Assessment of the in vivo dynamics of knee joint tissue deformations – A technological basis to identify and understand structural and compositional changes in early onset osteoarthritis after injury

骨关节炎是一种复杂的退行性疾病，影响关节的所有组织。导致骨关节炎的病理生理过程通常涉及关节内关节结构之间的相互作用，所有这些都是由改变的动态机械负荷(例如，超负荷)作用的--但驱动疾病发展的机械机制尚不清楚。在本项目中，我们将使用前十字韧带损伤模型作为一个示范系统来研究早发性膝骨性关节炎。我们将开发一套全面的技术，首次表征单个组织损伤(以及由此改变的机械负荷)如何影响整个关节的组织负荷动态，从而导致一连串的退行性组织变化。我们将结合我们在动态生物力学负荷评估和静态和动态磁共振成像(MRI)方面的专业知识，详细描述整个膝关节组织的形态、成分和动态变形的早期变化，作为对病理性机械负荷反应的疾病开始的指标。为了实现项目的目标，我们将使用多步骤方法。首先，我们将开发一套全面的磁共振成像技术，用于多对比度采集、组织分割以及整个关节组织的形态/形态和成分分析。我们将把这些技术扩展到动态核磁共振，以利用先前开发的MRI安全设备来表征运动/加载过程中的活体组织变形，该设备允许引导、被动和主动、卸载和加载膝关节运动。其次，我们将获得前十字韧带损伤组和年龄和性别匹配的健康对照组的MRI和生物力学数据。核磁共振数据将使用上述开发的方案获得；生物力学评估将包括运动分析、肌电和多模式测力，以量化膝关节功能和机械负荷，从而能够全面表征生理或病理关节生物力学。第三，我们将应用机器学习技术从综合生物力学和核磁共振数据集中提取可区分不同组的重要特征，然后使用经典统计测试和预测模型将这些膝关节病理性负荷和退变组织变化的特征联系起来，以获得对被认为支撑关节退变的机械诱导组织变化的作用的基本了解。

Osteoarthritis is a complex degenerative disease that affects all tissues of a joint. The pathophysiological processes that lead to osteoarthritis generally involve interplay between articulating structures within a joint, all of which are acted upon by altered dynamic mechanical loading (e.g., overloading) – but the mechanical mechanisms driving disease development remain unclear. In this project, we will use the anterior cruciate ligament injury model as an exemplary system to study early onset knee osteoarthritis. We will develop a comprehensive package of technologies required to characterize – for the first time – how single tissue injuries (and thus altered mechanical loading) influence the dynamics of tissue loading of an entire joint, leading to a cascade of degenerative tissue changes. We will combine our expertise in the assessment of dynamic biomechanical loading and static and dynamic magnetic resonance imaging (MRI) to enable a detailed characterization of early changes in morphometry, composition and dynamic deformation in tissues of the whole knee joint, as indicators of disease onset in response to pathological mechanical loading. To accomplish the aims of the project, we will use a multistep approach. First, we will develop a comprehensive package of MRI techniques for multi-contrast acquisition; tissue segmentation; as well as morphometrical/morphological and compositional analysis of tissues of the whole joint. We will extend these techniques for use with dynamic MRI to characterize in vivo tissue deformations during motion/loading using a previously developed MRI-safe device that allows for guided, passive and active, unloaded and loaded knee motion. Second, we will acquire MRI and biomechanics data for an anterior cruciate ligament injury group and an age- and sex-matched healthy control group. MRI data will be acquired using the aforementioned developed protocol; biomechanical assessments will include motion analysis, electromyography and multimodal dynamometry to quantify knee joint function and mechanical loading, enabling a comprehensive characterization of physiological or pathological joint biomechanics. Third, we will apply machine learning techniques to extract important features from the comprehensive biomechanics and MRI datasets that can differentiate between groups, and then relate these features of pathological knee loading and degenerative tissue changes using classical statistical tests and predictive modelling to gain a fundamental understanding of the role of mechanically-induced tissue changes that are thought to underpin joint degeneration.