Quantitative imaging and automated fuel pin identification for passive gamma emission tomography

Abstract Compliance of member States to the Treaty on the Non-Proliferation of Nuclear Weapons is monitored through nuclear safeguards. The Passive Gamma Emission Tomography (PGET) system is a novel instrument developed within the framework of the International Atomic Energy Agency (IAEA) project JN...

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Autores principales:	Ming Fang, Yoann Altmann, Daniele Della Latta, Massimiliano Salvatori, Angela Di Fulvio
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c762b902b09646758cde2b67bb0a46ff
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spelling	oai:doaj.org-article:c762b902b09646758cde2b67bb0a46ff2021-12-02T13:57:49ZQuantitative imaging and automated fuel pin identification for passive gamma emission tomography10.1038/s41598-021-82031-82045-2322https://doaj.org/article/c762b902b09646758cde2b67bb0a46ff2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82031-8https://doaj.org/toc/2045-2322Abstract Compliance of member States to the Treaty on the Non-Proliferation of Nuclear Weapons is monitored through nuclear safeguards. The Passive Gamma Emission Tomography (PGET) system is a novel instrument developed within the framework of the International Atomic Energy Agency (IAEA) project JNT 1510, which included the European Commission, Finland, Hungary and Sweden. The PGET is used for the verification of spent nuclear fuel stored in water pools. Advanced image reconstruction techniques are crucial for obtaining high-quality cross-sectional images of the spent-fuel bundle to allow inspectors of the IAEA to monitor nuclear material and promptly identify its diversion. In this work, we have developed a software suite to accurately reconstruct the spent-fuel cross sectional image, automatically identify present fuel rods, and estimate their activity. Unique image reconstruction challenges are posed by the measurement of spent fuel, due to its high activity and the self-attenuation. While the former is mitigated by detector physical collimation, we implemented a linear forward model to model the detector responses to the fuel rods inside the PGET, to account for the latter. The image reconstruction is performed by solving a regularized linear inverse problem using the fast-iterative shrinkage-thresholding algorithm. We have also implemented the traditional filtered back projection (FBP) method based on the inverse Radon transform for comparison and applied both methods to reconstruct images of simulated mockup fuel assemblies. Higher image resolution and fewer reconstruction artifacts were obtained with the inverse-problem approach, with the mean-square-error reduced by 50%, and the structural-similarity improved by 200%. We then used a convolutional neural network (CNN) to automatically identify the bundle type and extract the pin locations from the images; the estimated activity levels finally being compared with the ground truth. The proposed computational methods accurately estimated the activity levels of the present pins, with an associated uncertainty of approximately 5%.Ming FangYoann AltmannDaniele Della LattaMassimiliano SalvatoriAngela Di FulvioNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ming Fang Yoann Altmann Daniele Della Latta Massimiliano Salvatori Angela Di Fulvio Quantitative imaging and automated fuel pin identification for passive gamma emission tomography
description	Abstract Compliance of member States to the Treaty on the Non-Proliferation of Nuclear Weapons is monitored through nuclear safeguards. The Passive Gamma Emission Tomography (PGET) system is a novel instrument developed within the framework of the International Atomic Energy Agency (IAEA) project JNT 1510, which included the European Commission, Finland, Hungary and Sweden. The PGET is used for the verification of spent nuclear fuel stored in water pools. Advanced image reconstruction techniques are crucial for obtaining high-quality cross-sectional images of the spent-fuel bundle to allow inspectors of the IAEA to monitor nuclear material and promptly identify its diversion. In this work, we have developed a software suite to accurately reconstruct the spent-fuel cross sectional image, automatically identify present fuel rods, and estimate their activity. Unique image reconstruction challenges are posed by the measurement of spent fuel, due to its high activity and the self-attenuation. While the former is mitigated by detector physical collimation, we implemented a linear forward model to model the detector responses to the fuel rods inside the PGET, to account for the latter. The image reconstruction is performed by solving a regularized linear inverse problem using the fast-iterative shrinkage-thresholding algorithm. We have also implemented the traditional filtered back projection (FBP) method based on the inverse Radon transform for comparison and applied both methods to reconstruct images of simulated mockup fuel assemblies. Higher image resolution and fewer reconstruction artifacts were obtained with the inverse-problem approach, with the mean-square-error reduced by 50%, and the structural-similarity improved by 200%. We then used a convolutional neural network (CNN) to automatically identify the bundle type and extract the pin locations from the images; the estimated activity levels finally being compared with the ground truth. The proposed computational methods accurately estimated the activity levels of the present pins, with an associated uncertainty of approximately 5%.
format	article
author	Ming Fang Yoann Altmann Daniele Della Latta Massimiliano Salvatori Angela Di Fulvio
author_facet	Ming Fang Yoann Altmann Daniele Della Latta Massimiliano Salvatori Angela Di Fulvio
author_sort	Ming Fang
title	Quantitative imaging and automated fuel pin identification for passive gamma emission tomography
title_short	Quantitative imaging and automated fuel pin identification for passive gamma emission tomography
title_full	Quantitative imaging and automated fuel pin identification for passive gamma emission tomography
title_fullStr	Quantitative imaging and automated fuel pin identification for passive gamma emission tomography
title_full_unstemmed	Quantitative imaging and automated fuel pin identification for passive gamma emission tomography
title_sort	quantitative imaging and automated fuel pin identification for passive gamma emission tomography
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c762b902b09646758cde2b67bb0a46ff
work_keys_str_mv	AT mingfang quantitativeimagingandautomatedfuelpinidentificationforpassivegammaemissiontomography AT yoannaltmann quantitativeimagingandautomatedfuelpinidentificationforpassivegammaemissiontomography AT danieledellalatta quantitativeimagingandautomatedfuelpinidentificationforpassivegammaemissiontomography AT massimilianosalvatori quantitativeimagingandautomatedfuelpinidentificationforpassivegammaemissiontomography AT angeladifulvio quantitativeimagingandautomatedfuelpinidentificationforpassivegammaemissiontomography
_version_	1718392285273522176

Quantitative imaging and automated fuel pin identification for passive gamma emission tomography

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