The Science Behind the Springs: Using Biomechanics and Finite Element Modeling to Predict Outcomes in Spring-Assisted Sagittal Synostosis Surgery

Spring-assisted surgery for the correction of scaphocephaly has gained popularity over the past 2 decades. Our unit utilizes standardized torsional springs with a central helix for spring-assisted surgery. This design allows a high degree of accuracy and reproducibility of the force vectors and force distance curves. In this manuscript, we expand on the biomechanical testing and properties of these springs. Standardization of design has enabled us to study the springs on bench and in vivo and a comprehensive repository of calvarial remodeling and spring dynamics has been acquired and analyzed. Finite element modeling is a technique utilized to predict the outcomes of spring-assisted surgery. We have found this to be a useful tool, in planning our surgical strategy and improving outcomes. This technique has also contributed significantly to the process of informed consent preoperatively. In this article, we expand on our spring design and dynamics as well as the finite element modeling used to predict and improve outcomes. In our unit, this practice has led to a significant improvement in patient outcomes and parental satisfaction and we hope to make our techniques available to a wider audience

A 3D morphable model learnt from 10,000 faces

This is the final version of the article. It is the open access version, provided by the Computer Vision Foundation. Except for the watermark, it is identical to the IEEE published version. Available from IEEE via the DOI in this record.We present Large Scale Facial Model (LSFM) - a 3D Morphable Model (3DMM) automatically constructed from 9,663 distinct facial identities. To the best of our knowledge LSFM is the largest-scale Morphable Model ever constructed, containing statistical information from a huge variety of the human population. To build such a large model we introduce a novel fully automated and robust Morphable Model construction pipeline. The dataset that LSFM is trained on includes rich demographic information about each subject, allowing for the construction of not only a global 3DMM but also models tailored for specific age, gender or ethnicity groups. As an application example, we utilise the proposed model to perform age classification from 3D shape alone. Furthermore, we perform a systematic analysis of the constructed 3DMMs that showcases their quality and descriptive power. The presented extensive qualitative and quantitative evaluations reveal that the proposed 3DMM achieves state-of-the-art results, outperforming existing models by a large margin. Finally, for the benefit of the research community, we make publicly available the source code of the proposed automatic 3DMM construction pipeline. In addition, the constructed global 3DMM and a variety of bespoke models tailored by age, gender and ethnicity are available on application to researchers involved in medically oriented research.J. Booth is funded by an EPSRC DTA from Imperial College London, and holds a Qualcomm Innovation Fellowship. A. Roussos is funded by the Great Ormond Street Hospital Childrens Charity (Face Value: W1037). The work of S. Zafeiriou was partially funded by the EPSRC project EP/J017787/1 (4D-FAB)

A 3D Morphable Model learnt from 10,000 faces

We present Large Scale Facial Model (LSFM) — a 3D Morphable Model (3DMM) automatically constructed from 9,663 distinct facial identities. To the best of our knowledge LSFM is the largest-scale Morphable Model ever constructed, containing statistical information from a huge variety of the human population. To build such a large model we introduce a novel fully automated and robust Morphable Model construction pipeline. The dataset that LSFM is trained on includes rich demographic information about each subject, allowing for the construction of not only a global 3DMM but also models tailored for specific age, gender or ethnicity groups. As an application example, we utilise the proposed model to perform age classification from 3D shape alone. Furthermore, we perform a systematic analysis of the constructed 3DMMs that showcases their quality and descriptive power. The presented extensive qualitative and quantitative evaluations reveal that the proposed 3DMM achieves state-of-the-art results, outperforming existing models by a large margin. Finally, for the benefit of the research community, we make publicly available the source code of the proposed automatic 3DMM construction pipeline. In addition, the constructed global 3DMM and a variety of bespoke models tailored by age, gender and ethnicity are available on application to researchers involved in medically oriented research

Computational modelling of patient specific spring assisted lambdoid craniosynostosis correction

Lambdoid craniosynostosis (LC) is a rare non-syndromic craniosynostosis characterised by fusion of the lambdoid sutures at the back of the head. Surgical correction including the spring assisted cranioplasty is the only option to correct the asymmetry at the skull in LC. However, the aesthetic outcome from spring assisted cranioplasty may remain suboptimal. The aim of this study is to develop a parametric finite element (FE) model of the LC skulls that could be used in the future to optimise spring surgery. The skull geometries from three different LC patients who underwent spring correction were reconstructed from the pre-operative computed tomography (CT) in Simpleware ScanIP. Initially, the skull growth between the pre-operative CT imaging and surgical intervention was simulated using MSC Marc. The osteotomies and spring implantation were performed to simulate the skull expansion due to the spring forces and skull growth between surgery and post-operative CT imaging in MSC Marc. Surface deviation between the FE models and post-operative skull models reconstructed from CT images changed between ± 5 mm over the skull geometries. Replicating spring assisted cranioplasty in LC patients allow to tune the parameters for surgical planning, which may help to improve outcomes in LC surgeries in the future

A novel RBF-based predictive tool for facial distraction surgery in growing children with syndromic craniosynostosis

PURPOSE: Predicting changes in face shape from corrective surgery is challenging in growing children with syndromic craniosynostosis. A prediction tool mimicking composite bone and skin movement during facial distraction would be useful for surgical audit and planning. To model surgery, we used a radial basis function (RBF) that is smooth and continuous throughout space whilst corresponding to measured distraction at landmarks. Our aim is to showcase the pipeline for a novel landmark-based, RBF-driven simulation for facial distraction surgery in children. METHODS: An individual's dataset comprised of manually placed skin and bone landmarks on operated and unoperated regions. Surgical warps were produced for 'older' monobloc, 'older' bipartition and 'younger' bipartition groups by applying a weighted least-squares RBF fitted to the average landmarks and change vectors. A 'normalisation' warp, from fitting an RBF to craniometric landmark differences from the average, was applied to each dataset before the surgical warp. The normalisation was finally reversed to obtain the individual prediction. Predictions were compared to actual post-operative outcomes. RESULTS: The averaged change vectors for all groups showed skin and bone movements characteristic of the operations. Normalisation for shape-size removed individual asymmetry, size and proportion differences but retained typical pre-operative shape features. The surgical warps removed the average syndromic features. Reversing the normalisation reintroduced the individual's variation into the prediction. The mid-facial regions were well predicted for all groups. Forehead and brow regions were less well predicted. CONCLUSIONS: Our novel, landmark-based, weighted RBF can predict the outcome for facial distraction in younger and older children with a variety of head and face shapes. It can replicate the surgical reality of composite bone and skin movement jointly in one model. The potential applications include audit of existing patient outcomes, and predicting outcome for new patients to aid surgical planning