Search

Three-dimensional (3-D) reconstructions of computed tomography (CT) and magnetic resonance (MR) brain imaging studies are a routine component of both clinical practice and clinical and translational research. A side effect of such reconstructions is the creation of a potentially recognizable face. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) Privacy Rule requires that individually identifiable health information may not be used for research unless identifiers that may be associated with the health information including "Full face photographic images and other comparable images…" are removed (de-identification). Thus, a key question is: Are reconstructed facial images comparable to full-face photographs for the purpose of identification? To address this question, MR images were selected from existing research repositories and subjects were asked to pair an MR reconstruction with one of 40 photographs. The chance probability that an observer could match a photograph with its 3-D MR image was 1 in 40 (0.025), and we considered 4 successes out of 40 (4/40, 0.1) to indicate that a subject could identify persons' faces from their 3-D MR images. Forty percent of the subjects were able to successfully match photographs with MR images with success rates higher than the null hypothesis success rate. The Blyth-Still-Casella 95% confidence interval for the 40% success rate was 29%-52%, and the 40% success rate was significantly higher (P <0.001) than our null hypothesis success rate of 1 in 10 (0.10). [ABSTRACT FROM AUTHOR]

Normative values of pediatric skull circumference, cranial index, and braincase volume would inform multiple disciplines including neurosurgery, plastic surgery and anthropology. Semi-automated methods exist for obtaining these data but are time consuming and require expertise. We report on a new method for automated extraction of in vivo measures of pediatric crania based on x-ray computed tomography scans (CT). Data were obtained from a clinical image repository for pediatric populations in whom no pathology was noted. The automated process showed good agreement with semi-automated measures, although there was a small bias for both braincase volume and circumference. We developed an open source program to automatically extract measures of skull circumference, cranial index, and braincase volume that are likely to prove useful in multiple disciplines.

Skull thickness and density measures of normal pediatric crania would inform multiple disciplines including neurosurgery, optical and magnetoelectrophysiological imaging, and biomechanical modeling of head trauma. We report on a new method for automated extraction of in vivo skull thickness and density measures of pediatric crania based on x-ray computed tomography scans (CT). Data were obtained from a clinical image repository for pediatric populations in whom no pathology was noted. Skull thickness and density measures were systematically obtained across the calvarium. We find a set of measures that correlated with physiological age that are likely to prove useful in multiple disciplines.