ArticleNeuroImage2023
A face-off of MRI research sequences by their need for de-facing.
Article in NeuroImage, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 19 citations in OpenAlex.
- 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
- De-Identification of Magnetic Resonance Imaging to Protect Patient Privacy in Research Use: A Comprehensive Review.Healthcare informatics research · 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
- Measuring the potential risk of re-identification of imaging research participants from open-source automated face recognition software.NeuroImage · 2025Article
- Manual and automated facial de-identification techniques for patient imaging with preservation of sinonasal anatomy.International journal of computer assisted radiology and surgery · 2025Article
- Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential.Computers in biology and medicine · 2025Article
- Re-identification of anonymised MRI head images with publicly available software: investigation of the current risk to patient privacy.EClinicalMedicine · 2024Article
- Implementation and validation of face de-identification (de-facing) in ADNI4.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Assessing the Impact of Defacing Algorithms on Brain Volumetry Accuracy in MRI Analyses.Dementia and neurocognitive disorders · 2024Article
- Demystifying the likelihood of reidentification in neuroimaging data: A technical and regulatory analysis.Imaging neuroscience (Cambridge, Mass.) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 4 institutions in 1 country.
Funding
Abstract
It is now widely known that research brain MRI, CT, and PET images may potentially be re-identified using face recognition, and this potential can be reduced by applying face-deidentification ("de-facing") software. However, for research MRI sequences beyond T1-weighted (T1-w) and T2-FLAIR structural images, the potential for re-identification and quantitative effects of de-facing are both unknown, and the effects of de-facing T2-FLAIR are also unknown. In this work we examine these questions (where applicable) for T1-w, T2-w, T2*-w, T2-FLAIR, diffusion MRI (dMRI), functional MRI (fMRI), and arterial spin labelling (ASL) sequences. Among current-generation, vendor-product research-grade sequences, we found that 3D T1-w, T2-w, and T2-FLAIR were highly re-identifiable (96-98%). 2D T2-FLAIR and 3D multi-echo GRE (ME-GRE) were also moderately re-identifiable (44-45%), and our derived T2* from ME-GRE (comparable to a typical 2D T2*) matched at only 10%. Finally, diffusion, functional and ASL images were each minimally re-identifiable (0-8%). Applying de-facing with mri_reface version 0.3 reduced successful re-identification to ≤8%, while differential effects on popular quantitative pipelines for cortical volumes and thickness, white matter hyperintensities (WMH), and quantitative susceptibility mapping (QSM) measurements were all either comparable with or smaller than scan-rescan estimates. Consequently, high-quality de-facing software can greatly reduce the risk of re-identification for identifiable MRI sequences with only negligible effects on automated intracranial measurements. The current-generation echo-planar and spiral sequences (dMRI, fMRI, and ASL) each had minimal match rates, suggesting that they have a low risk of re-identification and can be shared without de-facing, but this conclusion should be re-evaluated if they are acquired without fat suppression, with a full-face scan coverage, or if newer developments reduce the current levels of artifacts and distortion around the face.
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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.