Functional adaptation of the infant craniofacial system to mechanical loadings arising from masticatory forces

A physico-mechanical model of postnatal craniofacial growth in human

Our fundamental understanding of the physico-mechanical forces that drive the size and shape changes of the cranium during ontogeny are limited. Biomechanical models based on finite element method present a huge opportunity to address this critical gap in our knowledge. Here, we describe a validated computational framework to predict normal craniofacial growth. Our results demonstrated that this approach is capable of predicting the growth of calvaria, face, and skull base. We highlighted the crucial role of skull base in antero-posterior growth of the face and also demonstrated the contribution of the maxillary expansion to the dorsoventral growth of the face and its interplay with the orbits. These findings highlight the importance of physical interactions of different components of the craniofacial system. The computational framework described here serves as a powerful tool to study fundamental questions in developmental biology and to advance treatment of conditions affecting the craniofacial system such as craniosynostosis.

Functional adaptation of the infant craniofacial system to mechanical loadings arising from masticatory forces

The morphology and biomechanics of infant crania undergo significant changes between the pre- and post-weaning phases due to increasing loading of the masticatory system. The aims of this study were to characterize the changes in muscle forces, bite forces and the pattern of mechanical strain and stress arising from the aforementioned forces across crania in the first 48 months of life using imaging and finite element methods. A total of 51 head computed tomography scans of normal individuals were collected and analysed from a larger database of 217 individuals. The estimated mean muscle forces of temporalis, masseter and medial pterygoid increase from 30.9 to 87.0 N, 25.6 to 69.6 N and 23.1 to 58.9 N, respectively (0-48 months). Maximum bite force increases from 90.5 to 184.2 N (3-48 months). There is a change in the pattern of strain and stress from the calvaria to the face during postnatal development. Overall, this study highlights the changes in the mechanics of the craniofacial system during normal development. It further raises questions as to how and what level of changes in the mechanical forces during the development can alter the morphology of the craniofacial system.

BounTI (boundary-preserving threshold iteration): A user-friendly tool for automatic hard tissue segmentation

X-ray Computed Tomography (CT) images are widely used in various fields of natural, physical, and biological sciences. 3D reconstruction of the images involves segmentation of the structures of interest. Manual segmentation has been widely used in the field of biological sciences for complex structures composed of several sub-parts and can be a time-consuming process. Many tools have been developed to automate the segmentation process, all with various limitations and advantages, however, multipart segmentation remains a largely manual process. The aim of this study was to develop an open-access and user-friendly tool for the automatic segmentation of calcified tissues, specifically focusing on craniofacial bones. Here we describe BounTI, a novel segmentation algorithm which preserves boundaries between separate segments through iterative thresholding. This study outlines the working principles behind this algorithm, investigates the effect of several input parameters on its outcome, and then tests its versatility on CT images of the craniofacial system from different species (e.g. a snake, a lizard, an amphibian, a mouse and a human skull) with various scan qualities. The case studies demonstrate that this algorithm can be effectively used to segment the craniofacial system of a range of species automatically. High-resolution microCT images resulted in more accurate boundary-preserved segmentation, nonetheless significantly lower-quality clinical images could still be segmented using the proposed algorithm. Methods for manual intervention are included in this tool when the scan quality is insufficient to achieve the desired segmentation results. While the focus here was on the craniofacial system, BounTI can be used to automatically segment any hard tissue. The tool presented here is available as an Avizo/Amira add-on, a stand-alone Windows executable, and a Python library. We believe this accessible and user-friendly segmentation tool can benefit the wider anatomical community.