Microfocus computed tomography (micro-CT) is an imaging method that provides three dimensional (3D) digital datasets with comparable resolution to light microscopy. Although it has traditionally been used for non-destructive testing in engineering, aerospace industries and in preclinical animal studies, new applications are rapidly becoming available in the clinical setting including post-mortem fetal imaging and pathological specimen analysis. Printing three dimensional models from imaging datasets for educational purposes is well established in the medical literature, but typically using low resolution (0.7 mm voxel size) data acquired from computed tomography (CT) or magnetic resonance (MR) examinations. With higher resolution imaging (voxel sizes below 1 micron, <0.001mm) at micro-CT, smaller structures can be better characterised, and datasets post-processed to create accurate anatomical models for review and handling. In this review, we provide examples of how 3D printing of micro-CT imaged specimen...

Implantation of spring-like distractors in the treatment of sagittal craniosynostosis is a novel technique that has proven functionally and aesthetically effective in correcting skull deformities; however, final shape outcomes remain moderately unpredictable due to an incomplete understanding of the skull-distractor interaction. The aim of this study was to create a patient specific computational model of spring assisted cranioplasty (SAC) that can help predict the individual overall final head shape. Pre-operative computed tomography images of a SAC patient were processed to extract a 3D model of the infant skull anatomy and simulate spring implantation. The distractors were modeled based on mechanical experimental data. Viscoelastic bone properties from the literature were tuned using the specific patient procedural information recorded during surgery and from x-ray measurements at follow-up. The model accurately captured spring expansion on-table (within 9% of the measured values), as well as at first and second follow-ups (within 8% of the measured values). Comparison between immediate post-operative 3D head scanning and numerical results for this patient proved that the model could successfully predict the final overall head shape. This preliminary work showed the potential application of computational modeling to study SAC, to support pre-operative planning and guide novel distractor design.

OBJECTIVE: Scaphocephaly secondary to sagittal craniosynostosis has been treated in recent years with spring-assisted cranioplasty, an innovative approach that leverages the use of metallic spring distractors to reshape the patient skull. In this study, a population of patients who had undergone spring cranioplasty for the correction of scaphocephaly at the Great Ormond Street Hospital for Children was retrospectively analyzed to systematically assess spring biomechanical performance and kinematics in relation to spring model, patient age, and outcomes over time.METHODS: Data from 60 patients (49 males, mean age at surgery 5.2 ± 0.9 months) who had received 2 springs for the treatment of isolated sagittal craniosynostosis were analyzed. The opening distance of the springs at the time of insertion and removal was retrieved from the surgical notes and, during the implantation period, from planar radiographs obtained at 1 day postoperatively and at the 3-week follow-up. The force exerted by the spring to the patient skull at each time point was derived after mechanical testing of each spring model—3 devices with the same geometry but different wire thicknesses. Changes in the cephalic index between preoperatively and the 3-week follow-up were recorded.RESULTS: Stiffer springs were implanted in older patients (p < 0.05) to achieve the same opening on-table as in younger patients, but this entailed significantly different—higher—forces exerted on the skull when combinations of stiffer springs were used (p < 0.001). After initial force differences between spring models, however, the devices all plateaued. Indeed, regardless of patient age or spring model, after 10 days from insertion, all the devices were open.CONCLUSIONS: Results in this study provide biomechanical insights into spring-assisted cranioplasty and could help to improve spring design and follow-up strategy in the future.

Purpose Spring-assisted cranioplasty is performed to correct the long and narrow head shape of children with sagittal synostosis. Such corrective surgery involves osteotomies and the placement of spring-like distractors, which gradually expand to widen the skull until removal about 4 months later. Due to its dynamic nature, associations between surgical parameters and post-operative 3D head shape features are difficult to comprehend. The current study aimed at applying population-based statistical shape modelling to gain insight into how the choice of surgical parameters such as craniotomy size and spring positioning affects post-surgical head shape. Methods Twenty consecutive patients with sagittal synostosis who underwent spring-assisted cranioplasty at Great Ormond Street Hospital for Children (London, UK) were prospectively recruited. Using a nonparametric statistical modelling technique based on mathematical currents, a 3D head shape template was computed from surface head scans of sagittal patients after spring removal. Partial least squares (PLS) regression was employed to quantify and visualise trends of localised head shape changes associated with the surgical parameters recorded during spring insertion: anterior–posterior and lateral craniotomy dimensions, anterior spring position and distance between anterior and posterior springs. Results Bivariate correlations between surgical parameters and corresponding PLS shape vectors demonstrated that anterior–posterior (Pearson’s \(r=0.64, p=0.002\)) and lateral craniotomy dimensions (Spearman’s \(\rho =0.67, p<0.001\)), as well as the position of the anterior spring (\(r=0.70, p<0.001\)) and the distance between both springs (\(r=0.67, p=0.002\)) on average had significant effects on head shapes at the time of spring removal. Such effects were visualised on 3D models. Conclusions Population-based analysis of 3D post-operative medical images via computational statistical modelling tools allowed for detection of novel associations between surgical parameters and head shape features achieved following spring-assisted cranioplasty. The techniques described here could be extended to other cranio-maxillofacial procedures in order to assess post-operative outcomes and ultimately facilitate surgical decision making.

Three-dimensional virtual simulation of orthognathic surgery is now a well-established method in maxillo-facial surgery. The commercial software packages are still burdened by a consistent imprecision on soft tissue predictions. In this study, the authors produced an anatomically detailed patient specific numerical model for simulation of soft tissue changes in orthognathic surgery. Eight patients were prospectively enrolled. Each patient underwent CBCT and planar x-rays prior to surgery and in addition received an MRI scan. Postoperative soft-tissue change was simulated using Finite Element Modeling (FEM) relying on a patient-specific 3D models generated combining data from preoperative CBCT (hard tissue) scans and MRI scans (muscles and skin). An initial simulation was performed assuming that all the muscles and the other soft tissue had the same material properties (Homogeneous Model). This model was compared with the postoperative CBCT 3D simulation for validation purpose. Design of experiments (DoE) was used to assess the effect of the presence of the muscles considered and of their variation in stiffness. The effect of single muscles was evaluated in specific areas of the midface. The quantitative distance error between the homogeneous model and actual patient surfaces for the midface area was 0.55 mm, standard deviation 2.9 mm. In our experience, including muscles in the numerical simulation of orthognathic surgery, brought an improvement in the quality of the simulation obtained.