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Machine learning classifiers evaluated for PET scanner background rejection

By PulseAugur Editorial · [1 sources] ·

Researchers have systematically evaluated machine learning classifiers, including XGBoost, AdaBoost, and neural networks, for background rejection in LAFOV PET scanners. The study focused on event-by-event classification using Monte Carlo simulations of the Siemens Biograph Vision Quadra scanner. Results indicate that a 4-feature model, incorporating attenuation factor, time difference, and energy-related variables, generalizes better across different phantom geometries than a more complex 6-feature model. While these machine learning approaches outperform traditional methods, further gains in image quality may require larger input representations or specific handling of scattered coincidences. AI

RANK_REASON Research paper detailing a systematic evaluation of machine learning classifiers for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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Machine learning classifiers evaluated for PET scanner background rejection

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Research paper detailing a systematic evaluation of machine learning classifiers for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Konrad Klimaszewski, Micha{\l} Obara, Mateusz Bala, Beatrix C. Hiesmayr, Lech Raczy\'nski, Roman Y. Shopa, Wojciech Zdeb, Wojciech Krzemien ·

    A systematic evaluation of machine learning classifiers for event-by-event background rejection in LAFOV PET scanners

    arXiv:2607.25732v1 Announce Type: new Abstract: The introduction of LAFOV PET scanners brings significant sensitivity gains but also a substantial increase in the background rate from accidental coincidences, phantom-scattered and detector-scattered photons. While machine learnin…

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