CT--Compatible Hexapod Robot for Multi--Directional Mechanical Testing of Bone by Digital Volume Correlation

在30年内，四分之一的加拿大人预计将患有骨关节炎，但我们仍然对骨关节炎以及其他退行性骨疾病背景下骨的基本机械特性知之甚少。典型的骨关节炎，随着软骨退化，关节经历适应性变化。然而，治疗受到阻碍，因为正常和骨关节炎患者之间的差异知之甚少，我们目前无法建模特定受试者的骨质量，使我们无法预测骨将如何在合成组件，如关节植入物的存在下重塑。此外，我们目前无法考虑受试者特定的骨质量来选择最佳的植入物类型或材料，以最大限度地减少骨中的机械应力并促进重塑。高内部骨应力可能导致小骨折，进而导致组件松动和最终失败，这阻碍了我们了解影响退行性骨病患者的过程以及如何最好地解决这些问题的能力。 我们对骨关节炎过程及其治疗的基本知识可以通过了解人体关节的基本力学和负荷转移来大大提高。主要申请人持有NSERC发现资助，通过实验性地向骨样本施加力，同时在高分辨率微型CT扫描仪中观察其内部结构，来开发和验证骨预测模型。目前，这项工作正在进行一个定制的多轴斯图尔特平台式机器人，它有一个负载限制，只能进行非破坏性的骨骼测试。此外，目前的机器人没有闭环负载控制，这限制了可能的调查。这项研究工具和仪器拨款将允许购买一种新的六足装载机器人，该机器人具有更高的破坏性测试负载范围，并具有闭环多轴负载控制，这将使研究团队能够使用生理上准确的负载矢量进行调查。人体骨的力学测试对于提高我们对骨关节炎过程相对于正常骨的自然机制的理解至关重要。这项工作也将使我们能够改进我们的模型，在需要关节置换的患者中受损的骨质量，并预测骨折的风险。一种更好的方法来测量骨骼内部如何传递力，将提高我们对这些自然过程的理解，并使我们能够验证旨在预测骨骼重塑的计算机模型。最终，这项工作将提供新的方法来评估新开发的生物材料和植入式器械的机械性能，包括用于治疗骨关节炎和其他退行性疾病过程的关节植入物。

Within 30 years, 1 in 4 Canadians is expected to have osteoarthritis, yet we still understand little about the basic mechanical properties of bone in the context of osteoarthritis, as well as other degenerative bone conditions. Typically with osteoarthritis, the joint undergoes adaptive changes as the cartilage degrades. However, treatment is hampered because differences between normal and osteoarthritic patients are poorly understood, and our current inability to model subject-specific bone quality prevents us from being able to predict how bone will remodel in the presence of synthetic components such as a joint implant. Moreover, we are currently not able to consider the subject-specific bone quality to select an optimal implant type or material that minimizes mechanical stresses in the bone and promotes remodeling. High internal bone stresses may lead to small fractures that progress to cause loosening of components and eventual failure, which impedes our ability to understand the processes that affect people who have degenerative bone diseases and how to best address them.  Our basic knowledge of the osteoarthritic process and its treatments can be vastly improved by understanding the underlying mechanics and load transfer of the human joint. The principal applicant holds a NSERC Discovery grant to develop and validate bone predictive models by experimentally applying forces to bone samples while viewing their internal structure in a high resolution micro-CT scanner. Currently, this work is being performed with a customized multi-axis Stewart platform style robot, which has a load limit only capable of performing non-destructive bone tests. Moreover, the current robot has no closed-loop load control, which limits the investigations that are possible. This Research Tools and Instruments grant will allow the acquisition of a new hexapod loading robot with a much higher load range for destructive testing, and with closed-loop multi-axis load control that will allow the research team to perform investigations with physiologically accurate loading vectors. Mechanical testing of human bone is essential to improve our understanding of the natural mechanisms of the osteoarthritis process in relation to normal bone. This work will also permit us to improve our models of compromised bone quality in patients requiring joint replacements, and to predict risk of fracture. A better way to measure how bone internally transfers forces would improve our understanding of these natural processes and allow us to validate computer models that are meant to predict bone remodeling. Ultimately, this work will provide new methods to assess mechanical performance of newly developed biomaterials and implantable devices, including joint implants used in the treatment of osteoarthritis and other degenerative disease processes.