Virtual Coronary Intervention: In Silico Treatment planning in Coronary Artery Disease.

Using Fractional Flow Reserve (FFR) to guide Percutaneous Coronary Intervention (PCI) improves patient outcomes and reduces costs, yet is currently used in less than 10% of all cases. Utilising computational fluid dynamics modelling techniques, it is possible to model a virtual FFR (vFFR) based upon imaging alone, removing the need for invasive instrumentation. Several groups have developed models to achieve this based upon either computed tomography coronary angiography or invasive angiographic imaging. These models could increase the availability of physiological assessment and also lend themselves to virtual coronary intervention (VCI); the ability to model the insertion of stents and predict the physiological outcome. This would be advantageous in treatment planning as a number of strategies could be trialled, allowing the operator to select the optimal procedure, before committing to intervention in the patient. This thesis describes the development and validation of a VCI tool as an add-on to the existing VIRTUheartTM angiography based vFFR system that has been developed at the University of Sheffield. The tool is initially validated against invasively acquired post PCI FFR values in a prospective study. Subsequent chapters assess the ability of this tool to impact ‘real world’ stenting, by predicting the best possible FFR on a vessel by vessel basis, determining the optimal strategy and impacting decision making in a virtual clinic setting.