Towards a radiation free numerical modelling framework to predict spring assisted correction of scaphocephaly

Sagittal Craniosynostosis (SC) is a congenital craniofacial malformation, involving premature sagittal suture ossification; spring-assisted cranioplasty (SAC) – insertion of metallic distractors for skull reshaping – is an established method for treating SC. Surgical outcomes are predictable using numerical modelling, however published methods rely on computed tomography (CT) scans availability, which are not routinely performed. We investigated a simplified method, based on radiation-free 3D stereophotogrammetry scans.Eight SAC patients (age 5.1 ± 0.4 months) with preoperative CT and 3D stereophotogrammetry scans were included. Information on osteotomies, spring model and post-operative spring opening were recorded. For each patient, two preoperative models (PREOP) were created: i) CT model and ii) S model, created by processing patient specific 3D surface scans using population averaged skin and skull thickness and suture locations. Each model was imported into ANSYS Mechanical (Analysis System Inc., Canonsburg, PA) to simulate spring expansion. Spring expansion and cranial index (CI - skull width over length) at times equivalent to immediate postop (POSTOP) and follow up (FU) were extracted and compared with in-vivo measurements.Overall expansion patterns were very similar for the 2 models at both POSTOP and FU. Both models had comparable outcomes when predicting spring expansion. Spring induced CI increase was similar, with a difference of 1.2%±0.8% for POSTOP and 1.6%±0.6% for FU.This work shows that a simplified model created from the head surface shape yields acceptable results in terms of spring expansion prediction. Further modelling refinements will allow the use of this predictive tool during preoperative planning

What can a morphometric study of unoperated children teach us about the natural history of metopic synostosis?

Outcomes of surgical repair of trigonocephaly are well reported in the literature, but there is a paucity of information on the natural history of unoperated children. The authors evaluated a group of unoperated children with metopic synostosis to describe the natural change in head shape over time. A database was screened for scans of children with unoperated trigonocephaly (2010-2021). Multisuture cases and those with a metopic ridge were excluded. Three-dimensional surface scans (3D stereophotogrammetry/CT) were used for morphological analysis. Nine previously published parameters were used: frontal angle (FA30°), anteroposterior (AP) volume ratio (APVR), AP area ratio (APAR), AP width ratios 1 and 2 (APWR1 and APWR2), and 4 AP diagonal ratios (30° right APDR [rAPDR30], 30° left APDR [lAPDR30], 60° right APDR [rAPDR60], and 60° left APDR [lAPDR60]). Ninety-seven scans were identified from a cohort of 316 patients with a single metopic suture, in which the male-to-female ratio was 2.7:1. Ages at the time of the scan ranged from 9 days to 11 years and were stratified into 4 groups: group 1, 3 years. Significant improvements were detected in 5 parameters (APVR, APAR, APWR1, rAPDR30, and lAPDR30) over time, whereas no significant differences were found in FA30, APWR2, rAPDR60, and lAPDR60 between age groups. Forehead shape (surface area and volume), as well as narrowing and anterolateral contour at the frontal points, differed significantly over time without surgery. However, forehead angulation, narrowing, and anterolateral contour at temporal points did not show significant differences. This knowledge will aid in surgical and parental decision-making

Correlation of Intracranial Volume With Head Surface Volume in Patients With Multisutural Craniosynostosis

Intracranial volume (ICV) is an important parameter for monitoring patients with multisutural craniosynostosis. Intracranial volume measurements are routinely derived from computed tomography (CT) head scans, which involves ionizing radiation. Estimation of ICV from head surface volumes could prove useful as 3D surface scanners could be used to indirectly acquire ICV information, using a non-invasive, non-ionizing method.Pre- and postoperative 3D CT scans from spring-assisted posterior vault expansion (sPVE) patients operated between 2008 and 2018 in a single center were collected. Patients were treated for multisutural craniosynostosis, both syndromic and non-syndromic. For each patient, ICV was calculated from the CT scans as carried out in clinical practice. Additionally, the 3D soft tissue surface volume (STV) was extracted by 3D reconstruction of the CT image soft tissue of each case, further elaborated by computer-aided design (CAD) software. Correlations were analyzed before surgery, after surgery, combined for all patients and in syndrome subgroups.Soft tissue surface volume was highly correlated to ICV for all analyses: r = 0.946 preoperatively, r = 0.959 postoperatively, and r = 0.960 all cases combined. Subgroup analyses for Apert, Crouzon-Pfeiffer and complex craniosynostosis were highly significant as well (P < 0.001).In conclusion, 3D surface model volumes correlated strongly to ICV, measured from the same scan, and linear equations for this correlation are provided. Estimation of ICV with just a 3D surface model could thus be realized using a simple method, which does not require radiations and therefore would allow closer monitoring in patients through multiple acquisitions over time

Latent Disentanglement in Mesh Variational Autoencoders Improves the Diagnosis of Craniofacial Syndromes and Aids Surgical Planning

The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise. However, there have traditionally been a number of barriers to accurate modelling, especially when operating on both a global and local level. In this work, we will discuss the application of the Swap Disentangled Variational Autoencoder (SD-VAE) with relevance to Crouzon, Apert and Muenke syndromes. Although syndrome classification is performed on the entire mesh, it is also possible, for the first time, to analyse the influence of each region of the head on the syndromic phenotype. By manipulating specific parameters of the generative model, and producing procedure-specific new shapes, it is also possible to simulate the outcome of a range of craniofacial surgical procedures. This opens new avenues to advance diagnosis, aids surgical planning and allows for the objective evaluation of surgical outcomes

A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling

Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face

Quantifying the effect of corrective surgery for trigonocephaly: A non-invasive, non-ionizing method using three-dimensional handheld scanning and statistical shape modelling

Trigonocephaly in patients with metopic synostosis is corrected by fronto-orbital remodelling (FOR). The aim of this study was to quantitatively assess aesthetic outcomes of FOR by capturing 3D forehead scans of metopic patients pre- and post-operatively and comparing them with controls. Ten single-suture metopic patients undergoing FOR and 15 age-matched non-craniosynostotic controls were recruited at Great Ormond Street Hospital for Children (UK). Scans were acquired with a three-dimensional (3D) handheld camera and post-processed combining 3D imaging software. 3D scans were first used for cephalometric measurements. Statistical shape modelling was then used to compute the 3D mean head shapes of the three groups (FOR pre-op, post-op and controls). Head shape variations were described via principal component analysis (PCA). Cephalometric measurements showed that FOR significantly increased the forehead volume and improved trigonocephaly. This improvement was supported visually by pre- and post-operative computed mean 3D shapes and numerically by PCA (p < 0.001). Compared with controls, post-operative scans showed flatter foreheads (p < 0.001). In conclusion, 3D scanning followed by 3D statistical shape modelling enabled the 3D comparison of forehead shapes of metopic patients and non-craniosynostotic controls, and demonstrated that the adopted FOR technique was successful in correcting bitemporal narrowing but overcorrected the rounding of the forehead

Lack of association of cranial lacunae with intracranial hypertension in children with Crouzon syndrome and Apert syndrome: a 3D morphometric quantitative analysis

Purpose Cranial lacunae (foci of attenuated calvarial bone) are CT equivalents ofBcopper beating seen on plain skull radio-graphs in children with craniosynostosis. The qualitative presence of copper beating has not been found to be useful for the diagnosis of intracranial hypertension (IH) in these patients. 3D morphometric analysis (3DMA) allows a more systematic and quantitative assessment of calvarial attenuation. We used 3DMA to examine the relationship between cranial lacunae and IH in children with Crouzon and Apert syndromic craniosynostosis

A population-specific material model for sagittal craniosynostosis to predict surgical shape outcomes

Sagittal craniosynostosis consists of premature fusion (ossification) of the sagittal suture during infancy, resulting in head deformity and brain growth restriction. Spring-assisted cranioplasty (SAC) entails skull incisions to free the fused suture and insertion of two springs (metallic distractors) to promote cranial reshaping. Although safe and effective, SAC outcomes remain uncertain. We aimed hereby to obtain and validate a skull material model for SAC outcome prediction. Computed tomography data relative to 18 patients were processed to simulate surgical cuts and spring location. A rescaling model for age matching was created using retrospective data and validated. Design of experiments was used to assess the effect of different material property parameters on the model output. Subsequent material optimization—using retrospective clinical spring measurements—was performed for nine patients. A population-derived material model was obtained and applied to the whole population. Results showed that bone Young’s modulus and relaxation modulus had the largest effect on the model predictions: the use of the population-derived material model had a negligible effect on improving the prediction of on-table opening while significantly improved the prediction of spring kinematics at follow-up. The model was validated using on-table 3D scans for nine patients: the predicted head shape approximated within 2 mm the 3D scan model in 80% of the surface points, in 8 out of 9 patients. The accuracy and reliability of the developed computational model of SAC were increased using population data: this tool is now ready for prospective clinical application