Insertion of synthetic lesions on patient data: a method for evaluating clinical performance differences between PET systems.

Quentin MaronnierNesrine RobaineLéonor ChaltielLawrence O DierickxThibaut Cassou-MounatMarie TerroirLavinia VijaDelphine VallotSéverine BrillouetChloé LamesaThomas FilleronOlivier CasellesFrédéric Courbon

Published in: EJNMMI physics (2024)

ISL proved relevant to evaluate performance differences between PET scanners. Using these synthetically modified clinical images, we can produce a controlled ground truth in a realistic anatomical model and exploit the potential of PET scanner for clinical purposes.

Keyphrases

computed tomography
pet ct
positron emission tomography
deep learning
case report
magnetic resonance imaging
machine learning
magnetic resonance
climate change
convolutional neural network
human health