Research
Our research aims to understand the physiology of vascular function and blood flow to translate findings into clinical understanding and applications for disease treatment. Our primary focus is the characterisation of the pathophysiological processes of coronary artery disease. We use powerful image processing techniques and computational and experimental modelling of patient-specific clinical data. Personalised modelling pipelines are developed to help early disease diagnosis and improve treatment strategies through outcome predictions. We collaborate closely with clinicians.
Current research projects
Paper explorer
Ideal stent
Ideal stent
Explore the evolution of stent design in our comprehensive review, tracing the journey from early metallic stents to the latest bioresorbable technologies. We delve into advancements in performance metrics and the continuous pursuit of the ideal stent.
Coronary artery imaging and annotations – ASOCA
Coronary artery imaging and annotations – ASOCA
Explore our comprehensive dataset featuring anonymized computed tomography coronary angiography (CTCA) images, complete with voxel-wise annotations, centrelines, calcification scores, and coronary lumen meshes for 20 normal and 20 diseased cases. This resource is invaluable for 3D printing patient-specific models, developing and validating segmentation algorithms, educating medical personnel, and conducting in-silico analyses, such as testing medical devices.
Rapid computational blood flow modelling
Rapid computational blood flow modelling
Explore how deep learning techniques can revolutionize the prediction of luminal wall shear stress (WSS) in coronary bifurcations. This study introduces a convolutional neural network model that estimates transient WSS throughout the cardiac cycle, achieving predictions within 5% deviation from traditional computational fluid dynamics (CFD) methods. Notably, this approach reduces computation time from approximately 3 hours to under 2 minutes, offering a rapid and accurate alternative to CFD for large-scale studies and potential clinical applications.
From coronary anatomy directly to risk markers
From coronary anatomy directly to risk markers
Discover how deep learning can expedite the estimation of time-averaged wall shear stress (TAWSS) in left main coronary bifurcations. This study presents a model that predicts TAWSS with high accuracy, achieving a mean absolute error of 0.0407 Pa, while significantly reducing computation time compared to traditional methods.
Revolutionary stent design optimisation approach
Revolutionary stent design optimisation approach
Explore the innovative design of auxetic metamaterial-inspired coronary stents through topological optimization. This study introduces stent architectures that enhance deliverability and reduce mechanical failure risks, while also improving wall shear stress distribution to mitigate adverse hemodynamic effects associated with stent thrombosis and in-stent restenosis.
First cross-design concept blood flow and structural stent optimisation
First cross-design concept blood flow and structural stent optimisation
Explore the optimization of stent geometries through multi-objective analysis. This study evaluates design variables by assessing trade-offs using common hemodynamic indices, aiming to enhance stent performance.
Accessing small-scale blood flow experimentally
Accessing small-scale blood flow experimentally
Explore the feasibility of characterizing coronary flow using a dynamically scaled phase contrast MRI (PC-MRI) phantom. This study demonstrates that such an approach provides higher resolution than current in vivo methods, offering a promising avenue for improved cardiac flow assessments.
Curvature plaque insights
Curvature plaque insights
Explore how coronary artery anatomy and hemodynamics influence the development and progression of atherosclerotic plaques. This study identifies key factors such as topological shear variation index (TSVI), curvature, and oscillatory shear index (OSI) as significant contributors to plaque onset, while time-averaged endothelial shear stress (TAESS) and relative residence time (RRT) are associated with plaque progression. These findings enhance our understanding of coronary artery disease mechanisms, potentially guiding improved diagnostic and therapeutic strategies.
Get involved
Faculty members, industry, graduate school students or students looking for graduate programs, we want to hear from you. We're recruiting undergraduate and postgraduate bioengineering students and offering PhD bioengineering scholarships. Joint PhD projects are also possible. Biomedical engineering Post-Doc inquiries are also encouraged.
Faculty members, industry and clinicians are encouraged to reach out. Especially if you're working in biomedical engineering and cardiovascular sciences, tissue engineering, life sciences, stem cell engineering and general stem cells in biological systems. We want to hear from you if you're interested in collaborating across biomedical engineering for cardiovascular health.