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