</creatorcontrib><creatorcontrib>Bouman, Charles A</creatorcontrib><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chowdhury, Mohammad Samin Nur</au><au>Yang, Diyu</au><au>Tang, Shimin</au><au>Venkatakrishnan, Singanallur V</au><au>Bilheux, Hassina Z</au><au>Buzzard, Gregery T</au><au>Bouman, Charles A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Autonomous Polycrystalline Material Decomposition for Hyperspectral Neutron Tomography</atitle><date>2023-02-27</date><risdate>2023</risdate><abstract>Hyperspectral neutron tomography is an effective method for analyzing
crystalline material samples with complex compositions in a non-destructive
manner. Since the counts in the hyperspectral neutron radiographs directly
depend on the neutron cross-sections, materials may exhibit contrasting neutron
responses across wavelengths. Therefore, it is possible to extract the unique
signatures associated with each material and use them to separate the
crystalline phases simultaneously.
We introduce an autonomous material decomposition (AMD) algorithm to
automatically characterize and localize polycrystalline structures using Bragg
edges with contrasting neutron responses from hyperspectral data. The algorithm
estimates the linear attenuation coefficient spectra from the measured
radiographs and then uses these spectra to perform polycrystalline material
decomposition and reconstructs 3D material volumes to localize materials in the
spatial domain. Our results demonstrate that the method can accurately estimate
both the linear attenuation coefficient spectra and associated reconstructions
on both simulated and experimental neutron data.</abstract><doi>10.48550/arxiv.2302.13921</doi><oa>free_for_read</oa></addata></record> |
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| title | Autonomous Polycrystalline Material Decomposition for Hyperspectral Neutron Tomography |
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