Application of diffusion tensor imaging techniques to study myocardial disarray in cardiac disease


We have an opportunity for a PhD student to join our team developing and applying magnetic resonance diffusion tensor imaging techniques to the study of cardiac disease.


Associate Professor Gemma Figtree, Professor Stuart Grieve

Research Location

North Shore - Kolling Institute of Medical Research

Program Type



The aim of this PhD project is to apply CMR techniques (diffusion tensor imaging) to assess for myocardial array and disarray in health and disease.
The organization of myocytes, particularly their "array" or "disarray", is key to understanding the mechanical and electrical properties of the heart. Diffusion Tensor imaging (DTI) is an MRI technique that enables the measurement of the restricted diffusion of water in tissue, and has recently emerged in the field of neuroimaging as an excellent tool to perform tractography of white matter. Its application to the heart has enormous potential. We have recently performed preliminary ex vivo DTI measurements of a normal mouse heart at 9.4 Telsa. Resolution was 0.15 x 0.15 x 0.5 micron voxel size. This pilot data demonstrates the potential of MR DTI methodology to noninvasively examine myocardial microarchitecture. This may have substantial impact on our ability to assess electrical and mechanical dysfunction. We have established collaborations with Professor Chris Semsarian and Professor Diane Faitkin, and the project will apply DTI techniques to quantify disarray in mouse models of hypertrophic and dilated cardiomyopathy, and to associate this with measures of arrhythmic potential in these mice. This work will have substantial implications to improved understanding and assessment of common human cardiac disease. The PhD student will be co-supervised by Dr Stuart Grieve, who has extensive experience in novel high-field MRI applications.

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Diffusion tensor imaging; cardiac magnetic resonance; hypertrophic cardiomyopathy; coronary artery disease

Opportunity ID

The opportunity ID for this research opportunity is: 1488

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