Personalisation of coronary artery stenting

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Narrowing of coronary arteries is a major cause of cardiovascular diseases, and remains the single largest killer in the world. The common treatment with stents is largely generic, with complication rates as high as 8%. This is because individual differences are largely unknown. We have developed a unique dataset of 3D images of coronary structures from a large population, which for the first time enables personalised insights on anatomical variation of the coronary arteries. Together with the innovative combination of 3D imaging, computational modelling and advanced machine learning, the student will develop a new generation of computational tools to design personalised stenting and implantation strategies based on coronary shape. This will directly lead to optimisation of stenting procedures and thus increase treatment success.

The ideal candidate will be passionate about applying engineering tools to directly improve healthcare. This project is highly interdisciplinary, and will involve working with (and extending) a suite of existing computational models, statistical analysis, and machine learning tools. Responsibilities evolve around medical image segmentation and processing from CT using VMTK, MATLAB and similar tools, bash programming and flow modelling to run computational simulations on super-computers and data post-processing in python and R. Previous experience with some or all these programming languages and tools is highly desirable.

Supervisory team
Susann
Beier

Engineering
Mechanical and Manufacturing Engineering
Shane
Thomas

Medicine
Medical Sciences
Socrates
Dokos

Engineering
Biomedical Engineering
s.beier@unsw.edu.au