Development of novel computational models that enable personalised analysis of biomechanics of the heart

Cardiovascular disease is the number one leading cause of mortality worldwide (http://www.who.int/cardiovascular_diseases/en/). Over the past decade, the morbidity and mortality associated with cardiovascular disease have reduced due to advancements in patient care. However, the prevalence of myocardial pathologies, such as heart failure (HF), remains significant with over 26 M patients suffering from HF worldwide.

HF is a heterogeneous disease with a wide range of underlying mechanisms that ultimately lead to the inability of the heart to deliver sufficient oxygenated blood to meet the requirements of the body. Naive characterisation of HF patients using conventional clinical indices, such as ejection fraction (EF), do not necessarily reflect the underlying disease processes, and more mechanistically-driven approaches are needed. In particular, the intrinsic myocardial mechanical properties (such as muscle wall stiffness, stress, and contractility) are thought to play significant roles in the progression of cardiovascular disease, however their relative roles in the different forms of the disease remain unknown. Computational modelling of heart mechanics provides a consistent integrative platform to allow for robust estimation of intrinsic myocardial tissue properties, knowledge of which will enable better characterisation of patients, and the potential development and assessment of efficacy of new more mechanism-targeted treatment strategies.

We are seeking a highly-motivated student to undertake PhD research aimed at developing novel computational models that enable personalised analysis of biomechanics of both the left and right heart ventricles for a range of cardiac patients and control subjects. This PhD study is a collaboration between the Cardiac Mechanics Group at the Auckland Bioengineering Institute, University of Auckland, and St Francis Hospital, New York. Capabilities of the proposed modelling framework will include: 1) personalised computer modelling of the left and right ventricles from cardiac magnetic resonance images; 2) biomechanical analyses using patient using haemodynamic measurements as the ventricular loading conditions; 3) inverse estimation of the intrinsic myocardial properties; which will enable 4) predictions of myocardial stress distributions and work performed during the cardiac cycle. The estimated intrinsic muscle properties will be correlated with existing biomarkers and measurement of tissue fibrosis via cardiac MRI T1-imaging and diffusion weighted imaging (where available) to investigate the anatomical, microstructural, and biomechanical mechanisms underpinning different types of heart disease.

The successful candidate will join the Cardiac Mechanics Group, which is team of internationally recognised researchers in the fields of functional imaging and modelling of the heart. Our team provides a collaborative and open-minded research environment, and candidates with an interest in state-of-the-art cardiac imaging, mathematical modelling, computer model-based image analysis, and clinical cardiology, are all encouraged to apply.