Face recognition from research brain PET: An unexpected PET problem.
Face recognition from research brain PET: An unexpected PET problem.
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DOI：
10.1016/j.neuroimage.2022.119357
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发表时间：
2022-09
期刊：
影响因子：
5.7
通讯作者：
Jack Jr, Clifford R.
中科院分区：
文献类型：
--
作者：
Schwarz, Christopher G.;Kremers, Walter K.;Lowe, Val J.;Savvides, Marios;Gunter, Jeffrey L.;Senjem, Matthew L.;Vemuri, Prashanthi;Kantarci, Kejal;Knopman, David S.;Petersen, Ronald C.;Jack Jr, Clifford R.
众所周知，来自MRI和CT的去识别研究大脑图像可以使用面部识别进行重新识别;然而，这尚未针对PET图像进行检查。我们使用淀粉样蛋白、tau蛋白和FDG PET扫描生成了182名志愿者的面部重建图像，并测量了商业面部识别软件（Microsoft Azure的Face API）自动将其与个体参与者的面部照片进行匹配的准确性。然后，我们将这种准确性与使用参与者的CT和MRI的相同实验进行了比较。PET/CT扫描仪PET图像的面部重建正确匹配率为42%（FDG），35%（tau）和32%（淀粉样蛋白），而CT匹配率为78%，MRI为97- 98%。我们建议，这些识别率足够高，研究应该考虑在数据共享之前，除了CT和结构MRI之外，还应在PET图像上使用面部去识别（“去脸”）软件。我们还更新了我们的mri_reface去识别软件，扩展了功能，以取代PET和CT图像中的面部图像。PET、CT和MRI的面部识别率分别降低到0-4%、5%和8%。我们通过两种不同的测量管道（PETSurfer/FreeSurfer 6.0和一种基于SPM 12和ANT的内部方法）测量了污损对局部淀粉样蛋白PET测量的影响，这些影响很小：污损图像和原始图像之间的ICC值> 0.98，偏差<2%，中位相对误差<2%。对整体淀粉样蛋白PET SUVR测量的影响甚至更小：ICC值为1.00，偏倚<0.5%，中位相对误差也<0.5%。
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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影响因子：
4.8
作者：
Buimer EEL;Schnack HG;Caspi Y;van Haren NEM;Milchenko M;Pas P;Alzheimer's Disease Neuroimaging Initiative;Hulshoff Pol HE;Brouwer RM
通讯作者：
Brouwer RM
DOI：
10.1016/j.jalz.2015.05.001
发表时间：
2015-07
期刊：
Alzheimer's & dementia : the journal of the Alzheimer's Association
影响因子：
--
作者：
Jagust WJ;Landau SM;Koeppe RA;Reiman EM;Chen K;Mathis CA;Price JC;Foster NL;Wang AY
通讯作者：
Wang AY
影响因子：
10.6
作者：
Juefei-Xu, Felix;Luu, Khoa;Savvides, Marios
通讯作者：
Savvides, Marios
DOI：
10.1016/j.jalz.2010.03.003
发表时间：
2010-05
期刊：
Alzheimer's & dementia : the journal of the Alzheimer's Association
影响因子：
--
作者：
Jagust WJ;Bandy D;Chen K;Foster NL;Landau SM;Mathis CA;Price JC;Reiman EM;Skovronsky D;Koeppe RA;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
5.7
作者：
Fischl, Bruce
通讯作者：
Fischl, Bruce