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This paper discusses the use of machine learning algorithms to improve depth-of-interaction (DOI) resolution in pixellated positron emission tomography (PET) detectors, addressing the common parallax error issue. Two neural network approaches, a dense and a convolutional neural network, were tested against a multiple linear regression method, showing significant improvements in DOI resolution compared to conventional methods. The study highlights the potential of these machine learning techniques to enhance

Author

Anes Mohammed

Language

EN