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Quantitative Assessment Protocol for Facial Soft Tissue Volumetric Changes with Stereophotogrammetry
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Facial recognition from volume-rendered magnetic resonance imaging data.

Fred W Prior1, Barry Brunsden, Charles Hildebolt

  • 1Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA. priorf@mir.wustl.edu

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|January 9, 2009
PubMed
Summary

Facial reconstructions from 3-D brain imaging (CT/MR) can be identifiable. Forty percent of participants successfully matched faces from 3-D MR scans to photographs, raising de-identification concerns.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Biomedical Research

Background:

  • Three-dimensional (3-D) reconstructions from CT and MR brain imaging are standard in clinical practice and research.
  • These reconstructions can inadvertently create recognizable facial images.
  • The HIPAA Privacy Rule mandates de-identification of patient data, including facial images, for research.

Purpose of the Study:

  • To determine if 3-D reconstructed facial images from MR scans are comparable to full-face photographs for identification purposes.
  • To assess the potential for facial recognition from de-identified 3-D brain imaging data.

Main Methods:

  • MR brain imaging datasets were sourced from research repositories.
  • Participants were tasked with matching 3-D MR reconstructions to one of 40 presented photographs.
  • Success rates were compared against a null hypothesis of random chance (1 in 40).

Main Results:

  • Forty percent of subjects successfully matched MR reconstructions with photographs at rates exceeding the null hypothesis.
  • The observed success rate (40%) was statistically significantly higher (P < 0.001) than the chance probability.
  • The 95% confidence interval for the success rate was 29%-52%.

Conclusions:

  • Reconstructed facial images from 3-D MR brain scans pose a potential risk for patient identification.
  • Current de-identification practices may be insufficient to prevent facial recognition from such imaging data.
  • Further research is needed to establish robust de-identification methods for 3-D neuroimaging.