Linking tissue mechanics with imaging

医学成像技术，如计算机断层扫描，可以用来估计人类关节中骨和软骨的机械性能(如强度)。这些成像技术可以帮助我们更好地了解骨关节炎的原因，骨关节炎是一种关节软骨的“磨损”疾病，影响着300多万加拿大人。在关节，如膝盖，骨骼末端被一层薄薄的软骨覆盖，这样就可以无痛运动。骨性关节炎的特征是软骨的破坏。目前尚不清楚是什么原因导致骨关节炎，但软骨下(软骨下)骨的力学性能的变化可能会影响软骨的破坏。这项研究的目标是开发和验证可用于评估软骨下骨和软骨的力学性能的成像工具。这将使用计算机断层扫描和(1)测量骨密度的校准系统，以及(2)测量软骨生物化学(软骨健康)的造影剂来完成。我们将使用材料测试和计算机建模的独特组合，评估成像的骨密度和软骨生物化学分别预测骨和软骨的机械性能的好坏。这项研究将支持学生在几个科学研究领域的培训，包括机械和生物医学工程、医学和人体运动学。这项研究将提供能够可靠地估计骨骼和软骨强度的新成像工具，以及可以用来更好地了解关节疾病的计算机模型。从拟议的研究中获得的知识可以用来开发更好的诊断工具，以评估关节健康和疾病，并提高我们对骨关节炎的理解。

Medical imaging techniques such as Computed Tomography may be used to estimate the mechanical properties (such as strength) of bone and cartilage in human joints. These imaging techniques can help us to better understand the cause of osteoarthritis, a 'wear and tear' disease on the cartilage of joints affecting over 3 million Canadians. At joints, such as the knee, bone ends are covered by a thin layer of cartilage which allows pain-free movement. Osteoarthritis is characterized by breakdown of this cartilage. It is not known what causes osteoarthritis but changes in the mechanical properties of (subchondral) bone underlying cartilage may influence cartilage breakdown. The goal of this research is to develop and validate imaging tools that can be used to assess mechanical properties of subchondral bone and cartilage. This will be accomplished using Computed Tomography together with (1) a calibration system to measure bone density, and (2) contrast agents to measure cartilage biochemistry (cartilage health). We will assess how well imaged bone density and cartilage biochemistry predict the mechanical properties of bone and cartilage, respectively, using a unique combination of material-testing and computer modeling. This research will support the training of students in several scientific research areas including mechanical and biomedical engineering, medicine and kinesiology. This research will provide new imaging tools capable of reliably estimating bone and cartilage strength, and computer models that can be used to better understand joint diseases. Knowledge gained from the proposed research can be used to develop better diagnostic tools for assessing joint health and disease, and to improve our understanding of osteoarthritis.