Relationships and redundancies of selected hemodynamic and structural parameters for characterizing virtual treatment of cerebral aneurysms with flow diverter devices. Issue 11 (26th July 2016) - Karmonik, C.; Anderson, J.R.; Beilner, J.; Ge, J.J.; Partovi, S.; Klucznik, R.P.; Diaz, O.; Zhang, Y.J.; Britz, G.W.; Grossman, R.G.; Lv, N.; Huang, Q.
Abstract: Background and purpose: To quantify the relationship and to demonstrate redundancies between hemodynamic and structural parameters before and after virtual treatment with a flow diverter device (FDD) in cerebral aneurysms. Methods: Steady computational fluid dynamics (CFD) simulations were performed for 10 cerebral aneurysms where FDD treatment with the SILK device was simulated by virtually reducing the porosity at the aneurysm ostium. Velocity and pressure values proximal and distal to and at the aneurysm ostium as well as inside the aneurysm were quantified. In addition, dome-to-neck ratios and size ratios were determined. Multiple correlation analysis (MCA) and hierarchical cluster analysis (HCA) were conducted to demonstrate dependencies between both structural and hemodynamic parameters. Results: Velocities in the aneurysm were reduced by 0.14 m/s on average and correlated significantly ( p <0.05) with velocity values in the parent artery (average correlation coefficient: 0.70). Pressure changes in the aneurysm correlated significantly with pressure values in the parent artery and aneurysm (average correlation coefficient: 0.87). MCA found statistically significant correlations between velocity values and between pressure values, respectively. HCA sorted velocity parameters, pressure parameters and structural parameters into different hierarchical clusters. HCA of aneurysms based on the parameter values yielded similar results by either including all ( n =22) or only non-redundant parameters ( n =2, 3 and 4). Conclusion: Hemodynamic and structural parameters before and after virtual FDD treatment show strong inter-correlations. Redundancy of parameters was demonstrated with hierarchical cluster analysis.