Optimizing prediction and understanding of osteoporotic insufficiency fractures using surrogate models, numerical simulation and quantification of local anisotropies by X-ray Vector Radiography

骨质疏松症是最常见的全身性骨骼疾病。其特征在于骨量减少和骨微结构恶化，导致骨折易感性增加。压缩性椎体骨折与死亡率的大幅增加有关。为了预测骨折风险，除了临床风险因素外，目前仅评估骨矿物质密度（BMD（例如，通过世界卫生组织的FRAX）;然而，多项研究表明，通过使用基于骨骼宏观和微观结构的数值模拟，在体外评估生物力学强度以及体内骨折风险方面有显著改善。在该项目中，我们将提高对骨质疏松骨和连续骨折发展的生物力学特性的理解和预测。为了实现这些目标，我们将使用显微CT和暗场成像（X射线向量射线照相术）分析骨小梁和皮质骨的局部各向异性。暗场成像是一种新的模式，补充传统的X射线成像。它将针对骨成像进行优化，并将深入了解小梁网络的各向异性。将根据体外和体内可用的图像质量对材料模型进行优化。我们将模拟骨重建不同的动态载荷条件和破骨细胞，成骨细胞和药物之间的相互作用的替代品。这些结果将被集成在不同的数值模拟。最后，优化的数值模拟将应用于患者数据，以在临床风险因素和生物标志物的背景下改善对个体骨折风险的预测。

Osteoporosis is the most frequent systemic skeletal disorder. It is characterized by a reduction of bone mass and deterioration of bone microarchitecture, resulting in an increased susceptibility to fracture. Consecutive vertebral fractures are associated with a massive increase in mortality. To predict fracture risk, currently only bone mineral density (BMD) is assessed in addition to clinical risk factors (e.g. by the FRAX of the WHO); however multiple studies demonstrated a significant improve in assessing biomechanical strength in vitro as well as fracture risk in vivo by using numerical simulations, based on bone macro- and micro-architecture.In this project, we will improve both understanding and prediction of biomechanical properties of osteoporotic bone and consecutive fracture development. To achieve these goals, we will analyze local anisotropy of trabecular and cortical bone using micro-CT and dark field imaging (X-ray Vector Radiography). Dark field imaging is a new modality, complementary to conventional X-ray imaging. It will be optimized for bone imaging and will give insight into the anisotropy of the trabecular network. Material models will be optimized regarding the image quality available in vitro and in vivo. We will simulate bone remodeling using surrogates for different dynamic loading conditions and interactions between osteoclasts, osteoblasts and medications. These results will be integrated in different numerical simulations. Finally, the optimized numerical simulations will be applied to patient data, to improve the prediction of individual fracture risk in the context of clinical risk factors and biomarkers.