Convolutional neural networks in the classification of multiphoton coincidences in a J-PET scanner

• Autorzy: Konieczka Paweł , Raczyński Lech , Wiślicki Wojciech

Identyfikatory

DOI

10.5604/01.3001.0054.1823

• Języki publikacji: EN

Abstrakty

EN

This work describes an investigation into the utilization of convolutional neural networks for the classification of three-photon coincidences, focusing specifically on the para- -positronium decay associated with a photon from nuclear deexcitation. The data were simulated using the Monte Carlo method, with scandium-44 as the source of β+ decays. A preprocessing method that allowed for initial cleaning of the training data was described. Subsequently, the parameters of the method for transforming tabular data into images were optimized. Finally, the created images were used to train a binary classifier using a convolutional network model. The developed data preprocessing step and transformation method into image format enabled the achievement of a precision rate of 52% at a sensitivity level of 95%, which was a 10 percentage point improvement compared to the logistic regression model.

Słowa kluczowe

EN

positron emission tomography; convolutional neural network; positronium; J-PET; para-positronium

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2023
• Tom: Vol. 19, no. 1
• Strony: 43--47
• Opis fizyczny: Bibliogr. 17 poz., rys., tab.

Twórcy

autor

Konieczka Paweł

• Department of Complex Systems, National Centre for Nuclear Research, Otwock-Świerk, Poland

autor

Raczyński Lech

• Department of Complex Systems, National Centre for Nuclear Research, Otwock-Świerk, Poland

• https://orcid.org/0000-0002-7039-2084

autor

Wiślicki Wojciech

• Department of Complex Systems, National Centre for Nuclear Research, Otwock-Świerk, Poland

• https://orcid.org/0000-0001-5765-6308

Bibliografia

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• 3. Jean JY, Mallon PE, Schrader DM. Introduction to Positron and Positronium Chemistry. In: Jean JY, Mallon PE, Schrader DM, editors. Principles and applications of positron and positronium chemistry. Singapore: World Scientific Publishing Co Pte Ltd; 2003. p. 1-15.
• 4. Moskal P. Positronium Imaging. In: 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE 2020; 2020; New York City. USA: 2020. p. 1-3.
• 5. Moskal P, Dulski K, Chug N, Curceanu C, Czerwiński E, Dadgar M, et al.: Positronium imaging with the novel multiphoton PET scanner. Sci. Adv. 2021;7:eabh4394.
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• 8. Moskal P, Niedźwiecki S, Bednarski T, Czerwiński E, Kapłon Ł, Kubicz E, et al. Test of a single module of the J-PET scanner based on plastic scintillators. Nucl. Instrum. Meth. Phys. Res. A. 2014;764:317-21.
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• 12. Jan S, Santin G, Strul D, Staelens S, Assie K, Autret D, et al. GATE: a simulation toolkit for PET and SPECT. Phys. Med. Biol. 2004;49:4543-62.
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• 14. Dadgar M, Kowalski P. Gate simulation study of the 24-module J-PET scanner: Data analysis and image reconstruction. Acta Physica Polonica B. 2020;51:309-15.
• 15. Baran J, Krzemień W, Raczyński L, Bala M, Coussat A, Parzych S, et al. Realistic Total-Body J-PET Geometry Optimization - Monte Carlo Study. arXiv preprint. 2022;arXiv:2212.02285.
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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).