One of the challenges in coronary artery disease management is dealing with complex scenarios, the decision whether or how to intervene based on limited information from different sources. There are several variables that can affect how the heart responds to treatment, including but not limited to: the extent of the damage and scarring, the efficiency of blood flow remodelling of the heart, and any associated valve disease. Moreover, the effect of an intervention may lead to further unforeseen complications (e.g. another stenosis may be “hidden” further along the vessel). Currently there is no tool for predicting such scenarios.
The objective is to develop and analyse a highly adaptive and robust model of blood flow that considers patient specific data. Modelling realistically, accurately and in real-time the blood flow through complex networks such as coronary collateralization during acute cardiac events. Developing robust flow models that can be subsequently incorporated into decision/planning virtual reality medical tools. The focus will be on smaller, specific regions of the anatomy, where accuracy is the most of importance and the flow can develop into specific patterns. With the aim of better understanding their condition and predicting factors of their future evolution.
Mark graduated from the University of Chester in 2015 with a 1st class honours degree in Computer Science. He is currently studying an MPhil/PhD in the department of Computer Science. His current research interests are computer simulation, computer graphics and virtual reality for medical applications.