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Author  |
Perez-Curbelo, J.; Roser, J.; Muñoz, E.; Barrientos, L.; Sanz, V.; Llosa, G. |
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Title |
Improving Compton camera imaging of multi-energy radioactive sources by using machine learning algorithms for event selection |
Type |
Journal Article |
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Year |
2025 |
Publication |
Radiation Physics and Chemistry |
Abbreviated Journal |
Radiat. Phys. Chem. |
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Volume |
226 |
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Pages |
112166 - 11pp |
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Keywords |
Compton cameras imaging; Event selection; Neural networks; Image reconstruction |
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Abstract |
Event selection and background reduction for Compton camera imaging of multi-energy radioactive sources has been performed by employing neural networks. A Compton camera prototype with detectors made of LaBr3 crystals coupled to silicon photomultiplier arrays was used to acquire experimental data from a circular array of Na-22 sources. The prototype and two arrays of Na-22 sources were simulated with GATE v8.2 Monte Carlo code, to obtain data for neural network training. Neural network models were trained on simulated data for event classification. The optimum models were found by using Weights & Biases platform tools. The trained models were used to classify simulated and real data for selecting signal events and rejecting background prior to image reconstruction. The models performed well on simulated data. The image obtained with experimental data showed an improvement with respect to event selection with energy cuts. The method is promising for Compton camera imaging of multi-energy radioactive sources. |
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Address |
[Perez-Curbelo, J.; Roser, J.; Munoz, E.; Barrientos, L.; Sanz, V.; Llosa, G.] CSIC UV, Inst Fis Corpuscular IFIC, Valencia, Spain, Email: Javier.Perez.Curbelo@ific.uv.es |
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Publisher |
Pergamon-Elsevier Science Ltd |
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Language |
English |
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Original Title |
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Series Volume |
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ISSN |
0969-806x |
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Notes |
WOS:001325220200001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
6269 |
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