Transformation of PET raw data into images for event classification using convolutional neural networks

Paweł Konieczka; Lech Raczyński; Wojciech Wiślicki; Oleksandr Fedoruk; Konrad Klimaszewski; Przemysław Kopka; Wojciech Krzemień; Roman Y. Shopa; Jakub Baran; Aurélien Coussat; Neha Chug; Catalina Curceanu; Eryk Czerwiński; Meysam Dadgar; Kamil Dulski; Aleksander Gajos; Beatrix C. Hiesmayr; Krzysztof Kacprzak; Łukasz Kapłon; Grzegorz Korcyl; Tomasz Kozik; Deepak Kumar; Szymon Niedźwiecki; Szymon Parzych; Elena Pérez del Río; Sushil Sharma; Shivani Shivani; Magdalena Skurzok; Ewa Łucja Stępień; Faranak Tayefi; Paweł Moskal; Paweł Konieczka; Lech Raczyński; Wojciech Wiślicki; Oleksandr Fedoruk; Konrad Klimaszewski; Przemysław Kopka; Wojciech Krzemień; Roman Y. Shopa; Jakub Baran; Aurélien Coussat; Neha Chug; Catalina Curceanu; Eryk Czerwiński; Meysam Dadgar; Kamil Dulski; Aleksander Gajos; Beatrix C. Hiesmayr; Krzysztof Kacprzak; Łukasz Kapłon; Grzegorz Korcyl; Tomasz Kozik; Deepak Kumar; Szymon Niedźwiecki; Szymon Parzych; Elena Pérez del Río; Sushil Sharma; Shivani Shivani; Magdalena Skurzok; Ewa Łucja Stępień; Faranak Tayefi; Paweł Moskal

doi:10.3934/mbe.2023669

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 8: 14938-14958. doi: 10.3934/mbe.2023669

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Transformation of PET raw data into images for event classification using convolutional neural networks

1.
Department of Complex Systems, National Centre for Nuclear Research, 05-400 Świerk, Poland
2.
High Energy Physics Division, National Centre for Nuclear Research, 05-400 Świerk, Poland
3.
Marian Smoluchowski Institute of Physics, Jagiellonian University, 31-348 Cracow, Poland
4.
Center for Theranostics, Jagiellonian University, 31-348 Cracow, Poland
5.
INFN, National Laboratory of Frascati, 00044 Frascati, Italy
6.
University of Vienna, Faculty of Physics, 1090 Vienna, Austria

Received: 09 March 2023 Revised: 09 June 2023 Accepted: 25 June 2023 Published: 12 July 2023

In positron emission tomography (PET) studies, convolutional neural networks (CNNs) may be applied directly to the reconstructed distribution of radioactive tracers injected into the patient's body, as a pattern recognition tool. Nonetheless, unprocessed PET coincidence data exist in tabular format. This paper develops the transformation of tabular data into $ n $-dimensional matrices, as a preparation stage for classification based on CNNs. This method explicitly introduces a nonlinear transformation at the feature engineering stage and then uses principal component analysis to create the images. We apply the proposed methodology to the classification of simulated PET coincidence events originating from NEMA IEC and anthropomorphic XCAT phantom. Comparative studies of neural network architectures, including multilayer perceptron and convolutional networks, were conducted. The developed method increased the initial number of features from 6 to 209 and gave the best precision results (79.8$ % $) for all tested neural network architectures; it also showed the smallest decrease when changing the test data to another phantom.
- positron emission tomography,
- convolutional neural network,
- kernel principal component analysis,
- medical imaging
Citation: Paweł Konieczka, Lech Raczyński, Wojciech Wiślicki, Oleksandr Fedoruk, Konrad Klimaszewski, Przemysław Kopka, Wojciech Krzemień, Roman Y. Shopa, Jakub Baran, Aurélien Coussat, Neha Chug, Catalina Curceanu, Eryk Czerwiński, Meysam Dadgar, Kamil Dulski, Aleksander Gajos, Beatrix C. Hiesmayr, Krzysztof Kacprzak, Łukasz Kapłon, Grzegorz Korcyl, Tomasz Kozik, Deepak Kumar, Szymon Niedźwiecki, Szymon Parzych, Elena Pérez del Río, Sushil Sharma, Shivani Shivani, Magdalena Skurzok, Ewa Łucja Stępień, Faranak Tayefi, Paweł Moskal. Transformation of PET raw data into images for event classification using convolutional neural networks[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14938-14958. doi: 10.3934/mbe.2023669

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Abstract

In positron emission tomography (PET) studies, convolutional neural networks (CNNs) may be applied directly to the reconstructed distribution of radioactive tracers injected into the patient's body, as a pattern recognition tool. Nonetheless, unprocessed PET coincidence data exist in tabular format. This paper develops the transformation of tabular data into $ n $-dimensional matrices, as a preparation stage for classification based on CNNs. This method explicitly introduces a nonlinear transformation at the feature engineering stage and then uses principal component analysis to create the images. We apply the proposed methodology to the classification of simulated PET coincidence events originating from NEMA IEC and anthropomorphic XCAT phantom. Comparative studies of neural network architectures, including multilayer perceptron and convolutional networks, were conducted. The developed method increased the initial number of features from 6 to 209 and gave the best precision results (79.8$ % $) for all tested neural network architectures; it also showed the smallest decrease when changing the test data to another phantom.

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