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

• Applicants: Nicholas Brisson, Ph.D.; Dr. Martin Krämer
• Subject Area: Orthopaedics, Traumatology, Reconstructive Surgery ; Medical Physics, Biomedical Technology
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 471160681

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.

• DFG Programme: Research Grants