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Related Concept Videos

Prosopagnosia01:24

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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
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Related Experiment Video

Updated: Sep 21, 2025

Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET
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Face recognition from research brain PET: An unexpected PET problem.

Christopher G Schwarz1, Walter K Kremers2, Val J Lowe1

  • 1Department of Radiology, Mayo Clinic, Rochester, MN, USA.

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|June 6, 2022
PubMed
Summary
This summary is machine-generated.

Face recognition can re-identify individuals from PET scans, necessitating de-facing. Applying de-facing software to PET, CT, and MRI scans significantly reduces re-identification risk while minimally impacting image data quality.

Keywords:
AnonymizationDe-facingDe-identificationFace recognitionPET/CT

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Area of Science:

  • Neuroimaging
  • Medical Imaging
  • Computer Vision

Background:

  • Research brain images from MRI and CT are known to be re-identifiable via face recognition.
  • The re-identification risk for Positron Emission Tomography (PET) scans has not been previously investigated.
  • Protecting participant privacy is crucial for responsible data sharing in medical research.

Purpose of the Study:

  • To assess the re-identification risk of PET scans using face recognition technology.
  • To compare the re-identification accuracy of PET scans with CT and MRI scans.
  • To evaluate the effectiveness of de-facing software in mitigating re-identification risks for PET, CT, and MRI data and its impact on quantitative measurements.

Main Methods:

  • Generated face reconstructions from amyloid, tau, and FDG PET scans of 182 volunteers.
  • Utilized Microsoft Azure's Face API to match reconstructed faces with participant photographs.
  • Compared face recognition accuracy across PET, CT, and MRI modalities.
  • Applied updated mri_reface software for de-facing PET, CT, and MRI images.
  • Assessed the impact of de-facing on regional and global amyloid PET measurements using two analysis pipelines.

Main Results:

  • Face reconstructions from PET scans were matched with 42% (FDG), 35% (tau), and 32% (amyloid) accuracy.
  • CT scans showed 78% match accuracy, while MRI scans achieved 97-98% accuracy.
  • De-facing reduced PET re-identification rates to 0-4%, CT to 5%, and MRI to 8%.
  • De-facing minimally affected regional amyloid PET measurements (ICC > 0.98, bias < 2%, relative error < 2%).
  • Global amyloid PET measurements showed negligible effects from de-facing (ICC = 1.00, bias < 0.5%, relative error < 0.5%).

Conclusions:

  • PET scans pose a significant, previously unrecognized risk for re-identification through face recognition.
  • De-facing PET images is recommended alongside CT and MRI before data sharing to protect participant privacy.
  • The mri_reface software effectively reduces re-identification risks across modalities with minimal impact on quantitative imaging data.