Is Accurate Lumen Segmentation More Important than Outlet Boundary Condition in Image-Based Blood Flow Simulations for Intracranial Aneurysms?

Abstract

Purpose Image-based blood flow simulations are increasingly used to investigate the hemodynamics in intracranial aneurysms (IAs). However, a strong variability in segmentation approaches as well as the absence of individualized boundary conditions (BCs) influence the quality of these simulation results leading to imprecision and decreased reliability. This study aims to analyze these influences on relevant hemodynamic parameters within IAs. Methods As a follow-up study of an international multiple aneurysms challenge, the segmentation results of five IAs differing in size and location were investigated. Specifically, five possible outlet BCs were considered in each of the IAs. These are comprised of the zero-pressure condition (BC1), a flow distribution based on Murray’s law with the exponents n = 2 (BC2) and n = 3 (BC3) as well as two advanced flow-splitting models considering the real vessels by including circular cross sections (BC4) or anatomical cross sections (BC5), respectively. In total, 120 time-dependent blood flow simulations were analyzed qualitatively and quantitatively, focusing on five representative intra-aneurysmal flow and five shear parameters such as vorticity and wall shear stress. Results The outlet BC variation revealed substantial differences. Higher shear stresses (up to Δ9.69 Pa), intrasaccular velocities (up to Δ0.15 m/s) and vorticities (up to Δ629.22 1/s) were detected when advanced flow-splitting was applied compared to the widely used zero-pressure BC. The tendency of outlets BCs to over- or underestimate hemodynamic parameters is consistent across different segmentations of a single aneurysm model. Segmentation-induced variability reaches Δ19.58 Pa, Δ0.42 m/s and Δ957.27 1/s, respectively. Excluding low fidelity segmentations, however, (a) reduces the deviation drastically (>43%) and (b) leads to a lower impact of the outlet BC on hemodynamic predictions. Conclusion With a more realistic lumen segmentation, the influence of the BC on the resulting hemodynamics is decreased. A realistic lumen segmentation can be ensured, e.g., by using high-resolved 2D images. Furthermore, the selection of an advanced outflow-splitting model is advised and the use of a zero-pressure BC and BC based on Murray’s law with exponent n = 3 should be avoided.

Fusiform versus Saccular Intracranial Aneurysms—Hemodynamic Evaluation of the Pre-Aneurysmal, Pathological, and Post-Interventional State

“…A splitting value was set at each outlet calculated with an in-house flow splitting method specific for each vessel model [ 35 ]. The splitting method was previously proofed to be stable and realistic [ 36 ].…”

Multi-Dimensional Modeling of Cerebral Hemodynamics: A Systematic Review

“…First, blood as a non-Newtonian fluid can be modeled with the Carreau model [80,119,154,155] or the Carreau-Yasuda model to mimic the non-Newtonian behavior [13]. Nevertheless, blood is mostly handled as Newtonian fluid with constant viscosity [15,16,156]. Berg et al, Fan et al and Razavi et al compared the impact of the choice of fluid modeling and found no significant difference between non-Newtonian and Newtonian blood composition in laminar flows [68,73,80].…”

“…In older studies, specific blood pressure or a zero pressure condition is used [80,81,164]. In recent research, novel splitting approaches are applied to better mimic the outflow conditions [16,27,162]. Moreover, 0D resistance models are often used at the vessel outlets [1,68,117,165], which will be further examined in Section 4.2.…”

“…Three-dimensional models are therefore used to compute the highly resolved neurovascular hemodynamics in complex vessel structures [1]. In particular, the impact of the morphology and prescribed boundary conditions on these hemodynamics can be analyzed using 3D models [16,166]. The geometries can be allocated into two categories [1]: idealized models, which are created artificially based on the literature [75,80,81,117,165,167], and patient-specific models, which are based on in vivo data captured with medical imaging techniques [1,13,68,72,80,83,123,125,135,168].…”

“…Therefore, mostly only a small part of the vessel containing the pathology is analyzed [15]. Moreover, since patientspecific real-time flow data are difficult to capture and are therefore rarely available, the surrounding information, which is used as boundary conditions, is often based on assumptions or taken from the literature [16]. In addition to receiving the boundary conditions from lowscale models, coupled multi-scale approaches exist, directly exchanging flow and pressure information [17,18].…”