Accuracy of computational cerebral aneurysm hemodynamics using patient-specific endovascular measurements

Abstract

Computational hemodynamic simulations of cerebral aneurysms have traditionally relied on stereotypical boundary conditions (such as blood flow velocity and blood pressure) derived from published values as patient-specific measurements are unavailable or difficult to collect. However, controversy persists over the necessity of incorporating such patient-specific conditions into computational analyses. We perform simulations using both endovascularly-derived patient-specific and typical literature-derived inflow and outflow boundary conditions. Detailed three-dimensional anatomical models of the cerebral vasculature are developed from rotational angiography data, and blood flow velocity and pressure are measured in situ by a dual-sensor pressure and velocity endovascular guidewire at multiple peri-aneurysmal locations in 10 unruptured cerebral aneurysms. These measurements are used to define inflow and outflow boundary conditions for computational hemodynamic models of the aneurysms. The additional in situ measurements which are not prescribed in the simulation are then used to assess the accuracy of the simulated flow velocity and pressure drop. Simulated velocities using patient-specific boundary conditions show good agreement with the guidewire measurements at measurement locations inside the domain, with no bias in the agreement and a random scatter of ≈25%. Simulated velocities using the simplified, literature-derived values show a systematic bias and over-predicted velocity by ≈30% with a random scatter of ≈40%. Computational hemodynamics using endovascularly measured patient-specific boundary conditions have the potential to improve treatment predictions as they provide more accurate and precise results of the aneurysmal hemodynamics than those based on commonly accepted reference values for boundary conditions.

Fig. 1

Velocity waveform vs. time used for the simplified boundary conditions.

Fig. 2

Spatial distributions of the time-averaged wall shear stress (TAWSS) in Pa for a single patient model (case no. 4). (a) simulation using patient-specific boundary conditions. (b) simulation with simplified boundary conditions.

Fig. 3

Normalized WSS of simplified vs. patient-specific boundary condition cases for (a) mean values and (b) peak values. Solid line is the identity line, and dashed line is the linear least squares fit.

Fig. 4

Comparison of CFD and phase-averaged wire peak systolic velocity measurements. (a) Comparison using patient-specific boundary conditions. (b) Comparison using simplified boundary conditions. Dashed line is the mean bias, and dotted line is ±2 standard deviations from the mean bias.

Fig. 5

Comparison of CFD and phase-averaged wire velocity measurements vs. time for a single patient model (case no. 4). (a) Location 2 using patient-specific boundary conditions. (b) Location 2 using simplified boundary conditions. (c) Location 3 using patient-specific boundary conditions. (d) Location 3 using simplified boundary conditions.

Fig. 6

Comparison of CFD and wire-derived peak systolic pressure drops. (a) Comparison using patient-specific boundary conditions. (b) Comparison using simplified boundary conditions. Dashed line is the mean bias, and dotted line is ±2 standard deviations from the mean bias.