Autonomous Polycrystalline Material Decomposition For Hyperspectral Neutron Tomography

Mohammad Samin Nur Chowdhury, Diyu Yang, Shimin Tang, Singanallur V. Venkatakrishnan, Hassina Z. Bilheux, Gregery T. Buzzard, Charles A. Bouman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Image Processing, ICIP 2023 - Proceedings
PublisherIEEE Computer Society
Pages1280-1284
Number of pages5
ISBN (Electronic)9781728198354
DOIs
StatePublished - 2023
Event30th IEEE International Conference on Image Processing, ICIP 2023 - Kuala Lumpur, Malaysia
Duration: Oct 8 2023Oct 11 2023

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference30th IEEE International Conference on Image Processing, ICIP 2023
Country/TerritoryMalaysia
CityKuala Lumpur
Period10/8/2310/11/23

Funding

G. Buzzard was partially supported by NSF CCF-1763896, and C. Bouman was partially supported by the Showalter Trust. This research used resources at the Spallation Neutron Source, a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

  • clustering
  • material decomposition
  • neutron computed tomography
  • non-negative matrix factorization

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