Multi-Pose Fusion for Autonomous Polycrystalline Material Decomposition in Hyperspectral Neutron Tomography

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

doi:10.1109/IEEECONF60004.2024.10942621

Multi-Pose Fusion for Autonomous Polycrystalline Material Decomposition in Hyperspectral Neutron Tomography

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Hyperspectral neutron computed tomography (HSnCT) is an effective technique for characterizing poly crystalline material samples. A typical scan involves making multiple HS projection measurements by rotating the sample about a single axis and using a standard algorithm for tomographic reconstruction. Recently, an autonomous polycrystalline material decomposition (APMD) algorithm was proposed to obtain accurate 3D reconstruction of the different materials or crystallographic phases in the object. However, for objects with complex compositions and shapes, using data from a single rotation axis may result in reconstructions with significant noise and inaccuracies in the material decomposition. In this paper, we present a multi-pose reconstruction algorithm to produce a single reconstruction from HSnCT data corresponding to multiple poses of the object. Our algorithm extends previous APMD work to incorporate hyperspectral neutron measurements from multiple poses, utilizing the Multi-Agent Consensus Equilibrium (MACE) framework to integrate projections from different rotation axes into a single reconstruction. We apply our method to simulated data and demonstrate that multi-pose APMD achieves significantly improved material decomposition accuracy compared to single-pose APMD.

Original language	English
Title of host publication	Conference Record of the 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	664-668
Number of pages	5
ISBN (Electronic)	9798350354058
DOIs	https://doi.org/10.1109/IEEECONF60004.2024.10942621
State	Published - 2024
Event	58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024 - Hybrid, Pacific Grove, United States Duration: Oct 27 2024 → Oct 30 2024

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Conference

Conference	58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024
Country/Territory	United States
City	Hybrid, Pacific Grove
Period	10/27/24 → 10/30/24

Funding

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/doepublic-access-plan).

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10.1109/IEEECONF60004.2024.10942621

Cite this

Yang, D., Nur Chowdhury, M. S., Tang, S., Venkatakrishnan, S. V., Bilheux, H. Z., Buzzard, G. T., & Bouman, C. A. (2024). Multi-Pose Fusion for Autonomous Polycrystalline Material Decomposition in Hyperspectral Neutron Tomography. In M. B. Matthews (Ed.), Conference Record of the 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024 (pp. 664-668). (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/IEEECONF60004.2024.10942621

Yang, Diyu ; Nur Chowdhury, Mohammad Samin ; Tang, Shimin et al. / Multi-Pose Fusion for Autonomous Polycrystalline Material Decomposition in Hyperspectral Neutron Tomography. Conference Record of the 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024. editor / Michael B. Matthews. IEEE Computer Society, 2024. pp. 664-668 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

@inproceedings{968a15c25d7b4507881a3be95000a843,

title = "Multi-Pose Fusion for Autonomous Polycrystalline Material Decomposition in Hyperspectral Neutron Tomography",

abstract = "Hyperspectral neutron computed tomography (HSnCT) is an effective technique for characterizing poly crystalline material samples. A typical scan involves making multiple HS projection measurements by rotating the sample about a single axis and using a standard algorithm for tomographic reconstruction. Recently, an autonomous polycrystalline material decomposition (APMD) algorithm was proposed to obtain accurate 3D reconstruction of the different materials or crystallographic phases in the object. However, for objects with complex compositions and shapes, using data from a single rotation axis may result in reconstructions with significant noise and inaccuracies in the material decomposition. In this paper, we present a multi-pose reconstruction algorithm to produce a single reconstruction from HSnCT data corresponding to multiple poses of the object. Our algorithm extends previous APMD work to incorporate hyperspectral neutron measurements from multiple poses, utilizing the Multi-Agent Consensus Equilibrium (MACE) framework to integrate projections from different rotation axes into a single reconstruction. We apply our method to simulated data and demonstrate that multi-pose APMD achieves significantly improved material decomposition accuracy compared to single-pose APMD.",

author = "Diyu Yang and {Nur Chowdhury}, {Mohammad Samin} and Shimin Tang and Venkatakrishnan, {Singanallur V.} and Bilheux, {Hassina Z.} and Buzzard, {Gregery T.} and Bouman, {Charles A.}",

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doi = "10.1109/IEEECONF60004.2024.10942621",

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Yang, D, Nur Chowdhury, MS, Tang, S, Venkatakrishnan, SV, Bilheux, HZ, Buzzard, GT & Bouman, CA 2024, Multi-Pose Fusion for Autonomous Polycrystalline Material Decomposition in Hyperspectral Neutron Tomography. in MB Matthews (ed.), Conference Record of the 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024. Conference Record - Asilomar Conference on Signals, Systems and Computers, IEEE Computer Society, pp. 664-668, 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024, Hybrid, Pacific Grove, United States, 10/27/24. https://doi.org/10.1109/IEEECONF60004.2024.10942621

Multi-Pose Fusion for Autonomous Polycrystalline Material Decomposition in Hyperspectral Neutron Tomography. / Yang, Diyu; Nur Chowdhury, Mohammad Samin; Tang, Shimin et al.
Conference Record of the 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024. ed. / Michael B. Matthews. IEEE Computer Society, 2024. p. 664-668 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Multi-Pose Fusion for Autonomous Polycrystalline Material Decomposition in Hyperspectral Neutron Tomography

AU - Yang, Diyu

AU - Nur Chowdhury, Mohammad Samin

AU - Tang, Shimin

AU - Venkatakrishnan, Singanallur V.

AU - Bilheux, Hassina Z.

AU - Buzzard, Gregery T.

AU - Bouman, Charles A.

PY - 2024

Y1 - 2024

N2 - Hyperspectral neutron computed tomography (HSnCT) is an effective technique for characterizing poly crystalline material samples. A typical scan involves making multiple HS projection measurements by rotating the sample about a single axis and using a standard algorithm for tomographic reconstruction. Recently, an autonomous polycrystalline material decomposition (APMD) algorithm was proposed to obtain accurate 3D reconstruction of the different materials or crystallographic phases in the object. However, for objects with complex compositions and shapes, using data from a single rotation axis may result in reconstructions with significant noise and inaccuracies in the material decomposition. In this paper, we present a multi-pose reconstruction algorithm to produce a single reconstruction from HSnCT data corresponding to multiple poses of the object. Our algorithm extends previous APMD work to incorporate hyperspectral neutron measurements from multiple poses, utilizing the Multi-Agent Consensus Equilibrium (MACE) framework to integrate projections from different rotation axes into a single reconstruction. We apply our method to simulated data and demonstrate that multi-pose APMD achieves significantly improved material decomposition accuracy compared to single-pose APMD.

AB - Hyperspectral neutron computed tomography (HSnCT) is an effective technique for characterizing poly crystalline material samples. A typical scan involves making multiple HS projection measurements by rotating the sample about a single axis and using a standard algorithm for tomographic reconstruction. Recently, an autonomous polycrystalline material decomposition (APMD) algorithm was proposed to obtain accurate 3D reconstruction of the different materials or crystallographic phases in the object. However, for objects with complex compositions and shapes, using data from a single rotation axis may result in reconstructions with significant noise and inaccuracies in the material decomposition. In this paper, we present a multi-pose reconstruction algorithm to produce a single reconstruction from HSnCT data corresponding to multiple poses of the object. Our algorithm extends previous APMD work to incorporate hyperspectral neutron measurements from multiple poses, utilizing the Multi-Agent Consensus Equilibrium (MACE) framework to integrate projections from different rotation axes into a single reconstruction. We apply our method to simulated data and demonstrate that multi-pose APMD achieves significantly improved material decomposition accuracy compared to single-pose APMD.

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Y2 - 27 October 2024 through 30 October 2024

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Yang D, Nur Chowdhury MS, Tang S, Venkatakrishnan SV, Bilheux HZ, Buzzard GT et al. Multi-Pose Fusion for Autonomous Polycrystalline Material Decomposition in Hyperspectral Neutron Tomography. In Matthews MB, editor, Conference Record of the 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024. IEEE Computer Society. 2024. p. 664-668. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/IEEECONF60004.2024.10942621

Multi-Pose Fusion for Autonomous Polycrystalline Material Decomposition in Hyperspectral Neutron Tomography

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