Open-Full-Jaw: An open-access dataset and pipeline for finite element models of human jaw

Developing computational models of the human jaw acquired from cone-beam computed tomography (CBCT) scans is time-consuming and labor-intensive. Besides, a quantitative comparison is not attainable in the literature due to the involved manual tasks and the lack of surface/volumetric meshes. We share an open-access repository of 17 patient-specific finite-element (FE) models of human jaws acquired from CBCT scans and the utilized pipeline for generating them. The proposed pipeline minimizes model generation time and potential errors caused by human interventions. It gets dense surface meshes and provides reduced conformal surface/volumetric meshes suitable for FE analysis. We have quantified the geometrical variations of developed models and assessed models' accuracy from different aspects; (1) the maximum deviations from the input meshes, (2) the mesh quality, and (3) the simulation results. Our results indicate that the developed computational models are precise and have quality meshes suitable for various FE scenarios. Therefore, we believe this dataset will pave the way for future population studies

Contact modeling from images using cut finite element solvers

This paper presents a robust digital pipeline from CT images to the simulation of contact between multiple bodies. The proposed strategy relies on a recently developed immersed finite element algorithm that is capable of simulating unilateral contact between solids without meshing [10]. It was shown that such an approach reduces the difficulties associated with the digital flow of information from analytically defined geometries to mechanical simulations. We now propose to extend our approach to include geometries, which are not defined mathematically but instead are obtained from images, and encoded in 3D arrays of voxels. This paper introduces two novel elements. Firstly, we reformulate our contact algorithm into an extension of an augmented La-grangian CutFEM algorithm. Secondly, we develop an efficient algorithm to convert the surface data generated by standard segmentation tools used in medical imaging into level-set functions. These two elements give rise to a robust digital pipeline with minimum user intervention. We demonstrate the capabilities of our algorithm on a hip joint geometry with contact between the femur and the hip bone