Automated analysis of craniofacial morphology using magnetic resonance images |
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Authors: | Chakravarty M Mallar Aleong Rosanne Leonard Gabriel Perron Michel Pike G Bruce Richer Louis Veillette Suzanne Pausova Zdenka Paus Tomá? |
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Institution: | Rotman Research Institute, Baycrest, Toronto, Ontario, Canada. mchakravarty@rotman-baycrest.on.ca |
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Abstract: | Quantitative analysis of craniofacial morphology is of interest to scholars
working in a wide variety of disciplines, such as anthropology, developmental
biology, and medicine. T1-weighted (anatomical) magnetic resonance images (MRI)
provide excellent contrast between soft tissues. Given its three-dimensional
nature, MRI represents an ideal imaging modality for the analysis of
craniofacial structure in living individuals. Here we describe how T1-weighted
MR images, acquired to examine brain anatomy, can also be used to analyze facial
features. Using a sample of typically developing adolescents from the Saguenay
Youth Study (N?=?597; 292 male, 305 female, ages: 12 to 18
years), we quantified inter-individual variations in craniofacial structure in
two ways. First, we adapted existing nonlinear registration-based morphological
techniques to generate iteratively a group-wise population average of
craniofacial features. The nonlinear transformations were used to map the
craniofacial structure of each individual to the population average. Using
voxel-wise measures of expansion and contraction, we then examined the effects
of sex and age on inter-individual variations in facial features. Second, we
employed a landmark-based approach to quantify variations in face surfaces. This
approach involves: (a) placing 56 landmarks (forehead, nose, lips, jaw-line,
cheekbones, and eyes) on a surface representation of the MRI-based group
average; (b) warping the landmarks to the individual faces using the inverse
nonlinear transformation estimated for each person; and (3) using a principal
components analysis (PCA) of the warped landmarks to identify facial features
(i.e. clusters of landmarks) that vary in our sample in a correlated fashion. As
with the voxel-wise analysis of the deformation fields, we examined the effects
of sex and age on the PCA-derived spatial relationships between facial features.
Both methods demonstrated significant sexual dimorphism in craniofacial
structure in areas such as the chin, mandible, lips, and nose. |
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