ArticleNeuroImage2022
Face recognition from research brain PET: An unexpected PET problem.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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.
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
19 citing papers in PubMed, 23 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
- Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.Journal of imaging informatics in medicine · 2026Review
- Large Language Models in Radiologist-Patient Communication: A Narrative Review for Clinical Practice.Cureus · 2026Review
- Development of a defacing algorithm to protect the privacy of head and neck cancer patients in publicly-accessible radiotherapy datasets.Medical physics · 2025Article
- 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
- 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
- Positron emission tomography harmonization in the Alzheimer's Disease Neuroimaging Initiative: A scalable and rigorous approach to multisite amyloid and tau quantification.Alzheimer's & dementia : the journal of the Alzheimer's Association · 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
- Deep learning-based defacing tool for CT angiography: CTA-DEFACE.European radiology experimental · 2024Article
- Exploring de-anonymization risks in PET imaging: Insights from a comprehensive analysis of 853 patient scans.Scientific data · 2024Article
- Assessing the Impact of Defacing Algorithms on Brain Volumetry Accuracy in MRI Analyses.Dementia and neurocognitive disorders · 2024Article
- Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency.Human brain mapping · 2024Article
- Direct comparison betweenNeuroImage · 2024Article
- Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology.AJNR. American journal of neuroradiology · 2023Review
- A face-off of MRI research sequences by their need for de-facing.NeuroImage · 2023Article
- Facial Anonymization and Privacy Concerns in Total-Body PET/CT.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- Restricted access data in the neurosciences: Are the restrictions always justified?Frontiers in neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 4 institutions in 1 country.
Funding
Abstract
It is well known that de-identified research brain images from MRI and CT can potentially be re-identified using face recognition; however, this has not been examined for PET images. We generated face reconstruction images of 182 volunteers using amyloid, tau, and FDG PET scans, and we measured how accurately commercial face recognition software (Microsoft Azure's Face API) automatically matched them with the individual participants' face photographs. We then compared this accuracy with the same experiments using participants' CT and MRI. Face reconstructions from PET images from PET/CT scanners were correctly matched at rates of 42% (FDG), 35% (tau), and 32% (amyloid), while CT were matched at 78% and MRI at 97-98%. We propose that these recognition rates are high enough that research studies should consider using face de-identification ("de-facing") software on PET images, in addition to CT and structural MRI, before data sharing. We also updated our mri_reface de-identification software with extended functionality to replace face imagery in PET and CT images. Rates of face recognition on de-faced images were reduced to 0-4% for PET, 5% for CT, and 8% for MRI. We measured the effects of de-facing on regional amyloid PET measurements from two different measurement pipelines (PETSurfer/FreeSurfer 6.0, and one in-house method based on SPM12 and ANTs), and these effects were small: ICC values between de-faced and original images were > 0.98, biases were <2%, and median relative errors were < 2%. Effects on global amyloid PET SUVR measurements were even smaller: ICC values were 1.00, biases were <0.5%, and median relative errors were also <0.5%.
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What OpenQuestion holds
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.