Quantitative assessment of the skull

Abstract

A technique for performing computational analysis of craniosynostosis from CT images is discussed. An image-computing platform assesses craniofacial malformations by using a shape analysis methodology for image processing. The shape of a patient's craniofacial anatomy is assessed in terms of a statistical shape model that incorporates normal shape variability. The model allows for the identification of a closest normal variant to a patient's anatomy rather than comparing the patient's anatomy to an age-specific average shape. Further, an image registration and segmentation method is discussed which enables obtaining a personalized anatomical model for each patient, thereby achieving a precise and systematic characterization of shape abnormality. Visual (qualitative) and quantitative surgical planning, as well as pre-operative and post-operative assessments are achieved. Further, automatic surgical planning for cranial vault reconstruction using tessellation algorithms to establish the relationship between patient's anatomy and the closest normal variant is discussed.