Abstract
We introduce a quantitative and automated method for personalized cranial shape remodeling via fronto-orbital advancement surgery. This paper builds on an objective method for automatic quantification of malformations caused by metopic craniosynostosis in children and presents a framework for personalized interventional planning. First, skull malformations are objectively quantified using a statistical atlas of normal cranial shapes. Then, we propose a method based on poly-rigid image registration that takes into account both the clinical protocol for fronto-orbital advancement and the physical constraints in the skull to plan the creation of the optimal post-surgical shape. Our automated surgical planning technique aims to minimize cranial malformations. The method was used to calculate the optimal shape for 11 infants with age 3.8 ± 3.0 month old presenting metopic craniosynostosis and cranial malformations. The post-surgical cranial shape provided for each patient presented a significant average malformation reduction of 49 % in the frontal cranial bones, and achieved shapes whose malformations were within healthy ranges. To our knowledge, this is the first work that presents an automatic framework for an objective and personalized surgical planning for craniosynostosis treatment.
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This work was partly funded by the National Institutes of Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development under grant NIH 1R41HD081712.
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Porras, A.R., Zukic, D., Equobahrie, A., Rogers, G.F., Linguraru, M.G. (2016). Personalized Optimal Planning for the Surgical Correction of Metopic Craniosynostosis. In: Shekhar, R., et al. Clinical Image-Based Procedures. Translational Research in Medical Imaging. CLIP 2016. Lecture Notes in Computer Science(), vol 9958. Springer, Cham. https://doi.org/10.1007/978-3-319-46472-5_8
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DOI: https://doi.org/10.1007/978-3-319-46472-5_8
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