P67. DEVELOPMENT OF A CRANIOFACIAL SOFT TISSUE ANTHROPOMORPHIC DATABASE: APPLICATION OF DIFFEOMORPHIC ALGORITHMS TO MRI

Abstract

PURPOSE: Assessing craniofacial outcomes in pediatric patients is challenging due to rapid craniofacial growth, often anticipated with surgical “overcorrection.” The authors present data developing a racially and ethnically sensitive anthropomorphic database, providing plastic and craniofacial surgeons with “normal” anatomic measurements to optimize aesthetic and reconstructive outcomes. METHODS: Patients with head MRIs without craniofacial indication from 2008-2021 were included. Images were used to construct composite (template) images with diffeomorphic algorithms and Advanced Normalization Tools (ANTs). Algorithms were based on symmetric diffeomorphic image registration with image averaging. Composites were thresholded to generate binary segmentations used for anatomic measurements in Materalise Mimics. RESULTS: High-resolution MRIs from 80 patients generated eight composites with ten MRI sequences each: four seven-year-olds and four eight-year-olds (two male, two female, two black, and two white). Head circumference of seven- and eight-year-old composites was 52.9cm and 53.9cm, respectively. The average lateral canthus to lateral canthus distance was 1.6mm longer in eight-year-old (90.7mm) than seven-year-old (88.1mm) composites. Nasion to nasal tip distance was 4.4mm longer in eight-year-old (31.4mm) than seven-year-old (26.9mm) composites. CONCLUSION: Application of diffeomorphic algorithms via ANTs to MRI is effective in creating composite templates to represent “normal” craniofacial and soft tissue anatomy. Future research will focus on development of mathematical tools to characterize anatomic normality, generation of indices to grade preoperative severity, and quantification of postoperative results to reduce subjectivity bias.