ArticleNeuroImage2021
Changing the face of neuroimaging research: Comparing a new MRI de-facing technique with popular alternatives.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
58 citing papers in PubMed.
- Quality assessment and control of unprocessed anatomical, functional and diffusion MRI of the human brain using MRIQC.Nature protocols · 2026Review
- The Impact and Reliability of Tissue Segmentation on In Vivo Magnetic Resonance Spectroscopy Metabolite Quantification.Magnetic resonance in medicine · 2026Article
- Real-World Imaging Data: Opportunities and Challenges.JMIR medical informatics · 2026Article
- Accelerating Research on Brain Aging: Enabling Brain Imaging Data Sharing in the Open Science Landscape.European journal of neurology · 2026Review
- Facial feature removal in magnetic resonance imaging scans of adults with Down syndrome: A de-facing methodological study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- From theory to practice: Re-identification Challenge to test imaging data pseudonymization effectiveness.Scientific data · 2026Article
- Transforming a clinical study database into a structured database adapted to artificial intelligence applications.Insights into imaging · 2026Article
- DefaceQA - automated quality assessment of brain MRI defacing software.BMC medical imaging · 2026Article
- The 2024 Brain Tumor Segmentation Challenge Meningioma Radiotherapy (BraTS-MEN-RT) dataset.Scientific data · 2026Article
- Study protocol for the Champaign-Urbana population study.Frontiers in neuroimaging · 2026Article
- sEEG-based brain-computer interfacing in a large adult and pediatric cohort.Journal of neural engineering · 2025Article
- Measuring the potential risk of re-identification of imaging research participants from open-source automated face recognition software.NeuroImage · 2025Article
- Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential.Computers in biology and medicine · 2025Article
- Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs.AJNR. American journal of neuroradiology · 2025Article
- Removing facial features from structural MRI images biases visual quality assessment.PLoS biology · 2025Article
- Defacing biases visual quality assessments of structural MRI.bioRxiv : the preprint server for biology · 2025Article
- An fMRI dataset for appetite neural correlates in people living with Motor Neuron Disease.Scientific data · 2025Article
- Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 2: Pathology Whole Slide Image De-identification, De-facing, the Role of AI in Image De-identification, and the NCI MIDI Datasets and Pipeline.Journal of imaging informatics in medicine · 2025Article
- Functional mapping of movement and speech using task-based electrophysiological changes in stereoelectroencephalography.Journal of neurosurgery · 2025Article
- From Scanner to Science: Reusing Clinically Acquired Medical Images for Research.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Recent advances in automated face recognition algorithms have increased the risk that de-identified research MRI scans may be re-identifiable by matching them to identified photographs using face recognition. A variety of software exist to de-face (remove faces from) MRI, but their ability to prevent face recognition has never been measured and their image modifications can alter automated brain measurements. In this study, we compared three popular de-facing techniques and introduce our mri_reface technique designed to minimize effects on brain measurements by replacing the face with a population average, rather than removing it. For each technique, we measured 1) how well it prevented automated face recognition (i.e. effects on exceptionally-motivated individuals) and 2) how it altered brain measurements from SPM12, FreeSurfer, and FSL (i.e. effects on the average user of de-identified data). Before de-facing, 97% of scans from a sample of 157 volunteers were correctly matched to photographs using automated face recognition. After de-facing with popular software, 28-38% of scans still retained enough data for successful automated face matching. Our proposed mri_reface had similar performance with the best existing method (fsl_deface) at preventing face recognition (28-30%) and it had the smallest effects on brain measurements in more pipelines than any other, but these differences were modest.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.