My Expertise
- biomedical engineering, computational fluid dynamics (CFD) modelling, coronary flow, simulations, modelling, medical imaging, deep learning / machine learning, fluid dynamics
Biomedical engineering, specifically fluid dynamics and mechanics of cardiovascular arteries to understand and control biological systems to inform clinical intervention such as stent implants. This included medical imaging and analysis, optimisation and experimental fluid dynamic and mechanical testing.
Field of Research (FoR)
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Biography
PHD RESEARCH TOPICS AVAILABLE - please see below and contact Susann for more information.
Current Research Projects
- Advanced medical image analysis
- Stent design optimisation
- Machine learning / Deep learning to understand disease risk
- Arterial computational fluid dynamics (CFD)
- Nanomedicine
- Fluid-structure interactions in stented arteries
- Cell migration modelling
- Advanced 4D MRI flow
For more information on Dr Beier's research please...view more
PHD RESEARCH TOPICS AVAILABLE - please see below and contact Susann for more information.
Current Research Projects
- Advanced medical image analysis
- Stent design optimisation
- Machine learning / Deep learning to understand disease risk
- Arterial computational fluid dynamics (CFD)
- Nanomedicine
- Fluid-structure interactions in stented arteries
- Cell migration modelling
- Advanced 4D MRI flow
For more information on Dr Beier's research please see the Coronary Atlas website and the Sydney Vascular Modelling Group's page.
Bio
Susann is originally from Germany were she received her mechanical engineering undergraduate degree with the KIT Research Center and the Cooperate State University of Baden-Wuerttemberg. After she worked at the Light Metals Research Center in Auckland, New Zealand as a Research Engineer where she soon lead national and international teams on large-scale research projects. During this time she also undertook her Research Masters in Chemical and Materials Engineering at the University of Auckland. Later she relocated to the Medical Faculty to undertake her PhD in Biomedical Sciences, combining her knowledge of material, fluid dynamic and biomedical sciences. Since, her focus has been engineering driven cardiovascular disease treatment and her work spans across image processing, large data analysis and computational modelling of blood flow and tissue mechanics. Susann joined UNSW as a Lecturer in January 2018 and continues to run a team in Auckland while building up her group in Australia. Susann is based at the School of Mechanical and Manufacturing Engineering and works closely with the Graduate School of Biomedical Engineering and Computer Sciences.
Lecturing
MECH9650 Microfluidics
Speaking
- Invited Chair, European Bifurcation Club EBC, Brussel, 2018
- European Bifurcation Club EBC, Porto, Portugal, 2017
- European Bifurcation Club EBC, Rotterdam, Netherlands, Keynote, 2016
- Research Bazaar ResBaz, Auckland, New Zealand, Keynote, 2016
- Annual Meeting of the Cardiovascular Society CSANZ, Auckland, New Zealand, Keynote, 2015
- MICCAI CVII-STENT, presentation, 2015
- European Bifurcation Club EBC, presentation, 2015
Awards
- The Australian Academy of Sciences Travel Award for ECR, 2018
- Heart Foundation Fellowship, 2017
- Visiting Fellow, Stanford University, 2017
- Auckland Medical Research Foundation (AMRF) Project grant $153,000 NZD, 2017
- Auckland Academic Health Alliance (AAHA) Collaboration Fund $100,000 NZD, 2016
- Cardiovascular Research Innovations Grant $35,000 AUD by the Cardiovascular Society of Australia and New Zealand, 2016
- Auckland Charitable Heart Trust, Research Fellowship, 2015
- Emerging Research of the Year Award, Runner-up, 2015
- 3min Thesis Competition, Finalist, 2014
- Emerging Research of the Year Award, Runner-up, 2013
- Auckland Charitable Heart Trust, Doctoral Scholarship, 2011
- Laval University Travel Award, 2010
Location
Level 4, Room 401D
UNSW, Kensington Campus
Sydney NSW 2052
AUSTRALIA
Map reference (Google map)
Research Activities
Research project seeking students: Stents are meshed wired scaffolds implanted into narrowed arteries to prevent heart attacks and strokes. They still fail in 1 in 10 patients besides being the most common treatment for the largest cause of death world-wide.
The aim of this work is to generate CAD models from micro-CT images of stented patient replicas. The replicas represent the anatomical/geometric distribution found within a large population. The CAD models will be used to generate CFD simulation of the blood flow, revealing their effectiveness for that patient…
Stents are meshed wired scaffolds implanted into narrowed arteries to prevent heart attacks and strokes. They still fail in 1 in 10 patients besides being the most common treatment for the largest cause of death world-wide.
The aim of our work is to expand a number of numerical CFD simulations to generate input for a developed multi-object algorithm. The optimum design trade offs for modern stents can then be determined through several iterations of solutions.
The specific outcome are new guidelines for clinicians to be implemented in practice,…
Research Project seeking students: Cardiovascular disease is the single largest killer in the world, causing heart attacks and strokes. Experimental investigation of underlying principals are key in combating this epidemic, however individual considerations are still completely missing.
We are continuously expanding the Coronary Atlas, which is a virtual map of arterial shape and blood flow. Using in-house build tools to run bash programmed batch processes on the National Computing Infrastructure we can fast process hundreds of medical CT images at the time. These are then…
Cardiovascular disease is the single largest killer in the world, causing heart attacks and strokes. Experimental investigation of underlying principals are key in combating this epidemic, however individual considerations are still completely missing.
The aim of this work is to develop accurate machine learning neural networks to identify links between a large set of demographic, arterial shape and flow data with clinical risk. This will help to identify biomarkers which can be deployed in clinical practice for early risk detection.
Available data will be…
