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. 2023 Jul 12;20(8):14938-14958.
doi: 10.3934/mbe.2023669.

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

Paweł Konieczka  1 , Lech Raczyński  1 , Wojciech Wiślicki  1 , Oleksandr Fedoruk  1 , Konrad Klimaszewski  1 , Przemysław Kopka  1 , Wojciech Krzemień  2 , Roman Y Shopa  1 , Jakub Baran  3   4 , Aurélien Coussat  3   4 , Neha Chug  3   4 , Catalina Curceanu  5 , Eryk Czerwiński  3   4 , Meysam Dadgar  3   4 , Kamil Dulski  3   4 , Aleksander Gajos  3   4 , Beatrix C Hiesmayr  6 , Krzysztof Kacprzak  3   4 , Łukasz Kapłon  3   4 , Grzegorz Korcyl  3   4 , Tomasz Kozik  3   4 , Deepak Kumar  3   4 , Szymon Niedźwiecki  3   4 , Szymon Parzych  3   4 , Elena Pérez Del Río  3   4 , Sushil Sharma  3   4 , Shivani Shivani  3   4 , Magdalena Skurzok  3   4   5 , Ewa Łucja Stępień  3   4 , Faranak Tayefi  3   4 , Paweł Moskal  3   4
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Free article

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

Paweł Konieczka et al. Math Biosci Eng. .
Free article

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.

Keywords: convolutional neural network; kernel principal component analysis; medical imaging; positron emission tomography.

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