Implementation of a novel direct tumor-targeting technique requires a computer modeling stage to generate particle release maps (PRMs) which allow for optimal catheter positioning and selection of best injection intervals for drug-particles. This simulation task for a patient-specific PRM may require excessive computational resources and a relatively long turn-around time for a fully transient analysis. Hence, steady-state conditions were sought which generates PRMs equivalent to the pulsatile arterial flow environment. Fluid-particle transport in a representative hepatic artery system was simulated under fully transient and steady-state flow conditions and their corresponding PRMs were analyzed and compared. Comparisons of the transient PRMs from ten equal intervals of the cardiac pulse revealed that the diastolic phase produced relatively constant PRMs due to its semisteady flow conditions. Furthermore, steady-state PRMs, which best matched the transient particle release maps, were found for each interval and over the entire cardiac pulse. From these comparisons, the flow rate and outlet pressure differences proved to be important parameters for estimating the PRMs. The computational times of the fully transient and steady simulations differed greatly, i.e., about 10 days versus 0.5 to 1 h, respectively. The time-averaged scenario may provide the best steady conditions for estimating the transient particle release maps. However, given the considerable changes in the PRMs due to the accelerating and decelerating phases of the cardiac cycle, it may be better to model several steady scenarios, which encompass the wide range of flows and pressures experienced by the arterial system in order to observe how the PRMs may change throughout the pulse. While adding more computation time, this method is still significantly faster than running the full transient case. Finally, while the best steady PRMs provide a qualitative guide for best catheter placement, the final injection position could be adjusted in vivo using biodegradable mock-spheres to ensure that patient-specific optimal tumor-targeting is achieved. In general, the methodology described could generate computationally very efficient and sufficiently accurate solutions for the transient fluid-particle dynamics problem. However, future work should test this methodology in patient-specific geometries subject to various flow waveforms.

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