Titel
Transformation of PET raw data into images for event classification using convolutional neural networks
Autor*in
Paweł Konieczka
Department of Complex Systems, National Centre for Nuclear Research, Poland
Lech Raczyński
Department of Complex Systems, National Centre for Nuclear Research, Poland
Wojciech Wiślicki
Department of Complex Systems, National Centre for Nuclear Research, Poland
... show all
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.
Stichwort
positron emission tomographyconvolutional neural networkkernel principal component
analysismedical imaging
Objekt-Typ
Sprache
Englisch [eng]
Persistent identifier
phaidra.univie.ac.at/o:2065399
Erschienen in
Titel
Mathematical Biosciences and Engineering
Band
20
Ausgabe
8
ISSN
1551-0018
Erscheinungsdatum
2023
Seitenanfang
14938
Seitenende
14958
Publication
American Institute of Mathematical Sciences (AIMS)
Fördergeber
Erscheinungsdatum
2023
Zugänglichkeit
Rechte
Rechteangabe
© 2023 the Author(s)
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