Improving Compton camera imaging of multi-energy radioactive sources by using machine learning algorithms for event selection

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 LaBr<inf>3</inf> crystals coupled to silicon photomultiplier arrays was used to acqui...

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Detalles Bibliográficos
Autores: Pérez-Curbelo, J., Roser, J., Muñoz, Enrique, Barrientos, Luis, Sanz, Verónica, Llosá, Gabriela
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:dnet:digitalcsic_::2e358fad5d2de4032fbf41940e2f9c38
Acceso en línea:http://hdl.handle.net/10261/430705
https://api.elsevier.com/content/abstract/scopus_id/85204640347
Access Level:acceso abierto
Palabra clave:Compton cameras imaging
Event selection
Image reconstruction
Neural networks
Descripción
Sumario: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 LaBr<inf>3</inf> crystals coupled to silicon photomultiplier arrays was used to acquire experimental data from a circular array of <sup>22</sup>Na sources. The prototype and two arrays of <sup>22</sup>Na 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.