Computational fluid dynamics modelling of perfusion measurements in dynamic contrast-enhanced computed tomography:...

Dynamic contrast-enhanced computed tomography (DCE-CT) is an imaging tool that aids in evaluating functional characteristics of tissue at different stages of disease management: diagnostic, radiation treatment planning, treatment effectiveness, and monitoring. Clinical validation of DCE-derived perfusion parameters remains an outstanding problem to address prior to perfusion imaging becoming a widespread standard as a non-invasive quantitative measurement tool. One approach to this validation process has been the development of quality assurance phantoms in order to facilitate controlled perfusion ex vivo. However, most of these systems fail to establish and accurately replicate physiologically relevant capillary permeability and exchange performance. The current work presents the first step in the development of a prospective suite of physics-based perfusion simulations based on coupled fluid flow and particle transport phenomena with the goal of enhancing the understanding of clinical contrast agent kinetics. Existing knowledge about a controllable, two-compartmental fluid exchange phantom was used to validate the computational fluid dynamics (CFD) simulation model presented herein. The sensitivity of CFD-derived contrast uptake curves to contrast injection parameters, including injection duration and flow rate, were quantified and found to be within 10% accuracy. The CFD model was employed to evaluate two commonly used clinical kinetic algorithms used to derive perfusion parameters: Fick's principle and the modified Tofts model. Neither kinetic model was able to capture the true transport phenomena it aimed to represent but if the overall contrast concentration after injection remained identical, then successive DCE-CT evaluations could be compared and could indeed reflect differences in regional tissue flow. This study sets the groundwork for future explorations in phantom development and pharmaco-kinetic modelling, as well as the development of novel contrast agents for DCE imaging.