Neural network method for localization of radioactive sources within a partially coded field-of-view in coded-aperture imaging
Copyright © 2021 Elsevier Ltd. All rights reserved..
Coded-aperture imagers typically have a smaller field-of-view (FOV) than in un-collimated gamma imaging systems. However, sources out of the fully coded field-of-view (FCFOV) can cause pseudo hotspots on the wrong side of an image reconstructed using the cross-correlation method. In this work, we propose a neural network method to identify and localize the sources within the partially coded field-of-view (PCFOV). The model was trained using Monte Carlo simulation data and evaluated with both simulation and experimental data. The results showed that the proposed model can identify and localize sources with good classification accuracy, low positioning error, and strong robustness to the statistical noise.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:170 |
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| Enthalten in: |
Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine - 170(2021) vom: 20. Apr., Seite 109637 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Qi [VerfasserIn] |
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| Links: |
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| Themen: |
Coded-aperture imaging |
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| Anmerkungen: |
Date Completed 12.04.2021 Date Revised 12.04.2021 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.apradiso.2021.109637 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM321412540 |
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| 245 | 1 | 0 | |a Neural network method for localization of radioactive sources within a partially coded field-of-view in coded-aperture imaging |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Copyright © 2021 Elsevier Ltd. All rights reserved. | ||
| 520 | |a Coded-aperture imagers typically have a smaller field-of-view (FOV) than in un-collimated gamma imaging systems. However, sources out of the fully coded field-of-view (FCFOV) can cause pseudo hotspots on the wrong side of an image reconstructed using the cross-correlation method. In this work, we propose a neural network method to identify and localize the sources within the partially coded field-of-view (PCFOV). The model was trained using Monte Carlo simulation data and evaluated with both simulation and experimental data. The results showed that the proposed model can identify and localize sources with good classification accuracy, low positioning error, and strong robustness to the statistical noise | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Coded-aperture imaging | |
| 650 | 4 | |a Neural network | |
| 650 | 4 | |a Nuclear security | |
| 650 | 4 | |a Partially coded field-of-view | |
| 700 | 1 | |a Cheng, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Tuo, Xianguo |e verfasserin |4 aut | |
| 700 | 1 | |a Mu, Yuxuan |e verfasserin |4 aut | |
| 700 | 1 | |a Xiao, Yongfu |e verfasserin |4 aut | |
| 700 | 1 | |a Xiong, Yisheng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Tao |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine |d 1994 |g 170(2021) vom: 20. Apr., Seite 109637 |w (DE-627)NLM074730231 |x 1872-9800 |7 nnns |
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