Quantitative morphological analysis framework of infant cranial sutures and fontanelles based on CT images.

Characterizing the suture morphological variation is a crucial step to investigate the influence of sutures on infant head biomechanics. This study aimed to establish a comprehensive quantitative framework for accurately capturing the cranial suture and fontanelle morphologies in infants. A total of 69 CT scans of 2-4 month-old infant heads were segmented to identify semilandmarks at the borders of cranial sutures and fontanelles. Morphological characteristics, including length, width, sinuosity index (SI), and surface area, were measured. For this, an automatic method was developed to determine the junction points between sutures and fontanelles, and thin-plate-spline (TPS) was utilized for area calculation. Different dimensionality reduction methods were compared, including nonlinear and linear principal component analysis (PCA), as well as deep-learning-based variational autoencoder (VAE). Finally, the significance of various covariates was analyzed, and regression analysis was performed to establish a statistical model relating morphological parameters with global parameters. This study successfully developed a quantitative morphological framework and demonstrate its application in quantifying morphologies of infant sutures and fontanelles, which were shown to significantly relate to global parameters of cranial size, suture SI, and surface area for infants aged 2-4 months. The developed framework proved to be reliable and applicable in extracting infant suture morphology features from CT scans. The demonstrated application highlighted its potential to provide valuable insights into the morphologies of infant cranial sutures and fontanelles, aiding in the diagnosis of suture-related skull fractures. Infant suture, Infant fontanelle, Morphological variation, Morphology analysis framework, Statistical model.

Epidemiology, Genetics, and Pathophysiology of Craniosynostosis.

Craniosynostosis, the premature fusion of the infant cranial skulls, can be recognized by characteristic head shape differences that worsen with head growth. Craniosynostosis can be syndromic or nonsyndromic and can involve one suture or multiple sutures. Timely cranial vault surgery is recommended to expand and reshape the skull, with a goal of preventing increased intracranial pressure and providing sufficient space for brain growth. Several gene variants and environmental exposures are known to increase the risk of single suture craniosynostosis (SSC), including in utero constraint, exposure to specific toxins and medications, and medical conditions such as thyroid dysregulation and metabolic bone disorders.

Computational Study of Fracture Characteristics in Infant Skulls Using a Simplified Finite Element Model.

Skull fracture characteristics are associated with loading conditions (such as the impact point and impact velocity) and could provide indication of abuse or accident-induced head injuries. However, correlations between fracture characteristics and loading conditions in infant and toddler are ill-understood. A simplified computational model representing an infant head was built to simulate skull responses to blunt impacts. The fractures were decided through a first principal strain-based element elimination strategy. Simulation results were qualitatively compared with test data from porcine heads. This simplified model well captured the fracture pattern, initial fracture position, and direction of fracture propagation. The model also very well described fracture characteristics found in studies with human infant cadaveric specimens. A series of parametric studies was conducted, and results indicated that the parameters studied had substantial effects on fracture patterns. Additionally, the jagged shapes of sutures were associated with strain concentrations in the skull.