Coronary artery disease is a leading cause of death worldwide, majorly impacting healthcare systems. This research demonstrated the ability for novel computational haemodynamic analyses to provide new insights into the functional environment of image-based coronary plaque characteristics. Geometric factors and biomechanical disease indicators, such as endothelial shear stress, were shown to be significantly predictive of elevated levels of micro-calcification activity, previously associated with high-risk features in coronary artery disease. The developed computational modelling framework may help maximise the information available to assist with disease risk stratification, while posing no harm to patients.
|Qualification||Doctor of Philosophy|
|Award date||7 Nov 2019|
|Publication status||Unpublished - 2019|
- Embargoed from 19/11/2019 to 19/11/2021. Made publicly available on 19/11/2021.