Assessment and visualization of hemodynamic loading in aneurysm sac and neck: Effect of foam insertion.

Shape memory polymer (SMP) foam is often proposed as the future alternative of coils in aneurysm treatment devices. Present work numerically investigates the unsteady, three-dimensional simulation of blood flow in a cerebral aneurysm filled with SMP foam. Simulations are conducted on patient-specific geometries with realistic blood velocity waveform imposed at the inlet while SMP foam is treated as a porous medium. The present study introduces a "loading risk map" that helps to visualize the hemodynamic effect of foam insertion on the aneurysm sac and neck. The loading risk maps suggest that while the SMP foam subdues the flow and wall shear pulsations in the aneurysm sac, the pressure distribution is minimally affected. The maps suggest that while the downstream lip is the most risk-prone site for both geometries, downstream vascular anatomy significantly influences foam efficiency in reducing pressure and wall shear stress loading.

A fast alternative to computational fluid dynamics for high quality imaging of blood flow.

Obtaining detailed, patient-specific blood flow information would be very useful in detecting and monitoring cardio-vascular diseases. Current approaches rely on computational fluid dynamics to achieve this; however, these are hardly usable in the daily clinical routine due to the required technical supervision and long computing times. We propose a fast measurement enhancement method that requires neither supervision nor long computation and it is the objective of this paper to evaluate its performance as compared to the state-of-the-art. To this end a large set of abdominal aortic bifurcation geometries was used to test this technique and the results were compared to measurements and numerical simulations. We find that this method is able to dramatically improve the quality of the measurement information, in particular the flow-derived quantities such as wall shear stress. Additionally, good estimation of unmeasurable quantities such as pressure can be provided. We demonstrate that this approach is a practical and clinically feasible alternative to fully-blown, time-consuming, patient-specific flow simulations.