Statistical shape modelling to aid surgical planning: associations between surgical parameters and head shapes following spring-assisted cranioplasty

Abstract

PurposeSpring-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.MethodsTwenty 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.ResultsBivariate correlations between surgical parameters and corresponding PLS shape vectors demonstrated that anterior–posterior (Pearson’s ) and lateral craniotomy dimensions (Spearman’s ), as well as the position of the anterior spring () and the distance between both springs () on average had significant effects on head shapes at the time of spring removal. Such effects were visualised on 3D models.ConclusionsPopulation-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.

Statistical shape modelling for the analysis of head shape variations

“…In the past, SSM has been used to assess the shape of the aortic arch in relation to its function, to relate surgical parameters to outcome in head shape, for surgical planning by analyzing individual head shapes of craniosynostosis patients, for quantifying the effect of corrective surgery for trigonocephaly by illustrating the average effects of surgery and to plan midface defect reconstruction (Mendoza et al, 2014;Bruse et al, 2016;Rodriguez-Florez et al, 2017a;Rodriguez-Florez et al, 2017b;Fuessinger et al, 2019) . By translating such methodology to the description of the pediatric calvarium, we were able to show the benefits of SSM, i.e.…”

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

“…All volumes were extracted from the same CT scans and processing of the soft tissue 3D-meshes occurred in a consistent and reproducible way, using a cutting plane previously described in the literature. 17,18 The software used for the STV calculation -Autodesk Meshmixer -is freely available for download. Using the reported equations, future studies using 3D-photogrammetry could calculate an estimation of ICV by processing the 3D model as described and entering the STV into the equation.…”

“…18 Rodriguez-Florez also used the same cutting plane for calculating the head volume under a soft tissue 3D model. 17 The authors calculated the volumes using the vascular modelling toolkit (VMTK, Orobix, Bergamo, Italy) in combination with MATLAB (The MathWorks Inc, Natick, MA), thus requiring extra software. The volumes in our study were easily retrieved in Autodesk Meshmixer, as they can automatically be displayed after processing, and do not require knowledge or purchase of extra software.…”

Machine learning methods to support personalized neuromusculoskeletal modelling

“…In particular, artificial neural networks have proven particularly effective in medical image processing and have been used in a wide range of applications from automatically determine body composition (Hemke et al 2020), cartilage pathologies (Liu et al 2018) and geometries (Nikolopoulos et al 2020), bone geometries (Ambellan et al 2019), and muscle volumes (Yeung et al 2019) and geometries (Ni et al 2019). Using a dataset of reconstructed anatomical structures or organs exists, statistical shape models have been used to extract features from anatomical data, using principal component analysis (Rodriguez-Florez et al 2017;Varzi et al 2015;Williams et al 2010) to create representations of anatomical tissue with associated principal components for bone (Grant et al 2020;Suwarganda et al 2019;Zhang and Besier 2017;Zhang et al 2014), cartilage (albeit indirectly) (Van Dijck et al 2018), meniscus (Dube et al 2018;Vrancken et al 2014), and other connective tissues (Neubert et al 2015). Once a statistical shape model has been created using a large sample of tissue morphometries (i.e., big data), weighted principal components can be used to reconstruct morphometry of a novel tissue using minimal (i.e., sparse) data.…”