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

在30年内，预计每4个加拿大人中就有一个会患上骨关节炎，但我们仍然对骨关节炎以及其他退行性骨疾病中的骨的基本力学性质知之甚少。典型的骨性关节炎，随着软骨的退化，关节会经历适应性变化。然而，治疗受到阻碍，因为人们对正常患者和骨关节炎患者之间的差异了解得很少，而且我们目前无法对特定对象的骨质量进行建模，使我们无法预测在存在合成组件(如关节植入物)的情况下骨骼将如何重塑。此外，我们目前还不能考虑受试者特定的骨质量来选择最优的种植体类型或材料，以最小化骨骼中的机械应力并促进重塑。高的内部骨骼应力可能会导致微小的骨折，进而导致部件松动和最终失败，这阻碍了我们了解影响退行性骨骼疾病患者的过程以及如何最好地解决这些过程的能力。通过了解人体关节的潜在机制和载荷转移，我们对骨关节炎过程及其治疗的基本知识可以得到极大的提高。主要申请者持有NSERC发现基金，用于开发和验证骨骼预测模型，方法是在高分辨率微型CT扫描仪中对骨骼样本进行试验性施力，同时查看其内部结构。目前，这项工作正在使用定制的多轴Stewart平台式机器人进行，该机器人的负载限制只能执行非破坏性骨骼测试。此外，目前的机器人没有闭环负载控制，这限制了可能的研究。这项研究工具和仪器拨款将允许购买一种新的六足加载机器人，具有更高的负载范围用于破坏性测试，并具有闭环多轴负载控制，使研究团队能够使用生理上准确的加载矢量进行调查。人体骨骼的力学测试对于提高我们对骨关节炎过程与正常骨相关的自然机制的理解是必不可少的。这项工作还将使我们能够改进我们对需要关节置换的患者的骨骼质量受损的模型，并预测骨折的风险。一种更好地测量骨骼内部转移力的方法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，这项工作将为评估新开发的生物材料和可植入设备的机械性能提供新的方法，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.