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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., Competing Interests: Declaration of Competing Interest • Dr. Schwarz receives research funding from the National Institutes of Health, related and unrelated to this study. • Dr. Kremers received grant funding from NIH for this study, and from NIH, DOD, AstraZeneca, Biogen and Roche unrelated to this study. • Dr. Arani receives research funding from the NIH, unrelated to this study. • Dr. Savvides reports no disclosures. • Dr. Reid reports no disclosures. • Dr. Gunter receives funding from the NIH. • Mr. Senjem owns or has owned stock in medical related companies, unrelated to the current work, within the past 36 months: Align Technology, Inc., Inovio Pharmaceuticals, Inc., Mesa Laboratories, Inc., Nvidia, Inc., Johnson and Johnson, LHC Group, Inc., Natus Medical Inc., Varex Imaging Corporation. • Dr. Cogswell reports no disclosures. • Dr. Vemuri receives funding from the NIH. • Dr. Kantarci consults for Biogen Inc., receives research support from Avid Radiopharmaceuticals and Eli Lilly, and receives funding from NIH and Alzheimer's Drug Discovery Foundation. • Dr. Knopman served on a Data Safety Monitoring Board for the DIAN study. He served on a Data Safety monitoring Board for a tau therapeutic for Biogen but received no personal compensation. He is an investigator in clinical trials sponsored by Biogen, Lilly Pharmaceuticals and the University of Southern California. He serves as a consultant for Samus Therapeutics, Roche, Magellan Health and Alzeca Biosciences but receives no personal compensation. He receives research support from the NIH. • Dr. Petersen is a consultant for Roche, Inc., Merck, Inc., Biogen, Inc., Nestle, Inc., and Eisai, Inc., served on a DSMB for Genentech, Inc.; receives royalties from publishing Mild Cognitive Impairment (Oxford University Press, 2003) and UpToDate; and receives research support from the NIH (P30 AG062677 (PI) and U01-AG006786 (PI), R01-AG011378 (Co-I), U24 AG057437 (Co-PI), UF1 NS125417 (C0-PI) and U01–024904 (Co-I)). • Dr. Jack serves on an independent data monitoring board for Roche, has served as a speaker for Eisai, and consulted for Biogen, but he receives no personal compensation from any commercial entity. He receives research support from NIH and the Alexander Family Alzheimer's Disease Research Professorship of the Mayo Clinic., (Copyright © 2023. Published by Elsevier Inc.)

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&#95;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%., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)