Project summary
Children with craniosynostosis typically undergo surgical treatment to remove the brain growth constraints and
correct for the malformations produced a volume overgrowth parallel to the fused sutures that compensates for
the local growth restrictions. However, there is a large variability of surgical techniques and outcomes among
institutions, and it is common for patients with suboptimal treatments to require additional invasive surgeries for
three main reasons: (1) local volume anomalies and their progression have not been characterized to estimate
how much local volume patients need during treatment; (2) our knowledge about how treatment modifies
cranial growth is limited and post-surgical growth predictions are not possible; and (3) there are no objective
and personalized methods to evaluate long-term outcomes, so treatment selection remains subjective.
Although our team and others have created methods to quantify cranial and head shape anomalies using
imaging data, existing methods cannot characterize the abnormal patterns of local volume development in
craniosynostosis, are age-agnostic, and do not account for sex, which is an essential modulator of
development. Moreover, our recently created data-driven normative cranial bone development model cannot
be used to predict growth of a surgically modified cranium, and large post-surgical datasets have not been
traditionally available to characterize volume development after treatment of patients with craniosynostosis.
This limited knowledge about the abnormal pre- and post-surgical local volume development has hindered the
translation of existing methods to plan, quantify, compare and predict surgical outcomes. Hence, treatment
selection remains subjective and the identification of relapsing patients still relies on subjective interpretation of
variable clinical symptoms with low predictive value.
We demonstrated that CT and 3D photogrammetry provide the same quantification of head anatomy, and
Children’s Hospital Colorado incorporated pre- and post-surgical 3D photogrammetry acquisition in the
standard clinical protocol of patients with craniosynostosis. In this project, we will leverage our retrospective
dataset to characterize local head volume development in patients with craniosynostosis before surgery, study
how treatment modifies growth patterns and identify relapsing patients using objective data. The main goals of
this project are: (1) to quantitatively characterize the local head volume distributions and their temporal
progression in patients with craniosynostosis; and (2) to predict and evaluate local head volume growth after
treatment of craniosynostosis. This project will create the necessary objective and quantitative knowledge to
achieve personalized optimal treatments, and software tools to objectively evaluate patients using non-invasive
3D photogrammetry before and after surgical treatment.
