The intervertebral disc is a complex, composite structure that is responsible for providing strength, stiffness and mobility to the spine in three dimensions during daily activities. A fundamental understanding of disc function is crucial for determining how the disc is injured during lifting activities that can occur as a one-off overload injury, or after years of repetitive lifting during manual handling activities. Experimental testing of the disc, together with computational models are used to understand the mechanisms of these injuries. Current computational models of spinal discs, for example, are unable to account for the complex, multi-axial, non-linear, time-dependent, multi-phasic (solid, fluid, ionic, chemical-electric) properties of the disc tissue. Validation of these models against experimental data is often lacking due to limitations in current mechanical testing systems. In order to understand how disc injuries occur, a validated computational model is required.
This project will involve developing a subject-specific computational (finite element) model of the lumbar vertebra-disc-vertebra segment using MRI and CT imaging data from cadaver lumbar spines. Validation of the model will be conducted against a rigorous dataset of experimental data that has been obtained from the same cadaver spines using an advanced, award-winning, multi-axial hexapod robot in the Biomechanics & Implants Laboratory, Medical Device Research Institute at Flinders University. The knowledge gained from this project will enable this model to be used for understanding which combinations of spinal movements, together with the magnitudes of loads that could lead to disc injuries, and ultimately towards developing strategies for minimising these injuries.
This research will involve collaboration with, and supervision from, an expert computational modeller at Queensland University of Technology (QUT): Associate Professor Paige Little, Principal Fellow in Spine Research, Group Leader Spine Research, Biomechanics & Spine Research Group, School of Mechanical, Medical and Process Engineering, Faculty of Engineering.
Associate Professor Paige Little (QUT) and Professor Mark Taylor
