Our paper "IRMA: Machine learning-based harmonization of 18F-FDG PET brain scans in multi-center studies" by Sofie Lövdal et al. has been published in the European Journal of Nuclear Medicine and Molecular Imaging and is available online (open access): https://link.springer.com/article/10.1007/s00259-025-07114-4 In this work we apply interpretable machine learning systems for the analysis of 18-FDG PET brain scans from different medical centers. We show that the center origin of healthy control brain images acquired with different scanners/protocols can be identified with high confidence. Consequently, machine learning models trained with data from different scanners may be heavily impacted by this bias when applied to a clinically relevant problem, e.g. the differential diagnosis of neurodegenerative disorders. We propose IRMA (Iterated Relevance Matrix Analysis) as a recursive method to learn and disregard bias in PET feature vectors. Related code is freely available at https://github.com/SofieLovdal/IRMA-harmonization --------------------------------------------------- Prof. Dr. Michael Biehl Bernoulli Institute for Mathematics, Computer Science & Artificial Intelligence P.O. Box 407, 9700 AK Groningen, NL https://www.cs.rug.nl/~biehl m.biehl@rug.nl