Potential for Re-Identifying Brain PET Research Participants using Face Recognition

Christopher Schwarz, Walter K. Kremers, Val J. Lowe, Marios Savvides, Jeffrey L. Gunter, Matthew L. Senjem, Prashanthi Vemuri, Kejal Kantarci, David S Knopman, Ronald C. Petersen, Clifford R Jr. Jack

Research output: Contribution to journalComment/debatepeer-review

Abstract

Background: Studies of aging and dementia often acquire brain MRI and PET images, then de-identify and share these widely with other researchers. It is known that research participants can potentially be re-identified from brain MRI and CT using face recognition, but this has not been tested with PET imaging. Method: We generated face reconstruction images of 182 volunteers using their existing amyloid (PiB), tau (FTP), and FDG PET scans from clinical GE PET/CT scanners, and we measured how often commercial automatic face recognition (Microsoft Azure) correctly matched these reconstructions with photographs of their faces. We additionally compared these match rates with face reconstructions from CT (from PET/CT) and MRI. When testing face recognition from PET scans, CT was used only during PET reconstruction, not for face recognition. We also validated our updated mri_reface software, originally developed to de-identify (“de-face”) faces in MRI, with new support for PET images. We measured face recognition rates before and after applying mri_reface to PET, CT, and MRI. In addition, we measured effects of de-facing on biomarker measurements (global and regional SUVR) using 244 ADNI Florbetapir (FBP) amyloid PET scans with two automated pipelines: FreeSurfer 6.0 (PETSurfer) and an in-house SPM12-based pipeline. Result: Rates of successful face recognition with PET ranged from 32-41% (Table 1) across tracers. These were lower than MRI (98%) and CT (78%) but still concerning for participant privacy. Using mri_reface reduced face recognition to 0-3.5% for PET, 5% for CT, and 8% for MRI. mri_reface had little effect on global amyloid SUVR measurements from ADNI PET images (Figure 1). ICC values between original and de-faced images were 1.00 with biases and median absolute differences < 0.5%, across both pipelines. Effects on individual regions all had ICC values > 0.98 with biases and median absolute differences < 2%. Conclusion: Face reconstructions from PET images have sufficient quality for potential re-identification using publicly available face recognition technology. Research studies should consider using face de-identification (de-facing) software on PET images in addition to CT and structural MRI. Our free, automatic mri_reface software greatly reduced the potential for face recognition without substantially affecting SUVR measurements.

Original languageEnglish (US)
Article numbere063652
JournalAlzheimer's and Dementia
Volume18
Issue numberS1
DOIs
StatePublished - Dec 2022

ASJC Scopus subject areas

  • Epidemiology
  • Health Policy
  • Developmental Neuroscience
  • Clinical Neurology
  • Geriatrics and Gerontology
  • Cellular and Molecular Neuroscience
  • Psychiatry and Mental health

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