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

骨关节炎是一种影响关节所有组织的复杂退行性疾病。导致骨关节炎的病理生理过程通常涉及关节内的关节连接结构之间的相互作用，所有这些都受到改变的动态机械载荷（例如，超载）-但驱动疾病发展的机械机制仍不清楚。在这个项目中，我们将使用前交叉韧带损伤模型作为一个示范系统来研究早发性膝关节骨关节炎。我们将开发一套全面的技术，首次描述单个组织损伤（从而改变机械负荷）如何影响整个关节的组织负荷动力学，导致一连串的退行性组织变化。我们将联合收割机结合我们在动态生物力学载荷评估以及静态和动态磁共振成像（MRI）方面的专业知识，以详细描述整个膝关节组织中形态测量学、组成和动态变形的早期变化，作为病理性机械载荷引起疾病发作的指标。为了实现这个项目的目标，我们将使用一个多步骤的方法。首先，我们将开发一套全面的MRI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学/形态学和成分分析。我们将扩展这些技术与动态MRI一起使用，以表征使用先前开发的MRI安全器械（允许引导、被动和主动、无载和有载膝关节运动）运动/加载期间的体内组织变形。其次，我们将获得前交叉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。将使用上述开发的方案采集MRI数据;生物力学评估将包括运动分析、肌电图和多模式测力法，以量化膝关节功能和机械负荷，从而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的生物力学和MRI数据集中提取重要特征，这些特征可以区分不同的组，然后使用经典的统计检验和预测建模将这些特征与病理性膝关节负荷和退行性组织变化联系起来，以从根本上了解机械诱导的组织变化的作用，这些组织变化被认为是关节退行性变的基础。

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.