My current research studies are focused on Magnetic Resonance Elastography (MRE), which is a quantitative technique to understand structural and material property of the human body organs and biological tissues. Many diseases affect tissue stiffness in a profound way. Some diseases such as cancer, inflammation and fibrosis increase tissue stiffness and some other diseases such as myopathy, emphysema decrease tissues stiffness. As a member of Prof. Bilston’s team, my current focus is developing nonlinear anisotropic models, which can improve analysis of the tissue properties significantly.
My background includes linear and nonlinear continuum mechanics, viscoelasticity, loss and storage energy, mechanical characterization of biological specimens, the suite of wet lab such as fibrosis tissue culturing, immunostaining and different microscopy skills. I am expert with finite element analysis, numerical solution of systems of ODEs. Moreover, I am skilled at softwares such as Ansys, Ls-Dyna, FEM Builder, Solid Works, Cosmos Works, Visual Nastran, MATLAB simulation, MATLAB programming, Labchart pro, Office tools and LateX.
My PhD work represents what I consider to be important breakthroughs in nonlinear mechanics of cells and biological tissues. I have made two theoretical contributions that can have substantial importance, and a series of interesting discoveries using these tools in a range of physiological and pathophysiological phenomena. I applied these tools to show how fibrosis selectively changes the damping of reconstituted heart tissue to lead to diastolic dysfunction in late stage fibrotic cardiomyopathy, and to identify the regimes over which stretching is most effective in ligaments. Moreover, I studied of the hierarchical origins of damping and viscoelasticity in tendon.
We have developed new MRI methods to measure the mechanical properties of soft tissues (Magnetic Resonance Elastography or MRE). So far, MRE has been used to measure the stiffness of the brain, muscles and other tissues. We continue to develop new approaches, such as combining elastography with Diffusion Tensor Imaging to measure the anisotropic properties of muscles and brain white matter tracts, and how this changes in muscle and neurological disorders. We have discovered that there are changes in tissue stiffness in hydrocephalus (a brain disorder), obstructive sleep apnoea, and degenerative muscle conditions (muscular dystrophy). We are currently working on new methods to measure tissue properties under loading. Honours and PhD projects are available both for developing new methods (to suit engineers and physicists) or in applying these techniques to study clinical disorders.
KATIE PELLAND Visiting PhD student
DR ELIZABETH CLARKE Visiting postdoctoral fellow
ALICE HATT Research assistant
ALICE PONG PhD student
FIONA KNAPMAN Research assistant
DR PETER BURKE Postdoctoral fellow
The viscoelastic behaviour of a biological material is central to its functioning and is an indicator of its health. The Fung quasi-linear viscoelastic (QLV) model, a standard tool for characterizing biological materials, provides excellent fits to most stress-relaxation data by imposing a simple form upon a material's temporal relaxation spectrum. However, model identification is challenging because the Fung QLV model's 'box'-shaped relaxation spectrum, predominant in biomechanics applications, can provide an excellent fit even when it is not a reasonable representation of a material's relaxation spectrum. Here, we present a robust and simple discrete approach for identifying a material's temporal relaxation spectrum from stress-relaxation data in an unbiased way. Our 'discrete QLV' (DQLV) approach identifies ranges of time constants over which the Fung QLV model's typical box spectrum provides an accurate representation of a particular material's temporal relaxation spectrum, and is effective at providing a fit to this model. The DQLV spectrum also reveals when other forms or discrete time constants are more suitable than a box spectrum. After validating the approach against idealized and noisy data, we applied the methods to analyse medial collateral ligament stress-relaxation data and identify the strengths and weaknesses of an optimal Fung QLV fit.