A Statistical Skull Geometry Model for Children 0-3 Years Old

Li, Zhigang; Park, Byoung Keon; Liu, Weiguo; Zhang, Jinhuan; Reed, Matthew P.; Rupp, Jonathan D.; Hoff, Carrie N.; Hu, Jingwen

doi:10.1371/journal.pone.0127322

Cited by 68 publications

(69 citation statements)

References 28 publications

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“…Human skull growth was also been modeled as a mechanical process that is driven by the expansion of the brain and consequently neurocranium using finite‐element (FE) methods (Margulies & Thibault, ; Li et al. ; Libby et al. ).…”

Section: Discussionmentioning

confidence: 99%

“…Human skull growth was also been modeled as a mechanical process that is driven by the expansion of the brain and consequently neurocranium using finite-element (FE) methods (Margulies & Thibault, 2000;Li et al 2015;Libby et al 2017). This approach focuses on material properties of the calvarial bones and sutures, whereas our approach relies on the data that CT exams provide.…”

Section: Statistical Growth Models Vs Finite Element Analysismentioning

confidence: 99%

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Cranial growth in isolated sagittal craniosynostosis compared with normal growth in the first 6 months of age

2019

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Sagittal craniosynostosis (SCS), the most common type of premature perinatal cranial suture fusion, results in abnormal head shape that requires extensive surgery to correct. It is important to find objective and repeatable measures of severity and surgical outcome to examine the effect of timing and technique on different SCS surgeries. The purpose of this study was to develop statistical models of infant (0–6 months old) skull growth in both normative and SCS subjects (prior to surgery). Our goal was to apply these models to the assessment of differences between these two groups in overall post‐natal growth patterns and sutural growth rates as a first step to develop methods for predictive models of surgical outcome. We identified 81 patients with isolated, non‐syndromic SCS from Seattle Children's Craniofacial Center patient database who had a preoperative CT exam before the age of 6 months. As a control group, we identified 117 CT exams without any craniofacial abnormalities or bone fractures in the same age group. We first created population‐level templates from the CT images of the SCS and normal groups. All CT images from both groups, as well as the canonical templates of both cohorts, were annotated with anatomical landmarks, which were used in a growth model that predicted the locations of these landmarks at a given age based on each population. Using the template images and the landmark positions predicted by the growth models, we created 3D meshes for each week of age up to 6 months for both populations. To analyze the growth patterns at the suture sites, we annotated both templates with additional semi‐landmarks equally spaced along the metopic, coronal, sagittal and lambdoidal cranial sutures. By transferring these semi‐landmarks to meshes produced from the growth model, we measured the displacement of the bone borders and suture closure rates. We found that the growth at the metopic and coronal sutures were more rapid in the SCS cohort than in the normal cohort. The antero‐posterior displacement of the semi‐landmarks also indicated a more rapid growth in the sagittal plane in the SCS model than in the normal model. Statistical templates and geometric morphometrics are promising tools for understanding the growth patterns in normal and synostotic populations and to produce objective and reproducible measurements of severity and outcome. Our study is the first of its kind to quantify the bone growth for the first 6 months of life in both normal and sagittal synostosis patients.

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Section: Discussionmentioning

confidence: 99%

Section: Statistical Growth Models Vs Finite Element Analysismentioning

confidence: 99%

Cranial growth in isolated sagittal craniosynostosis compared with normal growth in the first 6 months of age

2019

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“…To overcome this limitation, scaling techniques have been used to obtain the pediatric head injury criteria based on the available adult data [6][7][8][9]. These scaling methods assume that the head structures of children and adults are geometrically similar and cannot account for cranial sutures present in infant skulls [10][11][12]. In particular, infant skull is made up of lamellar layer and the Badult^three-layer skull structure does not start to develop until 3-4 years of age [13].…”

Section: Introductionmentioning

confidence: 99%

Prediction of skull fracture risk for children 0–9 months old through validated parametric finite element model and cadaver test reconstruction

Liu²,

Zhang

et al. 2015

Int J Legal Med

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Skull fracture is one of the most common pediatric traumas. However, injury assessment tools for predicting pediatric skull fracture risk is not well established mainly due to the lack of cadaver tests. Weber conducted 50 pediatric cadaver drop tests for forensic research on child abuse in the mid-1980s (Experimental studies of skull fractures in infants, Z Rechtsmed. 92: 87-94, 1984; Biomechanical fragility of the infant skull, Z Rechtsmed. 94: 93-101, 1985). To our knowledge, these studies contained the largest sample size among pediatric cadaver tests in the literature. However, the lack of injury measurements limited their direct application in investigating pediatric skull fracture risks. In this study, 50 pediatric cadaver tests from Weber's studies were reconstructed using a parametric pediatric head finite element (FE) model which were morphed into subjects with ages, head sizes/shapes, and skull thickness values that reported in the tests. The skull fracture risk curves for infants from 0 to 9 months old were developed based on the model-predicted head injury measures through logistic regression analysis. It was found that the model-predicted stress responses in the skull (maximal von Mises stress, maximal shear stress, and maximal first principal stress) were better predictors than global kinematic-based injury measures (peak head acceleration and head injury criterion (HIC)) in predicting pediatric skull fracture. This study demonstrated the feasibility of using age- and size/shape-appropriate head FE models to predict pediatric head injuries. Such models can account for the morphological variations among the subjects, which cannot be considered by a single FE human model.

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“…The data utilized is collected from studies by Loder [23], Kriewall [24], Li [25], Garfin [26], Wong [27], Desouza [28], Moreira [29], Sullivan [30], Voie [31] and Lillie [32] on adult and pediatric parietal bone structure and thickness as shown in Figure 8(a) In order to include data regarding gestational period skull thicknesses, the point of birth was considered to be 9 months after conception, with a post conception age of 36 weeks corresponding to the 0 point on the X-axis. Furthermore, whenever the thickness was given in tandem with an age range and sample size, an average age between the extremes was assumed, with the number of identical points corresponding to the given sample size.…”

Section: Figure 7: (A) Schematic Illustrating the Location Of Parietamentioning

confidence: 99%

Biomechanical Root Cause Analysis of Complications in Head Immobilization Devices for Pediatric Neurosurgery

Abdulhafez

Kadry

Zaazoue

et al. 2018

Volume 1: Additive Manufacturing; Bio and Sustainable Manufacturing

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Precise and firm fixation of the cranium is critical during craniotomy and delicate brain neurosurgery making head immobilization devices (HIDs) a staple instrument in brain neurosurgical operations today. However, despite their popularity, there is no standard procedure for their use and many complications arise from using HIDs in pediatric neurosurgery. In this paper, we identify biomechanical causes of complications and quantify risks in pin-type HIDs including clamping force selection, positioning and age effects. Based on our root cause analysis, we develop a framework to address the biomechanical factors that influence complications and understand the biomechanics of the clamping process. We develop an age-dependent finite element model (FEM) of a single pin on a cranial bone disc with the representative properties and skull thickness depending on age. This model can be utilized to reduce risk of complications by design as well as to provide recommendations for current practices.

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A Statistical Skull Geometry Model for Children 0-3 Years Old

Cited by 68 publications

References 28 publications

Cranial growth in isolated sagittal craniosynostosis compared with normal growth in the first 6 months of age

Cranial growth in isolated sagittal craniosynostosis compared with normal growth in the first 6 months of age

Prediction of skull fracture risk for children 0–9 months old through validated parametric finite element model and cadaver test reconstruction

Biomechanical Root Cause Analysis of Complications in Head Immobilization Devices for Pediatric Neurosurgery

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