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Radioactive Source Localisation via Projective Linear Reconstruction
Radiation mapping, through the detection of ionising gamma-ray emissions, is an important technique used across the nuclear industry to characterise environments over a range of length scales. In complex scenarios, the precise localisation and activity of radiological sources becomes difficult to de...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865332/ https://www.ncbi.nlm.nih.gov/pubmed/33530392 http://dx.doi.org/10.3390/s21030807 |
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author | White, Samuel R. Wood, Kieran T. Martin, Peter G. Connor, Dean T. Scott, Thomas B. Megson-Smith, David A. |
author_facet | White, Samuel R. Wood, Kieran T. Martin, Peter G. Connor, Dean T. Scott, Thomas B. Megson-Smith, David A. |
author_sort | White, Samuel R. |
collection | PubMed |
description | Radiation mapping, through the detection of ionising gamma-ray emissions, is an important technique used across the nuclear industry to characterise environments over a range of length scales. In complex scenarios, the precise localisation and activity of radiological sources becomes difficult to determine due to the inability to directly image gamma photon emissions. This is a result of the potentially unknown number of sources combined with uncertainties associated with the source-detector separation—causing an apparent ‘blurring’ of the as-detected radiation field relative to the true distribution. Accurate delimitation of distinct sources is important for decommissioning, waste processing, and homeland security. Therefore, methods for estimating the precise, ‘true’ solution from radiation mapping measurements are required. Herein is presented a computational method of enhanced radiological source localisation from scanning survey measurements conducted with a robotic arm. The procedure uses an experimentally derived Detector Response Function (DRF) to perform a randomised-Kaczmarz deconvolution from robotically acquired radiation field measurements. The performance of the process is assessed on radiation maps obtained from a series of emulated waste processing scenarios. The results demonstrate a Projective Linear Reconstruction (PLR) algorithm can successfully locate a series of point sources to within 2 cm of the true locations, corresponding to resolution enhancements of between 5× and 10×. |
format | Online Article Text |
id | pubmed-7865332 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-78653322021-02-07 Radioactive Source Localisation via Projective Linear Reconstruction White, Samuel R. Wood, Kieran T. Martin, Peter G. Connor, Dean T. Scott, Thomas B. Megson-Smith, David A. Sensors (Basel) Article Radiation mapping, through the detection of ionising gamma-ray emissions, is an important technique used across the nuclear industry to characterise environments over a range of length scales. In complex scenarios, the precise localisation and activity of radiological sources becomes difficult to determine due to the inability to directly image gamma photon emissions. This is a result of the potentially unknown number of sources combined with uncertainties associated with the source-detector separation—causing an apparent ‘blurring’ of the as-detected radiation field relative to the true distribution. Accurate delimitation of distinct sources is important for decommissioning, waste processing, and homeland security. Therefore, methods for estimating the precise, ‘true’ solution from radiation mapping measurements are required. Herein is presented a computational method of enhanced radiological source localisation from scanning survey measurements conducted with a robotic arm. The procedure uses an experimentally derived Detector Response Function (DRF) to perform a randomised-Kaczmarz deconvolution from robotically acquired radiation field measurements. The performance of the process is assessed on radiation maps obtained from a series of emulated waste processing scenarios. The results demonstrate a Projective Linear Reconstruction (PLR) algorithm can successfully locate a series of point sources to within 2 cm of the true locations, corresponding to resolution enhancements of between 5× and 10×. MDPI 2021-01-26 /pmc/articles/PMC7865332/ /pubmed/33530392 http://dx.doi.org/10.3390/s21030807 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article White, Samuel R. Wood, Kieran T. Martin, Peter G. Connor, Dean T. Scott, Thomas B. Megson-Smith, David A. Radioactive Source Localisation via Projective Linear Reconstruction |
title | Radioactive Source Localisation via Projective Linear Reconstruction |
title_full | Radioactive Source Localisation via Projective Linear Reconstruction |
title_fullStr | Radioactive Source Localisation via Projective Linear Reconstruction |
title_full_unstemmed | Radioactive Source Localisation via Projective Linear Reconstruction |
title_short | Radioactive Source Localisation via Projective Linear Reconstruction |
title_sort | radioactive source localisation via projective linear reconstruction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865332/ https://www.ncbi.nlm.nih.gov/pubmed/33530392 http://dx.doi.org/10.3390/s21030807 |
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