Subject-specific finite element (FE) models could improve decision making in canine long bone fracture repair. However, it preliminary requires that FE models predicting the mechanical response of canine long bone are proposed and validated. We present here a combined experimental-numerical approach to test the ability of subjectspecific FE models to predict the bending response of seven pairs of canine humeri directly from medical images. Our results show that bending stiffness and yield load are predicted with a mean absolute error of 10.1% (±5.2%) for the fourteen samples.This study constitutes a basis for the forthcoming optimization of canine long bone fracture repair.

Thanks to advances in medical imaging technologies and numerical methods, patient-specific modelling is more and more used to improve diagnosis and to estimate the outcome of surgical interventions. It requires the extraction of the domain of interest from the medical scans of the patient, as well as the discretisation of this geometry. However, extracting smooth multi-material meshes that conform to the tissue boundaries described in the segmented image is still an active field of research. We propose to solve this issue by combining an implicit surface reconstruction method with a multi-region mesh extraction scheme. The surface reconstruction algorithm is based on multi-level partition of unity implicit surfaces, which we extended to the multi-material case. The mesh generation algorithm consists in a novel multi-domain version of the marching tetrahedra. It generates multi-region meshes as a set of triangular surface patches consistently joining each other at material junctions. This paper presents this original meshing strategy, starting from boundary points extraction from the segmented data to heterogeneous implicit surface definition, multi-region surface triangulation and mesh adaptation. Results indicate that the proposed approach produces smooth and high-quality triangular meshes with a reasonable geometric accuracy. Hence, the proposed method is well suited for subsequent volume mesh generation and finite element simulations.

Distal humeral fractures are common fractures especially in immature small breed dogs. The pathogenesis is still unknown. For this study, a three-dimensional bone model of the canine elbow was created and finite element analysis performed in order to determine the relationship between fracture type and bone interactions. Fused and non-fused humeral condyles were considered. A failure criterion was implemented to simulate the pathogenesis until fracture. Our study results confirm the clinical observation that lateral condylar fracture is the most common fracture type, implying interaction with the radius. Medial and Y-fractures are less common and occur always in interaction with the ulna whereas the radius causes lateral condylar fracture. Additionally, the fracture type is sensitive to bone positioning during trauma. The pathogenesis of distal humeral fractures is more complex than generally reported in the literature.

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