Defacing Magnetic Resonance Imaging Algorithms: A Review on Privacy and Data Integrity

Neuroimaging datasets are increasingly shared in open repositories for research purposes, raising concerns about participant re-identification through facial features visible in brain magnetic resonance imaging (MRI) scans. MRI defacing algorithms address this risk by obscuring identifiable facial structures while preserving brain tissue for analysis. Algorithm performance varies substantially by context. For re-identification prevention, fsl_deface and mri_reface achieve the lowest recognition rates, while afni_refacer and pydeface demonstrate the highest processing success rates. However, all algorithms affect brain volumetric measurements to varying degrees, with some causing failures in automated segmentation pipelines. Performance is notably age-dependent, with specific algorithms underperforming in pediatric or elderly cohorts and in clinical populations with neurological disorders. Optimal algorithm selection depends on research priorities. For preserving brain measurements, mri_reface and SPM-based defacing are preferred; for pediatric studies, FreeSurfer better preserves brain voxels; for electroencephalography (EEG) and magnetoencephalography (MEG) co-registration, AnonyMI provides superior geometrical preservation. This review examines the major defacing algorithms and their validation across diverse datasets, evaluating effectiveness in preventing re-identification, preserving brain measurements, and maintaining compatibility across age groups. A comparative discussion highlights the trade-offs between privacy protection and data utility, emphasizing the need for a study-specific approach when selecting a defacing method.