ArticleBioengineering (Basel, Switzerland)2022
Modified MRI Anonymization (De-Facing) for Improved MEG Coregistration.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- De-Identification of Magnetic Resonance Imaging to Protect Patient Privacy in Research Use: A Comprehensive Review.Healthcare informatics research · 2026Article
- Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential.Computers in biology and medicine · 2025Article
- Assessing the Impact of Defacing Algorithms on Brain Volumetry Accuracy in MRI Analyses.Dementia and neurocognitive disorders · 2024Article
- Automatic Estimation of the Interference Subspace Dimension Threshold in the Subspace Projection Algorithms of Magnetoencephalography Based on Evoked State Data.Bioengineering (Basel, Switzerland) · 2024Article
- Reproducibility evaluation of the effects of MRI defacing on brain segmentation.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- A multi-site, multi-participant magnetoencephalography resting-state dataset to study dementia: The BioFIND dataset.NeuroImage · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Localising the sources of MEG/EEG signals often requires a structural MRI to create a head model, while ensuring reproducible scientific results requires sharing data and code. However, sharing structural MRI data often requires the face go be hidden to help protect the identity of the individuals concerned. While automated de-facing methods exist, they tend to remove the whole face, which can impair methods for coregistering the MRI data with the EEG/MEG data. We show that a new, automated de-facing method that retains the nose maintains good MRI-MEG/EEG coregistration. Importantly, behavioural data show that this "face-trimming" method does not increase levels of identification relative to a standard de-facing approach and has less effect on the automated segmentation and surface extraction sometimes used to create head models for MEG/EEG localisation. We suggest that this trimming approach could be employed for future sharing of structural MRI data, at least for those to be used in forward modelling (source reconstruction) of EEG/MEG data.
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Registered trials
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