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Purpose:The aim of this study was to validate a computational fluid dynamics (CFD) simulation of flow-diverter treatment through Doppler ultrasonography measurements in patient-specific models of intracranial bifurcation and side-wall aneurysms.Methods:Computational and physical models of patient-specific bifurcation and sidewall aneurysms were constructed from computed tomography angiography with use of stereolithography, a three-dimensional printing technology. Flow dynamics parameters before and after flow-diverter treatment were measured with pulse-wave and color Doppler ultrasonography, and then compared with CFD simulations.Results:CFD simulations showed drastic flow reduction after flow-diverter treatment in both aneurysms. The mean volume flow rate decreased by 90% and 85% for the bifurcation aneurysm and the side-wall aneurysm, respectively. Velocity contour plots from computer simulations before and after flow diversion closely resembled the patterns obtained by color Doppler ultrasonography.Conclusion:The CFD estimation of flow reduction in aneurysms treated with a flow-diverting stent was verified by Doppler ultrasonography in patient-specific phantom models of bifurcation and side-wall aneurysms. The combination of CFD and ultrasonography may constitute a feasible and reliable technique in studying the treatment of intracranial aneurysms with flow-diverting stents.

This paper presents an experimental study of two dimensional (2D) tissue motion estimation in ultrafast imaging using transverse oscillation and a phase based motion estimation. In this study, the motion is measured all along the wall of an artery phantom. The method employs ultrafast imaging which has become a world-wide used modality, with several promising clinical application, transverse oscillations technics which allow improving the motion estimation in transverse direction i.e. perpendicular to the beam axis, and a phase-based motion estimation. The experiment in vitro results show that the motion estimation is very reproducible all along the phantom wall, and even if the wall displacement estimable are very small ( 0.001 pixel ), our method is able to estimate the 2D motion.