Knee osteoarthritis (KOA) is a debilitating disease that affects a significant portion of the population at some point in their lives. The potential causes are multifactored, and ongoing investigations aim to provide insights into the mechanisms of action. Knee instability correlates with KOA, but more research is needed to understand this relationship fully. Stability can be divided into two forms: active stability, which the muscles provide, and passive stability, provided primarily by ligaments. The focus of Brett’s research is exclusively passive stability.

The joint’s laxity quantifies passive stability. The measurement of laxity entails applying a force to the knee and measuring the resulting change in position and orientation. this process is largely manual in current clinical practice, and determining laxity relies on the measurer’s experience. This project proposes a robotics- and ultrasound-based approach to obtain accurate 3D laxity measurements in vivo. A novel robotic system accomplishes the force applied in this setup, whilst a separate PhD project using ultrasound imaging develops the displacement and angular measurements system.

This project intends to use the developed laxity device to quantify knee laxity between different stages of KOA and healthy control. Existing load scenarios will provide insights into these groups and allow comparisons with contemporary laxity devices. However, existing literature on load scenarios is generalised, often emphasising the simplicity of implementation by clinicians and are for substantial ligament loadings (e.g., ACL tear). Because these laxity tests cannot differentiate amongst different ligament loadings, they cannot evaluate smaller structures not visible in medical imaging. Computational modelling combined with optimisation procedures will develop laxity tests, allowing subject-specific loadings that optimally stretch individual ligament bundles.

Computational knee models provide insights into knee biomechanics and could offer another tool in pre-operative planning in knee interventions. These models combine knee geometry and mechanical models of the knee structures (e.g., bone, ligaments, etc.). Medical images can make the geometry and nonlinear constitutive equations derive the knee structure properties. However, direct measurements of ligament properties in a non-invasive manner are impossible. Thus, generic values estimated from laxity tests are used for such properties. However, this application’s existing clinical laxity tests are nonoptimal as they are limited to a small set of load orientations and magnitudes. Laxity tests need to stretch individual ligament bundles differently than other bundles to resolve redundancy in the ligament structures and estimate the ligament bundle properties. The developed laxity device will be able to apply loads in all potential orientations or magnitudes that provide the maximum stretch of these bundles.

Towards this goal, a follow-up study will use a subset of subjects from an initial study using generic laxity testing. Their laxity data will combine with their knee computational model to estimate the subject-specific material properties of the ligaments via an optimisation procedure. These new properties will then be applied to the subject-specific models, and a similar optimisation sequence will be implemented to determine theoretical loading scenarios that optimally stretch individual ligament bundles These theoretical loading scenarios will then be tested using the laxity device again on the subset of subjects from the larger scale study to determine the efficacy of the algorithm.

Brett holds a Master of Science in Biomedical Engineering from the Chalmers University of Technology. You can read more about Brett’s contribution on Aalborg University’s homepage here.