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

Paweł Konieczka (Corresponding author), 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 KorcylTomasz Kozik, Deepak Kumar, Szymon Niedźwiecki, Szymon Parzych, Elena Pérez Del Río, Sushil Sharma, Shivani Shivani, Magdalena Skurzok, Ewa Łucja Stepień, Faranak Tayefi, Paweł Moskal

Publications: Contribution to journalArticlePeer Reviewed


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.
Original languageEnglish
Pages (from-to)14938-14958
Number of pages21
JournalMathematical Biosciences and Engineering
Issue number8
Publication statusPublished - 12 Jul 2023

Austrian Fields of Science 2012

  • 103012 High energy physics
  • 302071 Radiology
  • 102019 Machine learning


  • convolutional neural network
  • kernel principal component analysis
  • medical imaging
  • Positron emission tomography

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