One of the challenges in cardiovascular disease management is the clinical
decision-making process. When a clinician is dealing with complex and uncertain
situations, the decision on whether or how to intervene is made based upon distinct
information from diverse sources. There are several variables that can affect how
the vascular system responds to treatment. These include: the extent of the damage
and scarring, the efficiency of blood flow remodelling, and any associated pathology.
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 or exploring such scenarios.
This thesis explores the development of a highly adaptive real-time simulation of
blood flow that considers patient specific data and clinician interaction. The simulation
should model blood realistically, accurately, and through complex vascular networks
in real-time. Developing robust flow scenarios that can be incorporated into the
decision and planning medical tool set. The focus will be on specific regions of the
anatomy, where accuracy is of the utmost importance and the flow can develop into
specific patterns, with the aim of better understanding their condition and predicting
factors of their future evolution. Results from the validation of the simulation showed
promising comparisons with the literature and demonstrated a viability for clinical
use