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Research Papers

Hemodynamics of Flow Diverters OPEN ACCESS

[+] Author and Article Information
Ronak Dholakia, Chander Sadasivan, David J. Fiorella, Henry H. Woo

Department of Neurological Surgery,
Stony Brook University Medical Center,
Stony Brook, NY 11794

Baruch B. Lieber

Professor
Department of Neurological Surgery,
Stony Brook University Medical Center,
HSC T12, Room 080,
100 Nicolls Road,
Stony Brook, NY 11794-8122
e-mail: Baruch.lieber@stonybrook.edu

1Corresponding author.

Apart from the device comparison section on Hemodynamics of Stents and Flow Diverters, portions of this paper appear as part of a book chapter in “Flow Diversion of Cerebral Aneurysms,” 2016, Min S. Park, Phil Taussky, Felipe C. Albuquerque, and Cameron G. McDougall, Eds., Thieme Medical Publishers, New York, www.thieme.com (Reprinted with Permission).Manuscript received June 29, 2016; final manuscript received September 21, 2016; published online January 19, 2017. Assoc. Editor: Victor H. Barocas.

J Biomech Eng 139(2), 021002 (Jan 19, 2017) (10 pages) Paper No: BIO-16-1270; doi: 10.1115/1.4034932 History: Received June 29, 2016; Revised September 21, 2016

Cerebral aneurysms are pathological focal evaginations of the arterial wall at and around the junctions of the circle of Willis. Their tenuous walls predispose aneurysms to leak or rupture leading to hemorrhagic strokes with high morbidity and mortality rates. The endovascular treatment of cerebral aneurysms currently includes the implantation of fine-mesh stents, called flow diverters, within the parent artery bearing the aneurysm. By mitigating flow velocities within the aneurysmal sac, the devices preferentially induce thrombus formation in the aneurysm within hours to days. In response to the foreign implant, an endothelialized arterial layer covers the luminal surface of the device over a period of days to months. Organization of the intraneurysmal thrombus leads to resorption and shrinkage of the aneurysm wall and contents, eventually leading to beneficial remodeling of the pathological site to a near-physiological state. The devices' primary function of reducing flow activity within aneurysms is corollary to their mesh structure. Complete specification of the device mesh structure, or alternately device permeability, necessarily involves the quantification of two variables commonly used to characterize porous media—mesh porosity and mesh pore density. We evaluated the flow alteration induced by five commercial neurovascular devices of varying porosity and pore density (stents: Neuroform, Enterprise, and LVIS; flow diverters: Pipeline and FRED) in an idealized sidewall aneurysm model. As can be expected in such a model, all devices substantially reduced intraneurysmal kinetic energy as compared to the nonstented case with the coarse-mesh stents inducing a 65–80% reduction whereas the fine-mesh flow diverters induced a near-complete flow stagnation (∼98% reduction). We also note a trend toward greater device efficacy (lower intraneurysmal flow) with decreasing device porosity and increasing device pore density. Several such flow studies have been and are being conducted in idealized as well as patient-derived geometries with the overarching goals of improving device design, facilitating treatment planning (what is the optimal device for a specific aneurysm), and predicting treatment outcome (will a specific aneurysm treated with a specific device successfully occlude over the long term). While the results are generally encouraging, there is poor standardization of study variables between different research groups, and any consensus will only be reached after standardized studies are conducted on collectively large datasets. Biochemical variables may have to be incorporated into these studies to maximize predictive values.

FIGURES IN THIS ARTICLE
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Flow diverters are essentially fine-mesh stents that are endovascularly implanted in cerebral arteries at the site of aneurysms. They are so named because their primary function is to “divert” blood flow away from the aneurysm and into the parent artery (strictly speaking, they reduce flow activity within the aneurysm). The theoretical sequence of phenomena governing flow diversion treatment of aneurysms can be listed simply as follows: (a) The placement of a fine-mesh screen at the aneurysm neck reduces the blood flow activity within the aneurysm, (b) the sluggish flow promotes clot formation within the aneurysm as platelets are activated when they go through the fine mesh and are trapped inside the aneurysm, (c) the fine-mesh screen is paved over by a new arterial wall lining, and (d) the thrombosed aneurysm is resorbed by the body's wound healing mechanisms—the end result of which is a remodeled vessel returned to its normal physiological state. These events are schematically described in Fig. 1. While it is a relatively straightforward list, needless to mention, the actual interplay of the various processes involved is quite complex.

The mesh structure of flow diverters is crucial to their effective function so, at the very least, a flow diverter's mesh needs to be far finer than that of a stent (Fig. 2(a)). Because the device acts as a porous screen at the aneurysm neck, some characteristics of porous media can be used to characterize the mesh structure. Porous media are studied in a wide variety of fields such as hydrogeology, petroleum engineering, ceramics, concrete, and textiles; in general, these fields evaluate two fundamental characteristics of the medium—porosity and pore connectivity—to assess the overall permeability [13]. While the permeability is dependent on the porosity measure, it does not rely only on this variable [3]. For the flow diverters, the two variables effecting device permeability and, by extension, device treatment efficacy can be termed as porosity and pore density. Porosity is the percentage ratio of the metal-free surface area (total surface area − metal surface area) to the total surface area. This is alternately referred to as metal coverage (ratio of metal surface area to total surface area). Pore density is number of pores per unit surface area of the device. Flow diverters are manufactured by braiding (helically wrapping multiple metal wires into a tubular structure) and the fundamental variables governing device design are the device diameter, the wire diameter, and the angle that the wires make to the device axis (called braiding angle). The nominal or design porosity and pore density of a device can be mathematically expressed in terms of these variables.

Using these mathematical expressions, the theoretical variations in porosity and pore density of a characteristic flow diverter as it is deployed in arteries of varying diameter can be plotted (Fig. 2(b)). Braiding allows for the device wires to slip on each other and thus elongate longitudinally when deployed in arteries with smaller diameter than the device's in-air diameter and vice versa expand in diameter when compressed longitudinally (such device “packing” at the aneurysm neck section is common practice when deploying some commercial flow diverters). While this feature allows for superior wall apposition in cerebral vessels of varying diameter, the deployed pore structure of a braided device, and hence its permeability at the aneurysm neck, will vary depending on parent vessel diameter, curvature, aneurysm neck size, etc. (Fig. 2(c)). As the plots show, the device oversizing can cause a maximal change (relative to designed values) in porosity and pore density of around 20–25% for most braided flow diverters. Whether this range effects aneurysm treatment efficacy is not yet clear, but a preliminary study [4] suggests that just a 3% absolute difference (∼12% relative difference) in deployed device porosity can result in different aneurysm outcomes. The point of minimal efficacy (highest porosity and lower pore density) occurs when the angle between the wires is 90 deg. On the other hand, the radial expansion of the devices by longitudinal compression results in decreased device porosity and increased pore density.

Detailed evaluations of the changes in intra-aneurysmal hemodynamics due to flow diverters have mostly been conducted via in vitro and numerical studies [511]. The traditional or theoretical view of flow patterns in untreated idealized sidewall aneurysms holds that the parent artery flow divides or impinges at the distal neck of the aneurysm, follows the distal wall to the dome, and exits the aneurysm near the proximal neck thereby forming a vortex within the aneurysm (Fig. 3(a)). Given the complexity and variability of patient aneurysm geometries, this flow pattern can vary substantially with the pulsatility and strength of the parent artery flow, aneurysm geometry and its orientation with respect to the parent vessel, and presence of side branches at or near the aneurysm neck. Depending on these parameters, for example, there may be a single vortex with its center undulating (with the cardiac cycle) within the aneurysm and transiently entering the parent vessel [6,12], or the flow may divide after it enters the aneurysm and form regions with both clockwise and counterclockwise flow [7,13]. In general, and based mostly on the numerical simulations, the peak velocities ranging from 10% to 150% of the parent vessel velocity and average wall shear stress (shearing force per unit area exerted by fluid motion along the wall) values ranging from 10 to 220 dynes/sq cm have been reported within aneurysms [14].

The placement of a flow diverter across the aneurysm neck breaks up the in- and outflow patterns. The intra-aneurysmal flow activity (optimally) becomes restricted to the neck region (Fig. 3(a), Pipeline device) and is generally in the direction of parent artery flow, entering near the proximal neck and exiting near the distal neck [6,7,15,16]. Most flow studies thus far have involved sparse, low sample sizes because of the computational expense required to numerically simulate flow fields in flow diverted aneurysms or the device expense involved with benchtop studies. Several intra-aneurysmal hemodynamics parameters have been evaluated, but the most common ones are related to flow velocity or its derivatives (inflow rate, average velocity, maximum velocity, kinetic energy (proportional to velocity squared), vorticity and hydrodynamic circulation (velocity measures of rotational/vortical motion)); wall shear stress, a function of velocity gradient and viscosity is another common parameter. Aggregating the results from about 20 flow studies on idealized, in vivo, and patient geometries (87 patient cases, half of which are from two studies [10,11]) suggests a spectrum of flow diversion response. Flow diversion reduces the intraneurysmal flow activity in idealized/simplified sidewall-type geometries by about 75–95% [7,1720], by about 20–40% in simplified bifurcation-type geometries [17,21], and by around 60% in an in vivo aneurysm model [22,23]. Reduction in the intraneurysmal flow activity in clinical cases runs the entire gamut from around 20–30% in bifurcation geometries to around 80–90% in sidewall geometries with an average reduction of around 50–60% [911,2431].

While this class of studies measures the detailed intraneurysmal velocity fields with the concomitant experimental or computational expense associated with trying to simulate reality, another category [3235] evaluates the intraneurysmal transport of angiographic contrast from the images already being acquired during the endovascular procedure; these studies also note significant increases in contrast residence time measures after flow diversion. As a side consequence of this sluggish flow after flow diversion, the intraneurysmal blood viscosity increases especially near the aneurysm dome because blood is a non-Newtonian fluid and so its viscosity increases with decreasing shear rate [36] (velocity gradient or change in velocity over a distance). A numerical simulation considering non-Newtonian effects in a simplified geometry suggests a threefold increase in blood viscosity in more than half of the aneurysm sac as compared to the nonstented case [37]. Another noteworthy phenomenon is that because the devices are porous and the aneurysm is not hemodynamically sealed off immediately after device deployment, there is essentially no change in the intraneurysmal pressure due to the deployment of a flow diverter [38].

Effect of Pore Structure.

The pore structure of a flow diverter primarily controls the device-induced intraneurysmal flow alterations. As can be expected, reducing the device porosity can substantially reduce the flow activity within the aneurysm, and numerous studies over the past two decades have confirmed this [6,7,17,19,21,28,39,40]. The numerical simulation results in Fig. 3(a) show an example of dampened flow activity within a simplified sidewall aneurysm from the deployment of devices with decreasing porosity. The vortical flow structure (and velocity magnitude) is slightly reduced after the deployment of a high-porosity stent (Neuroform), but is severely diminished after deployment of a flow diverter (Pipeline). Figure 3(b) shows the collated results from a few studies evaluating the effect of porosity on aneurysmal velocities. While decreasing the porosity clearly reduces the flow activity, there is generally a wide spread in the degree of reduction depending on the geometry of the aneurysm–parent vessel complex. Flow diverter-equivalent porosities can induce near-stagnant flows in sidewall and/or low flow aneurysms, while the flow reductions in bifurcation and/or high-flow patient geometries are comparatively less. Increasing the device pore density while maintaining a constant porosity also results in reduced intraneurysmal flow activity [7,15,4042]. Again, there is a wide variation in the results thus far and a very loose extrapolation from these studies on simplified geometries suggests that doubling the pore density (at constant porosity) can result in reduction of flow activity ranging anywhere between 20% and 150%.

It is unclear whether an “one-size-fits-all” porosity or pore density threshold exists that will result in successful occlusion of all aneurysms, but given that one-year aneurysm occlusion rates after flow diversion are reasonably high (∼90%), the porosities and pore densities of current commercial devices can be considered to be more or less in the correct range. Needless to mention, any theoretical considerations of minimizing porosity and/or maximizing pore density need to be balanced against practical considerations such as additional metal-to-artery burden resulting in in-stent thrombosis, increased device stiffness resulting in difficulties with navigating tortuous vessels, as well as coverage of side-branch ostia resulting in perforator occlusions. As shown in Fig. 1, the physiological pressure gradient across perforators jailed by flow diverters maintains the flow through these side branches, and the rate of perforator infarction is thus only about 3% [43]. This is also supported by the results from the flow studies that have evaluated the flow through side branches [7,18,4446]; at maximum, the flow diverters reduced the mean flow rate through the studied branches by 20%.

While the practice of packing flow diverter wires together at the aneurysm neck by longitudinal compression can theoretically improve the flow diversion effects, there are precautions that must be considered during device deployment. In general, a uniform distribution of the pore structure covering the aneurysm neck will provide the most favorable flow diversion behavior. Marked differences in the pore structure can occur [47] and cause flow to preferentially enter the aneurysm through device segments with looser, more open, pores and potentially lead to unfavorable results. The worst of these scenarios occurs when the devices are malapposed to the arterial wall at the aneurysm neck resulting in a “jet” type flow entry into the aneurysm from the gap between the device and the wall [10,40]. While more significant with stents [21], the flow diverters deployed across aneurysms at the outer curvature of vessels can have their pores spread open leading to increased flow activity within the aneurysm. A similar effect can occur when the devices bulge into the aneurysm during device packing, causing pore structures to open up and increase the intraneurysmal flow activity [48] and thus counteracting any beneficial effects of packing.

In order to further evaluate the effect of device pore structure on the intraneurysmal hemodynamics, we conducted computational fluid dynamics (CFD) analysis on five commercially available devices (U.S. and/or Europe) deployed in idealized sidewall aneurysm models. Identical physical silicone sidewall models were manufactured and the devices were deployed across the aneurysms in order to extract accurate pore structures by micro-CT imaging. The devices include FRED (MicroVention Terumo, Tustin, CA), Pipeline (Covidien Medtronic, Irvine, CA), Enterprise (Codman Neuro DePuy Synthes J&J, Raynham, MA), Neuroform (Boston Scientific Stryker, Fremont, CA), and LVIS (MicroVention Terumo, Tustin, CA).

CFD Model Construction.

Each device-implanted silicone model was imaged in a micro-CT scanner (μCT50 Scanco Medical, Bruttisellen, Switzerland) at a resolution of 12 μm; the FRED device was scanned at a resolution of 6 μm. mimics (Materialise, Leuven, Belgium) software was used to segment the micro-CT image slices to delineate the device wires/struts; only the portion of the device covering the neck of the aneurysm was retained (Fig. 4). The segmented images were thresholded into binary images (stent/background) and imported into matlab (Mathworks, Natick, MA) to extract wire centroids at each section. The centroids from all slices for each device were imported into solidworks (Dassault Systems, Waltham, MA) and, through a semi-automated procedure, 3D splines were fit onto the centroids to extract the wire centerlines. While the braided devices (LVIS, FRED, and Pipeline) have a circular wire cross section, the laser cut stents (Enterprise and Neuroform) have a rectangular wire cross section. To determine the cross-sectional dimensions of the Enterprise and Neuroform stents, the devices were embedded in epoxy, sectioned multiple times, and the cross-sectional strut width and thickness were measured on microscopy images of the sections. The FRED device is a dual mesh composed of larger diameter 16 wire outer braid (the LVIS device) and a thinner diameter 48 wire inner braid. Since the literature data on FRED were not available, the diameter of the inner wires was calibrated based on the known outer braid diameter. The device cross-sectional dimensions were also confirmed with the literature data [4,49,50] and are presented in Table 1. The table also lists the micro-CT derived porosity and pore density for each device. The CAD models for the sidewall aneurysm and the devices were imported into adina (ADINA R&D, Watertown, MA) and Boolean subtraction was performed to obtain the stented sidewall aneurysm lumen for CFD simulations.

Meshing and Boundary Conditions.

For all CFD models, meshing was performed using the meshing tools available within adina. The adina mesh utility is based on Delaunay triangulation. A free-form meshing scheme comprising of four-node tetrahedral elements was utilized. The meshing scheme was highly anisotropic in order to adequately resolve the stent strut/device wire faces while maintaining the overall mesh size parsimoniously such that it can be computationally solvable through available resources. The global mesh maximum edge size applied for all models was 0.25 mm that was determined as a good starting point based on previous mesh independence tests (data not shown). The stent struts for Enterprise and Neuroform devices and the wire faces for the LVIS were resolved through a maximum edge size of 0.012 mm. The mesh was approximately 2 × 106 elements for these three models. In case of Pipeline, the wire faces were resolved through an edge size of 0.012 mm resulting in a mesh of 3.5 × 106 elements (Fig. 5(a)). The FRED inner wire mesh faces were resolved through an average element edge size of 0.01 mm resulting in a mesh size approaching 4 × 106 elements (Fig. 5(b)).

At the inlet of each model, a population-averaged internal carotid artery pulsatile flow waveform obtained from the literature [51] was applied in the form of a fully developed Womersley flow profile [52]. The fluid was considered Newtonian and the working fluid viscosity was assumed to be 4 cP. No-slip boundary conditions were applied on the walls that were also assumed to be rigid and the flow was assumed to be laminar.

The CFD simulations were performed under the Galerkin finite element formulation in adina [53]. The CFD simulations within this formulation were validated against the analytical and experimental solutions. The CFD simulations were solved for conservation of mass and fluid momentum. An automated time-stepping scheme was utilized to assist in convergence of the simulations. The simulations were performed on an in-house 16-core Linux server (King Star supercomputer, Sunnyvale, CA) with 64 GB available RAM (one simulation was also performed on one node/16 cores (not parallelized) of the Blacklight PSC XSEDE national supercomputer). Postprocessing of the CFD simulation results was performed in adina and the exported nodal velocity vector data were analyzed in matlab.

CFD Results.

Spatial maps of velocity magnitude and velocity vectors in the midplane obtained by the CFD simulations for the control and the five devices are presented in Fig. 6. The flow patterns inside the aneurysm remain consistent before and after the deployment of LVIS, Neuroform, and Enterprise stents whereby a counterclockwise vortex develops inside the aneurysm after the acceleration phase of systole. The flow enters the aneurysm through the distal neck area in the control as well as Neuroform, Enterprise, and LVIS stents, and it persists through the rest of the cardiac cycle as can be seen in Figs. 6(a)6(d). Although the velocity profiles for the high-porosity devices (Neuroform, Enterprise, and LVIS) remained similar to that in the control, the velocity magnitude was attenuated following device implantation as is evident in Fig. 6.

Pipeline and FRED (Figs. 6(e) and 6(f)), which were designed as flow diverters, induce a reversal in the intra-aneurysmal flow direction before and after the implantation. In case of FRED, the flow inlet region changes from the distal neck to the proximal neck of the aneurysm (Fig. 6(e)). For Pipeline, despite the substantial reduction in velocity magnitude and absence of a vortex in the central region of the aneurysm, the flow still entered through the distal region of the neck after the acceleration phase of systole (Fig. 6(f)). Nonetheless, for both Pipeline and FRED, the large vortex seen in the control as well as in the highly porous stents was absent.

The differences in the intra-aneurysmal velocity were examined by evaluating the intra-aneurysmal kinetic energy. For all models, the kinetic energy was calculated from the nodal velocity vectors for the entire aneurysm. The instantaneous intra-aneurysmal kinetic energy through the cardiac cycle for each device (Fig. 7) shows successive reduction of the energy peak over the Neuroform, LVIS, Enterprise, FRED, and Pipeline devices, respectively. The kinetic energy waveforms were then time averaged to obtain the mean kinetic energy values for each device throughout the beat (Fig. 8(a)). The mean intra-aneurysmal kinetic energy follows a similar trend to that of the instantaneous kinetic energy.

To provide a clearer representation of the kinetic energy changes following device implantation, the percentage reduction in the mean intra-aneurysmal kinetic energy as compared to the control was calculated for all devices (Fig. 8(b)). The highest percent reduction in the intra-aneurysmal kinetic energy was induced by both Pipeline (97.6%) and FRED (97.67%). The kinetic energy was also reduced substantially by LVIS (84.01%), Enterprise (82.4%), and Neuroform (64.5%). Figure 9 shows the correlation between the device porosity and device pore density, and the reduction in the kinetic energy; it may be noted that nonlinear model fits to these data were not attempted because of the low sample size

Discussion.

To the knowledge of the authors, this is the first study performing a detailed computational analysis of the intra-aneurysmal hemodynamics on the micro-CT based geometric configurations of five commercial neurovascular devices, especially the comparison between the two flow diverter devices: Pipeline and FRED. As can be expected, the simulation results demonstrate that devices designed to act as flow diverters show better performance in terms of favorably modulating the intra-aneurysmal hemodynamics.

Although the CFD simulations demonstrated the kinetic energy reductions ranging from 65% to 82% for the high-porosity stent devices, such enhanced flow diversion effect can be attributed to the fact that the sidewall aneurysms (which are perpendicular to the oncoming flow) essentially produce a zero angle of attack to the oncoming parent vessel flow. Therefore, any interruption at the aneurysm neck will produce a marked effect on the flow inside the aneurysm. This trend was also seen in the previous experimental studies with simple spiral devices as well as flow diverter devices [7,15]. Similar to the previous studies mentioned above, the reduction in the flow activity after flow diversion seen in these results is >95%. Nonetheless, any differences in the flow diversion effect between the stent devices themselves would be helpful to the neurointerventionalist when selecting a stent device for stent-assisted coiling [54]. The CFD simulations showed that LVIS and Enterprise performed better in terms of their flow diversion effect as compared to the Neuroform (82% versus 65% reduction in kinetic energy). The open cell design of the Neuroform stent (as opposed to Enterprise which is of closed cell design) results in providing least coverage over the aneurysm neck and least resistance to flow. Additional studies evaluating these stents in various aneurysm geometries would be required to conclude the superiority of, or preference for, one stent over the other for different types of aneurysms.

The extensive computational expense involved in constructing the CFD model and running the simulations limited the sample size to one deployment per device. The relatively (relative to device wire dimension) larger elements used in this study were chosen to keep the mesh size computationally feasible given the computational resources available to us. In general, the time-consuming and laborious nature of model construction makes the CFD or fluid structure interaction (FSI) simulations difficult for the large number of samples required for statistical comparisons. To reduce the time involved in generating solid models and improve the process efficiency, we are currently developing an image to mesh technique (I2M) to obtain finite element grids directly from segmented medical images [5557]. One of the drawbacks of using the meshing tools in the commercial package adina, for example, is that it is restricted to working with Delaunay triangulation which introduces slivers in the finite element meshes when discretizing complex geometries involving a huge transition in edge sizes. The I2M technique departs from the free-form Delaunay triangulation algorithms and offers a body-centered cubic lattice approach-based structured grid, which can generate highly anisotropic meshes with a smooth transition in size of the elements to avoid any simulation instabilities.

As in many other in silico studies, a literature-derived inflow waveform was used as the inlet boundary condition for the model used here. The CFD simulations of devices placed in patient-derived geometries (as shown in Fig. 10) will preferably incorporate the pressure and flow waveforms obtained in the same subject. Other conditions that can help improve the accuracy of the calculated results include fluid–structure interactions, which incorporate the compliance of the blood vessels in addition to the resistance of the distal capillary bed.

As the computer power increases, so does the spatiotemporal resolution of computational simulations. Nevertheless, most CFD studies seem to be content with reporting the qualitative results in the form of color or vector maps rather than reduce a tremendous amount of information produced by the simulations to quantifiable indices that can serve as discriminants for treatment planning or predicting the treatment outcomes. Another pitfall in using such simulations is the lack of critical validation of many CFD studies against the experimental data [5862]. Unless validated, the simulations should be viewed with caution particularly if attempts are made to apply them to clinical decision making or for device evaluation. We are currently acquiring the experimental data in physical models to validate these CFD results.

The primary goal of the studies that measure the intraneurysmal flow alterations is to derive the flow-related parameters that can predict the long-term aneurysm occlusion after flow diversion. If successful, such studies could facilitate optimal device selection prior to treatment, and companion clinical angiographic studies could help guide the treatment by assessing the device efficacy in near-real time during the procedure. For example, Fig. 10 shows the device-induced flow alterations in two right carotid aneurysms; the aneurysm with the 99% kinetic energy (KE) reduction was completely occluded at 6 months follow-up, while the one with 52% KE reduction still had residual filling at 6 months follow-up. The sample sizes are, again, sparse but both in vivo [22,23,32,63] and clinical studies [9,10,24,6466] have noted that the reduction in the intraneurysmal flow activity (based on whichever variable the study chose to quantify) after flow diversion can be significantly different between aneurysms that remained patent at follow-up versus those that occluded at follow-up. Unfortunately, each research group chooses, even prefers, to define their own variable to describe the changes in the flow activity. Until some standard variables are agreed upon and evaluated by all participants on their patient datasets, it may be difficult to establish the index (indices) that is (are) able to predict the long-term aneurysm occlusion after flow diversion. Such standardization will be required of both angiographic analysis and numerical simulations in order to establish a predictive index. The data are, however, being collected; for example, an aggregate of 94 patients (59 angiograms [65,66] and 35 numerical simulations [10,24]) suggests a reasonably high predictive value of chosen indices (mean aneurysm inflow rate, mean aneurysm velocity, space-and-time average velocity, inflow and outflow slopes of contrast concentration-time curves, and angiographic mean aneurysm flow amplitude), with specificity and sensitivity of around 75–90%.

While these results are promising, whether or not a purely flow-derived parameter, which is based on physics alone, will be able to predict aneurysm occlusion without the consideration of biological/biochemical parameters is unclear at this point. The incorporation of biochemical factors into flow studies is in the incipient stages [67,68].

Dullien, F. A. L. , and Batra, V. K. , 1970, “ Determination of the Structure of Porous Media,” Ind. Eng. Chem., 62(10), pp. 25–53. [CrossRef]
Koponen, A. , Kataja, M. , and Timonen, J. , 1997, “ Permeability and Effective Porosity of Porous Media,” Phys. Rev. E, 56(3), pp. 3319–3325. [CrossRef]
Neithalath, N. , Sumanasooriya, M. S. , and Deo, O. , 2010, “ Characterizing Pore Volume, Sizes, and Connectivity in Pervious Concretes for Permeability Prediction,” Mater. Charact., 61(8), pp. 802–813. [CrossRef]
Jou, L. D. , Chintalapani, G. , and Mawad, M. E. , 2016, “ Metal Coverage Ratio of Pipeline Embolization Device for Treatment of Unruptured Aneurysms: Reality Check,” Interventional Neuroradiology, 22(1), pp. 42–48. [CrossRef] [PubMed]
Aenis, M. , Stancampiano, A. P. , Wakhloo, A. K. , and Lieber, B. B. , 1997, “ Modeling of Flow in a Straight Stented and Nonstented Side Wall Aneurysm Model,” ASME J. Biomech. Eng., 119(2), pp. 206–212. [CrossRef]
Lieber, B. B. , Stancampiano, A. P. , and Wakhloo, A. K. , 1997, “ Alteration of Hemodynamics in Aneurysm Models by Stenting: Influence of Stent Porosity,” Ann. Biomed. Eng., 25(3), pp. 460–469. [CrossRef] [PubMed]
Seong, J. , Wakhloo, A. K. , and Lieber, B. B. , 2007, “ In Vitro Evaluation of Flow Divertors in an Elastase-Induced Saccular Aneurysm Model in Rabbit,” ASME J. Biomech. Eng., 129(6), pp. 863–872. [CrossRef]
Stuhne, G. R. , and Steinman, D. A. , 2004, “ Finite-Element Modeling of the Hemodynamics of Stented Aneurysms,” ASME J. Biomech. Eng., 126(3), pp. 382–387. [CrossRef]
Kulcsar, Z. , Augsburger, L. , Reymond, P. , Pereira, V. M. , Hirsch, S. , Mallik, A. S. , Millar, J. , Wetzel, S. G. , Wanke, I. , and Rufenacht, D. A. , 2012, “ Flow Diversion Treatment: Intra-Aneurismal Blood Flow Velocity and WSS Reduction are Parameters to Predict Aneurysm Thrombosis,” Acta Neurochir., 154(10), pp. 1827–1834. [CrossRef]
Mut, F. , Raschi, M. , Scrivano, E. , Bleise, C. , Chudyk, J. , Ceratto, R. , Lylyk, P. , and Cebral, J. R. , 2015, “ Association Between Hemodynamic Conditions and Occlusion Times After Flow Diversion in Cerebral Aneurysms,” J. Neurointerventional Surg., 7(4), pp. 286–290. [CrossRef]
Larrabide, I. , Geers, A. J. , Morales, H. G. , Aguilar, M. L. , and Rufenacht, D. A. , 2015, “ Effect of Aneurysm and ICA Morphology on Hemodynamics Before and After Flow Diverter Treatment,” J. Neurointerventional Surg., 7(4), pp. 272–280. [CrossRef]
Rhee, K. , Han, M. H. , and Cha, S. H. , 2002, “ Changes of Flow Characteristics by Stenting in Aneurysm Models: Influence of Aneurysm Geometry and Stent Porosity,” Ann. Biomed. Eng., 30(7), pp. 894–904. [CrossRef] [PubMed]
Liou, T. M. , Liou, S. N. , and Chu, K. L. , 2004, “ Intra-Aneurysmal Flow With Helix and Mesh Stent Placement Across Side-Wall Aneurysm Pore of a Straight Parent Vessel,” ASME J. Biomech. Eng., 126(1), pp. 36–43. [CrossRef]
Sadasivan, C. , Fiorella, D. J. , Woo, H. H. , and Lieber, B. B. , 2013, “ Physical Factors Effecting Cerebral Aneurysm Pathophysiology,” Ann. Biomed. Eng., 41(7), pp. 1347–1365. [CrossRef] [PubMed]
Lieber, B. B. , Livescu, V. , Hopkins, L. N. , and Wakhloo, A. K. , 2002, “ Particle Image Velocimetry Assessment of Stent Design Influence on Intra-Aneurysmal Flow,” Ann. Biomed. Eng., 30(6), pp. 768–777. [CrossRef] [PubMed]
Yu, S. C. , and Zhao, J. B. , 1999, “ A Steady Flow Analysis on the Stented and Non-Stented Sidewall Aneurysm Models,” Med. Eng. Phys., 21(3), pp. 133–141. [CrossRef] [PubMed]
Seshadhri, S. , Janiga, G. , Beuing, O. , Skalej, M. , and Thevenin, D. , 2011, “ Impact of Stents and Flow Diverters on Hemodynamics in Idealized Aneurysm Models,” ASME J. Biomech. Eng., 133(7), p. 071005. [CrossRef]
Trager, A. L. , Sadasivan, C. , and Lieber, B. B. , 2012, “ Comparison of the In Vitro Hemodynamic Performance of New Flow Diverters for Bypass of Brain Aneurysms,” ASME J. Biomech. Eng., 134(8), p. 084505. [CrossRef]
Bouillot, P. , Brina, O. , Ouared, R. , Yilmaz, H. , Lovblad, K. O. , Farhat, M. , and Mendes Pereira, V. , 2016, “ Computational Fluid Dynamics With Stents: Quantitative Comparison With Particle Image Velocimetry for Three Commercial Off the Shelf Intracranial Stents,” J. Neurointerventional Surg., 8(3), pp. 309–315. [CrossRef]
Dennis, K. D. , Rossman, T. L. , Kallmes, D. F. , and Dragomir-Daescu, D. , 2015, “ Intra-Aneurysmal Flow Rates Are Reduced by Two Flow Diverters: An Experiment Using Tomographic Particle Image Velocimetry in an Aneurysm Model,” J. Neurointerventional Surg., 7(12), pp. 937–942. [CrossRef]
Roszelle, B. N. , Gonzalez, L. F. , Babiker, M. H. , Ryan, J. , Albuquerque, F. C. , and Frakes, D. H. , 2013, “ Flow Diverter Effect on Cerebral Aneurysm Hemodynamics: An In Vitro Comparison of Telescoping Stents and the Pipeline,” Neuroradiology, 55(6), pp. 751–758. [CrossRef] [PubMed]
Cebral, J. R. , Mut, F. , Raschi, M. , Hodis, S. , Ding, Y. H. , Erickson, B. J. , Kadirvel, R. , and Kallmes, D. F. , 2014, “ Analysis of Hemodynamics and Aneurysm Occlusion After Flow-Diverting Treatment in Rabbit Models,” AJNR, 35(8), pp. 1567–1573. [CrossRef] [PubMed]
Huang, Q. , Xu, J. , Cheng, J. , Wang, S. , Wang, K. , and Liu, J. M. , 2013, “ Hemodynamic Changes by Flow Diverters in Rabbit Aneurysm Models: A Computational Fluid Dynamic Study Based on Micro-Computed Tomography Reconstruction,” Stroke, 44(7), pp. 1936–1941. [CrossRef] [PubMed]
Ouared, R. , Larrabide, I. , Brina, O. , Bouillot, P. , Erceg, G. , Yilmaz, H. , Lovblad, K. O. , and Mendes Pereira, V. , “ Computational Fluid Dynamics Analysis of Flow Reduction Induced by Flow-Diverting Stents in Intracranial Aneurysms: A Patient-Unspecific Hemodynamics Change Perspective,” J. Neurointerventional Surg., epub.
Jing, L. , Zhong, J. , Liu, J. , Yang, X. , Paliwal, N. , Meng, H. , Wang, S. , and Zhang, Y. , 2016, “ Hemodynamic Effect of Flow Diverter and Coils in Treatment of Large and Giant Intracranial Aneurysms,” World Neurosurg., 89, pp. 199–207. [CrossRef] [PubMed]
Karmonik, C. , Chintalapani, G. , Redel, T. , Zhang, Y. J. , Diaz, O. , Klucznik, R. , and Grossman, R. G. , 2013, “ Hemodynamics at the Ostium of Cerebral Aneurysms With Relation to Post-Treatment Changes by a Virtual Flow Diverter: A Computational Fluid Dynamics Study,” 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 3–7, pp. 1895–1898.
Tsang, A. C. , Lai, S. S. , Chung, W. C. , Tang, A. Y. , Leung, G. K. , Poon, A. K. , Yu, A. C. , and Chow, K. W. , 2015, “ Blood Flow in Intracranial Aneurysms Treated With Pipeline Embolization Devices: Computational Simulation and Verification With Doppler Ultrasonography on Phantom Models,” Ultrasonography, 34(2), pp. 98–108. [CrossRef] [PubMed]
Kojima, M. , Irie, K. , Fukuda, T. , Arai, F. , Hirose, Y. , and Negoro, M. , 2012, “ The Study of Flow Diversion Effects on Aneurysm Using Multiple Enterprise Stents and Two Flow Diverters,” Asian J. Neurosurg., 7(4), pp. 159–165. [CrossRef] [PubMed]
Janiga, G. , Daroczy, L. , Berg, P. , Thevenin, D. , Skalej, M. , and Beuing, O. , 2015, “ An Automatic CFD-Based Flow Diverter Optimization Principle for Patient-Specific Intracranial Aneurysms,” J. Biomech., 48(14), pp. 3846–3852. [CrossRef] [PubMed]
Shobayashi, Y. , Tateshima, S. , Kakizaki, R. , Sudo, R. , Tanishita, K. , and Vinuela, F. , 2013, “ Intra-Aneurysmal Hemodynamic Alterations by a Self-Expandable Intracranial Stent and Flow Diversion Stent: High Intra-Aneurysmal Pressure Remains Regardless of Flow Velocity Reduction,” J. Neurointerventional Surg., 5(Suppl. 3), pp. iii38–iii42. [CrossRef]
Augsburger, L. , Reymond, P. , Rufenacht, D. A. , and Stergiopulos, N. , 2011, “ Intracranial Stents Being Modeled as a Porous Medium: Flow Simulation in Stented Cerebral Aneurysms,” Ann. Biomed. Eng., 39(2), pp. 850–863. [CrossRef] [PubMed]
Sadasivan, C. , Cesar, L. , Seong, J. , Wakhloo, A. K. , and Lieber, B. B. , 2009, “ Treatment of Rabbit Elastase-Induced Aneurysm Models by Flow Diverters: Development of Quantifiable Indexes of Device Performance Using Digital Subtraction Angiography,” IEEE Trans. Med. Imaging, 28(7), pp. 1117–1125. [CrossRef] [PubMed]
Grunwald, I. Q. , Kamran, M. , Corkill, R. A. , Kuhn, A. L. , Choi, I. S. , Turnbull, S. , Dobson, D. , Fassbender, K. , Watson, D. , and Gounis, M. J. , 2012, “ Simple Measurement of Aneurysm Residual After Treatment: The SMART Scale for Evaluation of Intracranial Aneurysms Treated With Flow Diverters,” Acta Neurochir., 154(1), pp. 21–26; Discussion 26. [CrossRef]
Joshi, M. D. , O'Kelly, C. J. , Krings, T. , Fiorella, D. , and Marotta, T. R. , 2013, “ Observer Variability of an Angiographic Grading Scale Used for the Assessment of Intracranial Aneurysms Treated With Flow-Diverting Stents,” AJNR, 34(8), pp. 1589–1592. [CrossRef] [PubMed]
Struffert, T. , Ott, S. , Kowarschik, M. , Bender, F. , Adamek, E. , Engelhorn, T. , Golitz, P. , Lang, S. , Strother, C. M. , and Doerfler, A. , 2013, “ Measurement of Quantifiable Parameters by Time-Density Curves in the Elastase-Induced Aneurysm Model: First Results in the Comparison of a Flow Diverter and a Conventional Aneurysm Stent,” Eur. Radiol., 23(2), pp. 521–527. [CrossRef] [PubMed]
Cho, Y. I. , and Kensey, K. R. , 1991, “ Effects of the Non-Newtonian Viscosity of Blood on Flows in a Diseased Arterial Vessel. Part 1: Steady Flows,” Biorheology, 28(3–4), pp. 241–262. https://www.researchgate.net/profile/Young_Cho5/publication/21222708_Effects_of_the_non-Newtonian_viscosity_of_blood_flows_in_a_diseased_arterial_vessel_Part_I_steady_flows/links/541ed8b90cf203f155c247b6.pdf [PubMed]
Ohta, M. , Wetzel, S. G. , Dantan, P. , Bachelet, C. , Lovblad, K. O. , Yilmaz, H. , Flaud, P. , and Rufenacht, D. A. , 2005, “ Rheological Changes After Stenting of a Cerebral Aneurysm: A Finite Element Modeling Approach,” Cardiovasc. Interventional Radiol., 28(6), pp. 768–772. [CrossRef]
Tateshima, S. , Jones, J. G. , Mayor Basto, F. , Vinuela, F. , and Duckwiler, G. R. , 2014, “ Aneurysm Pressure Measurement Before and After Placement of a Pipeline Stent: Feasibility Study Using a 0.014 Inch Pressure Wire for Coronary Intervention,” J. Neurointerventional Surg., 8(6), pp. 603–607. [CrossRef]
Augsburger, L. , Farhat, M. , Reymond, P. , Fonck, E. , Kulcsar, Z. , Stergiopulos, N. , and Rufenacht, D. A. , 2009, “ Effect of Flow Diverter Porosity on Intraaneurysmal Blood Flow,” Klin. Neuroradiologie, 19(3), pp. 204–214. [CrossRef]
Rayepalli, S. , Gupta, R. , Lum, C. , Majid, A. , and Koochesfahani, M. , 2013, “ The Impact of Stent Strut Porosity on Reducing Flow in Cerebral Aneurysms,” J. Neuroimaging, 23(4), pp. 495–501. [CrossRef] [PubMed]
Yu, C. H. , and Kwon, T. K. , 2014, “ Study of Parameters for Evaluating Flow Reduction With Stents in a Sidewall Aneurysm Phantom Model,” Biomed. Mater. Eng., 24(6), pp. 2417–2424. [PubMed]
Lee, C. J. , Srinivas, K. , and Qian, Y. , 2014, “ Three-Dimensional Hemodynamic Design Optimization of Stents for Cerebral Aneurysms,” Proc. Inst. Mech. Eng., Part H, 228(3), pp. 213–224. [CrossRef]
Brinjikji, W. , Murad, M. H. , Lanzino, G. , Cloft, H. J. , and Kallmes, D. F. , 2013, “ Endovascular Treatment of Intracranial Aneurysms With Flow Diverters: A Meta-Analysis,” Stroke, 44(2), pp. 442–447. [CrossRef] [PubMed]
Cebral, J. R. , Raschi, M. , Mut, F. , Ding, Y. H. , Dai, D. , Kadirvel, R. , and Kallmes, D. , 2014, “ Analysis of Flow Changes in Side Branches Jailed by Flow Diverters in Rabbit Models,” Int. J. Numer. Methods Biomed. Eng., 30(10), pp. 988–999. [CrossRef]
Hu, P. , Qian, Y. , Zhang, Y. , Zhang, H. Q. , Li, Y. , Chong, W. , and Ling, F. , 2015, “ Blood Flow Reduction of Covered Small Side Branches After Flow Diverter Treatment: A Computational Fluid Hemodynamic Quantitative Analysis,” J. Biomech., 48(6), pp. 895–898. [CrossRef] [PubMed]
Tang, A. Y. , Chung, W. C. , Liu, E. T. , Qu, J. Q. , Tsang, A. C. , Leung, G. K. , Leung, K. M. , Yu, A. C. , and Chow, K. W. , 2015, “ Computational Fluid Dynamics Study of Bifurcation Aneurysms Treated With Pipeline Embolization Device: Side Branch Diameter Study,” J. Med. Biol. Eng., 35(3), pp. 293–304. [CrossRef] [PubMed]
Makoyeva, A. , Bing, F. , Darsaut, T. E. , Salazkin, I. , and Raymond, J. , 2013, “ The Varying Porosity of Braided Self-Expanding Stents and Flow Diverters: An Experimental Study,” AJNR, 34(3), pp. 596–602. [CrossRef] [PubMed]
Karunanithi, K. , Lee, C. J. , Chong, W. , and Qian, Y. , 2015, “ The Influence of Flow Diverter's Angle of Curvature Across the Aneurysm Neck on Its Haemodynamics,” Proc. Inst. Mech. Eng., Part H, 229(8), pp. 560–569. [CrossRef]
Patel, N. V. , Gounis, M. J. , Wakhloo, A. K. , Noordhoek, N. , Blijd, J. , Babic, D. , Takhtani, D. , Lee, S. K. , and Norbash, A. , 2011, “ Contrast-Enhanced Angiographic Cone-Beam CT of Cerebrovascular Stents: Experimental Optimization and Clinical Application,” AJNR, 32(1), pp. 137–144. [PubMed]
Raymond, J. , Darsaut, T. E. , Bing, F. , Makoyeva, A. , Kotowski, M. , Gevry, G. , and Salazkin, I. , 2013, “ Stent-Assisted Coiling of Bifurcation Aneurysms May Improve Endovascular Treatment: A Critical Evaluation in an Experimental Model,” AJNR, 34(3), pp. 570–576. [CrossRef] [PubMed]
Gwilliam, M. N. , Hoggard, N. , Capener, D. , Singh, P. , Marzo, A. , Verma, P. K. , and Wilkinson, I. D. , 2009, “ MR Derived Volumetric Flow Rate Waveforms at Locations Within the Common Carotid, Internal Carotid, and Basilar Arteries,” J. Cereb. Blood Flow Metab., 29(12), pp. 1975–1982. [CrossRef] [PubMed]
Womersley, J. R. , 1955, “ Method for the Calculation of Velocity, Rate of Flow and Viscous Drag in Arteries When the Pressure Gradient is Known,” J. Physiol., 127(3), pp. 553–563. [CrossRef] [PubMed]
Bathe, K.-J. , and Bathe, K.-J. , 1996, Finite Element Procedures, Prentice Hall, Englewood Cliffs, NJ.
Fiorella, D. , Arthur, A. , Boulos, A. , Diaz, O. , Jabbour, P. , Pride, L. , Turk, A. S. , Woo, H. H. , Derdeyn, C. , Millar, J. , and Clifton, A. , 2016, “ Final Results of the U.S. Humanitarian Device Exemption Study of the Low-Profile Visualized Intraluminal Support (LVIS) Device,” J. Neurointerventional Surg., 8(9), pp. 894–897. [CrossRef]
Dholakia, R. , Drakopoulos, F. , Sadasivan, C. , Jiao, X. , Fiorella, D. J. , Woo, H. H. , Lieber, B. B. , and Chrisochoides, N. , 2015, “ High Fidelity Image-to-Mesh Conversion for Brain Aneurysm/Stent Geometries,” IEEE International Symposium on Biomedical Imaging. https://crtc.cs.odu.edu/pub/papers/conf_153.pdf
Foteinos, P. , and Chrisochoides, N. , 2013, “ High Quality Real-Time Image-to-Mesh Conversion for Finite Element Simulations,” 27th ACM International Conference on Supercomputing (ICS'13), pp. 233–242.
Foteinos, P. , and Chrisochoides, N. , 2014, “ High Quality Real-Time Image-to-Mesh Conversion for Finite Element Simulations,” J. Parallel Distrib. Comput., 74(2), pp. 2123–2140. [CrossRef]
Stewart, S. C. , Paterson, E. , Burgreen, G. , Hariharan, P. , Giarra, M. , Reddy, V. , Day, S. , Manning, K. , Deutsch, S. , Berman, M. , Myers, M. , and Malinauskas, R. , 2012, “ Assessment of CFD Performance in Simulations of an Idealized Medical Device: Results of FDA's First Computational Interlaboratory Study,” Cardiovasc. Eng. Technol., 3(2), pp. 139–160. [CrossRef]
Hariharan, P. , D'Souza, G. , Horner, M. , Malinauskas, R. A. , and Myers, M. R. , 2015, “ Verification Benchmarks to Assess the Implementation of Computational Fluid Dynamics Based Hemolysis Prediction Models,” ASME J. Biomech. Eng., 137(9), p. 094501. [CrossRef]
Trias, M. , Arbona, A. , Masso, J. , Minano, B. , and Bona, C. , 2014, “ FDA's Nozzle Numerical Simulation Challenge: Non-Newtonian Fluid Effects and Blood Damage,” PLoS One, 9(3), p. e92638. [CrossRef] [PubMed]
Steinman, D. A. , Hoi, Y. , Fahy, P. , Morris, L. , Walsh, M. T. , Aristokleous, N. , Anayiotos, A. S. , Papaharilaou, Y. , Arzani, A. , Shadden, S. C. , Berg, P. , Janiga, G. , Bols, J. , Segers, P. , Bressloff, N. W. , Cibis, M. , Gijsen, F. H. , Cito, S. , Pallares, J. , Browne, L. D. , Costelloe, J. A. , Lynch, A. G. , Degroote, J. , Vierendeels, J. , Fu, W. , Qiao, A. , Hodis, S. , Kallmes, D. F. , Kalsi, H. , Long, Q. , Kheyfets, V. O. , Finol, E. A. , Kono, K. , Malek, A. M. , Lauric, A. , Menon, P. G. , Pekkan, K. , Esmaily Moghadam, M. , Marsden, A. L. , Oshima, M. , Katagiri, K. , Peiffer, V. , Mohamied, Y. , Sherwin, S. J. , Schaller, J. , Goubergrits, L. , Usera, G. , Mendina, M. , Valen-Sendstad, K. , Habets, D. F. , Xiang, J. , Meng, H. , Yu, Y. , Karniadakis, G. E. , Shaffer, N. , and Loth, F. , 2013, “ Variability of Computational Fluid Dynamics Solutions for Pressure and Flow in a Giant Aneurysm: The ASME 2012 Summer Bioengineering Conference CFD Challenge,” ASME J. Biomech. Eng., 135(2), p. 021016.
Berg, P. , Roloff, C. , Beuing, O. , Voss, S. , Sugiyama, S. I. , Aristokleous, N. , Anayiotos, A. S. , Ashton, N. , Revell, A. , Bressloff, N. W. , Brown, A. G. , Chung, B. J. , Cebral, J. R. , Copelli, G. , Fu, W. , Qiao, A. , Geers, A. J. , Hodis, S. , Dragomir-Daescu, D. , Nordahl, E. , Suzen, Y. B. , Khan, M. O. , Valen-Sendstad, K. , Kono, K. , Menon, P. G. , Albal, P. G. , Mierka, O. , Munster, R. , Morales, H. G. , Bonnefous, O. , Osman, J. , Goubergrits, L. , Pallares, J. , Cito, S. , Passalacqua, A. , Piskin, S. , Pekkan, K. , Ramalho, S. , Marques, N. , Sanchi, S. , Schumacher, K. R. , Sturgeon, J. , Svihlova, H. , Hron, J. , Usera, G. , Mendina, M. , Xiang, J. , Meng, H. , Steinman, D. A. , and Janiga, G. , 2015, “ The Computational Fluid Dynamics Rupture Challenge 2013—Phase II: Variability of Hemodynamic Simulations in Two Intracranial Aneurysms,” ASME J. Biomech. Eng., 137(12), p. 121008. [CrossRef]
Chung, B. , Mut, F. , Kadirvel, R. , Lingineni, R. , Kallmes, D. F. , and Cebral, J. R. , 2015, “ Hemodynamic Analysis of Fast and Slow Aneurysm Occlusions by Flow Diversion in Rabbits,” J. Neurointerventional Surg., 7(12), pp. 931–935. [CrossRef]
Chong, W. , Zhang, Y. , Qian, Y. , Lai, L. , Parker, G. , and Mitchell, K. , 2014, “ Computational Hemodynamics Analysis of Intracranial Aneurysms Treated With Flow Diverters: Correlation With Clinical Outcomes,” AJNR, 35(1), pp. 136–142. [CrossRef] [PubMed]
Pereira, V. M. , Bonnefous, O. , Ouared, R. , Brina, O. , Stawiaski, J. , Aerts, H. , Ruijters, D. , Narata, A. P. , Bijlenga, P. , Schaller, K. , and Lovblad, K. O. , 2013, “ A DSA-Based Method Using Contrast-Motion Estimation for the Assessment of the Intra-Aneurysmal Flow Changes Induced by Flow-Diverter Stents,” AJNR, 34(4), pp. 808–815. [CrossRef] [PubMed]
Golitz, P. , Struffert, T. , Rosch, J. , Ganslandt, O. , Knossalla, F. , and Doerfler, A. , 2015, “ Cerebral Aneurysm Treatment Using Flow-Diverting Stents: In-Vivo Visualization of Flow Alterations by Parametric Colour Coding to Predict Aneurysmal Occlusion: Preliminary Results,” Eur. Radiol., 25(2), pp. 428–435. [CrossRef] [PubMed]
Malaspinas, O. , Turjman, A. , Ribeiro de Sousa, D. , Garcia-Cardena, G. , Raes, M. , Nguyen, P. T. , Zhang, Y. , Courbebaisse, G. , Lelubre, C. , Zouaoui Boudjeltia, K. , and Chopard, B. , 2016, “ A Spatio-Temporal Model for Spontaneous Thrombus Formation in Cerebral Aneurysms,” J. Theor. Biol., 394, pp. 68–76. [CrossRef] [PubMed]
Peach, T. W. , Ngoepe, M. , Spranger, K. , Zajarias-Fainsod, D. , and Ventikos, Y. , 2014, “ Personalizing Flow-Diverter Intervention for Cerebral Aneurysms: From Computational Hemodynamics to Biochemical Modeling,” Int. J. Numer. Methods Biomed. Eng., 30(11), pp. 1387–1407. [CrossRef]
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References

Dullien, F. A. L. , and Batra, V. K. , 1970, “ Determination of the Structure of Porous Media,” Ind. Eng. Chem., 62(10), pp. 25–53. [CrossRef]
Koponen, A. , Kataja, M. , and Timonen, J. , 1997, “ Permeability and Effective Porosity of Porous Media,” Phys. Rev. E, 56(3), pp. 3319–3325. [CrossRef]
Neithalath, N. , Sumanasooriya, M. S. , and Deo, O. , 2010, “ Characterizing Pore Volume, Sizes, and Connectivity in Pervious Concretes for Permeability Prediction,” Mater. Charact., 61(8), pp. 802–813. [CrossRef]
Jou, L. D. , Chintalapani, G. , and Mawad, M. E. , 2016, “ Metal Coverage Ratio of Pipeline Embolization Device for Treatment of Unruptured Aneurysms: Reality Check,” Interventional Neuroradiology, 22(1), pp. 42–48. [CrossRef] [PubMed]
Aenis, M. , Stancampiano, A. P. , Wakhloo, A. K. , and Lieber, B. B. , 1997, “ Modeling of Flow in a Straight Stented and Nonstented Side Wall Aneurysm Model,” ASME J. Biomech. Eng., 119(2), pp. 206–212. [CrossRef]
Lieber, B. B. , Stancampiano, A. P. , and Wakhloo, A. K. , 1997, “ Alteration of Hemodynamics in Aneurysm Models by Stenting: Influence of Stent Porosity,” Ann. Biomed. Eng., 25(3), pp. 460–469. [CrossRef] [PubMed]
Seong, J. , Wakhloo, A. K. , and Lieber, B. B. , 2007, “ In Vitro Evaluation of Flow Divertors in an Elastase-Induced Saccular Aneurysm Model in Rabbit,” ASME J. Biomech. Eng., 129(6), pp. 863–872. [CrossRef]
Stuhne, G. R. , and Steinman, D. A. , 2004, “ Finite-Element Modeling of the Hemodynamics of Stented Aneurysms,” ASME J. Biomech. Eng., 126(3), pp. 382–387. [CrossRef]
Kulcsar, Z. , Augsburger, L. , Reymond, P. , Pereira, V. M. , Hirsch, S. , Mallik, A. S. , Millar, J. , Wetzel, S. G. , Wanke, I. , and Rufenacht, D. A. , 2012, “ Flow Diversion Treatment: Intra-Aneurismal Blood Flow Velocity and WSS Reduction are Parameters to Predict Aneurysm Thrombosis,” Acta Neurochir., 154(10), pp. 1827–1834. [CrossRef]
Mut, F. , Raschi, M. , Scrivano, E. , Bleise, C. , Chudyk, J. , Ceratto, R. , Lylyk, P. , and Cebral, J. R. , 2015, “ Association Between Hemodynamic Conditions and Occlusion Times After Flow Diversion in Cerebral Aneurysms,” J. Neurointerventional Surg., 7(4), pp. 286–290. [CrossRef]
Larrabide, I. , Geers, A. J. , Morales, H. G. , Aguilar, M. L. , and Rufenacht, D. A. , 2015, “ Effect of Aneurysm and ICA Morphology on Hemodynamics Before and After Flow Diverter Treatment,” J. Neurointerventional Surg., 7(4), pp. 272–280. [CrossRef]
Rhee, K. , Han, M. H. , and Cha, S. H. , 2002, “ Changes of Flow Characteristics by Stenting in Aneurysm Models: Influence of Aneurysm Geometry and Stent Porosity,” Ann. Biomed. Eng., 30(7), pp. 894–904. [CrossRef] [PubMed]
Liou, T. M. , Liou, S. N. , and Chu, K. L. , 2004, “ Intra-Aneurysmal Flow With Helix and Mesh Stent Placement Across Side-Wall Aneurysm Pore of a Straight Parent Vessel,” ASME J. Biomech. Eng., 126(1), pp. 36–43. [CrossRef]
Sadasivan, C. , Fiorella, D. J. , Woo, H. H. , and Lieber, B. B. , 2013, “ Physical Factors Effecting Cerebral Aneurysm Pathophysiology,” Ann. Biomed. Eng., 41(7), pp. 1347–1365. [CrossRef] [PubMed]
Lieber, B. B. , Livescu, V. , Hopkins, L. N. , and Wakhloo, A. K. , 2002, “ Particle Image Velocimetry Assessment of Stent Design Influence on Intra-Aneurysmal Flow,” Ann. Biomed. Eng., 30(6), pp. 768–777. [CrossRef] [PubMed]
Yu, S. C. , and Zhao, J. B. , 1999, “ A Steady Flow Analysis on the Stented and Non-Stented Sidewall Aneurysm Models,” Med. Eng. Phys., 21(3), pp. 133–141. [CrossRef] [PubMed]
Seshadhri, S. , Janiga, G. , Beuing, O. , Skalej, M. , and Thevenin, D. , 2011, “ Impact of Stents and Flow Diverters on Hemodynamics in Idealized Aneurysm Models,” ASME J. Biomech. Eng., 133(7), p. 071005. [CrossRef]
Trager, A. L. , Sadasivan, C. , and Lieber, B. B. , 2012, “ Comparison of the In Vitro Hemodynamic Performance of New Flow Diverters for Bypass of Brain Aneurysms,” ASME J. Biomech. Eng., 134(8), p. 084505. [CrossRef]
Bouillot, P. , Brina, O. , Ouared, R. , Yilmaz, H. , Lovblad, K. O. , Farhat, M. , and Mendes Pereira, V. , 2016, “ Computational Fluid Dynamics With Stents: Quantitative Comparison With Particle Image Velocimetry for Three Commercial Off the Shelf Intracranial Stents,” J. Neurointerventional Surg., 8(3), pp. 309–315. [CrossRef]
Dennis, K. D. , Rossman, T. L. , Kallmes, D. F. , and Dragomir-Daescu, D. , 2015, “ Intra-Aneurysmal Flow Rates Are Reduced by Two Flow Diverters: An Experiment Using Tomographic Particle Image Velocimetry in an Aneurysm Model,” J. Neurointerventional Surg., 7(12), pp. 937–942. [CrossRef]
Roszelle, B. N. , Gonzalez, L. F. , Babiker, M. H. , Ryan, J. , Albuquerque, F. C. , and Frakes, D. H. , 2013, “ Flow Diverter Effect on Cerebral Aneurysm Hemodynamics: An In Vitro Comparison of Telescoping Stents and the Pipeline,” Neuroradiology, 55(6), pp. 751–758. [CrossRef] [PubMed]
Cebral, J. R. , Mut, F. , Raschi, M. , Hodis, S. , Ding, Y. H. , Erickson, B. J. , Kadirvel, R. , and Kallmes, D. F. , 2014, “ Analysis of Hemodynamics and Aneurysm Occlusion After Flow-Diverting Treatment in Rabbit Models,” AJNR, 35(8), pp. 1567–1573. [CrossRef] [PubMed]
Huang, Q. , Xu, J. , Cheng, J. , Wang, S. , Wang, K. , and Liu, J. M. , 2013, “ Hemodynamic Changes by Flow Diverters in Rabbit Aneurysm Models: A Computational Fluid Dynamic Study Based on Micro-Computed Tomography Reconstruction,” Stroke, 44(7), pp. 1936–1941. [CrossRef] [PubMed]
Ouared, R. , Larrabide, I. , Brina, O. , Bouillot, P. , Erceg, G. , Yilmaz, H. , Lovblad, K. O. , and Mendes Pereira, V. , “ Computational Fluid Dynamics Analysis of Flow Reduction Induced by Flow-Diverting Stents in Intracranial Aneurysms: A Patient-Unspecific Hemodynamics Change Perspective,” J. Neurointerventional Surg., epub.
Jing, L. , Zhong, J. , Liu, J. , Yang, X. , Paliwal, N. , Meng, H. , Wang, S. , and Zhang, Y. , 2016, “ Hemodynamic Effect of Flow Diverter and Coils in Treatment of Large and Giant Intracranial Aneurysms,” World Neurosurg., 89, pp. 199–207. [CrossRef] [PubMed]
Karmonik, C. , Chintalapani, G. , Redel, T. , Zhang, Y. J. , Diaz, O. , Klucznik, R. , and Grossman, R. G. , 2013, “ Hemodynamics at the Ostium of Cerebral Aneurysms With Relation to Post-Treatment Changes by a Virtual Flow Diverter: A Computational Fluid Dynamics Study,” 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 3–7, pp. 1895–1898.
Tsang, A. C. , Lai, S. S. , Chung, W. C. , Tang, A. Y. , Leung, G. K. , Poon, A. K. , Yu, A. C. , and Chow, K. W. , 2015, “ Blood Flow in Intracranial Aneurysms Treated With Pipeline Embolization Devices: Computational Simulation and Verification With Doppler Ultrasonography on Phantom Models,” Ultrasonography, 34(2), pp. 98–108. [CrossRef] [PubMed]
Kojima, M. , Irie, K. , Fukuda, T. , Arai, F. , Hirose, Y. , and Negoro, M. , 2012, “ The Study of Flow Diversion Effects on Aneurysm Using Multiple Enterprise Stents and Two Flow Diverters,” Asian J. Neurosurg., 7(4), pp. 159–165. [CrossRef] [PubMed]
Janiga, G. , Daroczy, L. , Berg, P. , Thevenin, D. , Skalej, M. , and Beuing, O. , 2015, “ An Automatic CFD-Based Flow Diverter Optimization Principle for Patient-Specific Intracranial Aneurysms,” J. Biomech., 48(14), pp. 3846–3852. [CrossRef] [PubMed]
Shobayashi, Y. , Tateshima, S. , Kakizaki, R. , Sudo, R. , Tanishita, K. , and Vinuela, F. , 2013, “ Intra-Aneurysmal Hemodynamic Alterations by a Self-Expandable Intracranial Stent and Flow Diversion Stent: High Intra-Aneurysmal Pressure Remains Regardless of Flow Velocity Reduction,” J. Neurointerventional Surg., 5(Suppl. 3), pp. iii38–iii42. [CrossRef]
Augsburger, L. , Reymond, P. , Rufenacht, D. A. , and Stergiopulos, N. , 2011, “ Intracranial Stents Being Modeled as a Porous Medium: Flow Simulation in Stented Cerebral Aneurysms,” Ann. Biomed. Eng., 39(2), pp. 850–863. [CrossRef] [PubMed]
Sadasivan, C. , Cesar, L. , Seong, J. , Wakhloo, A. K. , and Lieber, B. B. , 2009, “ Treatment of Rabbit Elastase-Induced Aneurysm Models by Flow Diverters: Development of Quantifiable Indexes of Device Performance Using Digital Subtraction Angiography,” IEEE Trans. Med. Imaging, 28(7), pp. 1117–1125. [CrossRef] [PubMed]
Grunwald, I. Q. , Kamran, M. , Corkill, R. A. , Kuhn, A. L. , Choi, I. S. , Turnbull, S. , Dobson, D. , Fassbender, K. , Watson, D. , and Gounis, M. J. , 2012, “ Simple Measurement of Aneurysm Residual After Treatment: The SMART Scale for Evaluation of Intracranial Aneurysms Treated With Flow Diverters,” Acta Neurochir., 154(1), pp. 21–26; Discussion 26. [CrossRef]
Joshi, M. D. , O'Kelly, C. J. , Krings, T. , Fiorella, D. , and Marotta, T. R. , 2013, “ Observer Variability of an Angiographic Grading Scale Used for the Assessment of Intracranial Aneurysms Treated With Flow-Diverting Stents,” AJNR, 34(8), pp. 1589–1592. [CrossRef] [PubMed]
Struffert, T. , Ott, S. , Kowarschik, M. , Bender, F. , Adamek, E. , Engelhorn, T. , Golitz, P. , Lang, S. , Strother, C. M. , and Doerfler, A. , 2013, “ Measurement of Quantifiable Parameters by Time-Density Curves in the Elastase-Induced Aneurysm Model: First Results in the Comparison of a Flow Diverter and a Conventional Aneurysm Stent,” Eur. Radiol., 23(2), pp. 521–527. [CrossRef] [PubMed]
Cho, Y. I. , and Kensey, K. R. , 1991, “ Effects of the Non-Newtonian Viscosity of Blood on Flows in a Diseased Arterial Vessel. Part 1: Steady Flows,” Biorheology, 28(3–4), pp. 241–262. https://www.researchgate.net/profile/Young_Cho5/publication/21222708_Effects_of_the_non-Newtonian_viscosity_of_blood_flows_in_a_diseased_arterial_vessel_Part_I_steady_flows/links/541ed8b90cf203f155c247b6.pdf [PubMed]
Ohta, M. , Wetzel, S. G. , Dantan, P. , Bachelet, C. , Lovblad, K. O. , Yilmaz, H. , Flaud, P. , and Rufenacht, D. A. , 2005, “ Rheological Changes After Stenting of a Cerebral Aneurysm: A Finite Element Modeling Approach,” Cardiovasc. Interventional Radiol., 28(6), pp. 768–772. [CrossRef]
Tateshima, S. , Jones, J. G. , Mayor Basto, F. , Vinuela, F. , and Duckwiler, G. R. , 2014, “ Aneurysm Pressure Measurement Before and After Placement of a Pipeline Stent: Feasibility Study Using a 0.014 Inch Pressure Wire for Coronary Intervention,” J. Neurointerventional Surg., 8(6), pp. 603–607. [CrossRef]
Augsburger, L. , Farhat, M. , Reymond, P. , Fonck, E. , Kulcsar, Z. , Stergiopulos, N. , and Rufenacht, D. A. , 2009, “ Effect of Flow Diverter Porosity on Intraaneurysmal Blood Flow,” Klin. Neuroradiologie, 19(3), pp. 204–214. [CrossRef]
Rayepalli, S. , Gupta, R. , Lum, C. , Majid, A. , and Koochesfahani, M. , 2013, “ The Impact of Stent Strut Porosity on Reducing Flow in Cerebral Aneurysms,” J. Neuroimaging, 23(4), pp. 495–501. [CrossRef] [PubMed]
Yu, C. H. , and Kwon, T. K. , 2014, “ Study of Parameters for Evaluating Flow Reduction With Stents in a Sidewall Aneurysm Phantom Model,” Biomed. Mater. Eng., 24(6), pp. 2417–2424. [PubMed]
Lee, C. J. , Srinivas, K. , and Qian, Y. , 2014, “ Three-Dimensional Hemodynamic Design Optimization of Stents for Cerebral Aneurysms,” Proc. Inst. Mech. Eng., Part H, 228(3), pp. 213–224. [CrossRef]
Brinjikji, W. , Murad, M. H. , Lanzino, G. , Cloft, H. J. , and Kallmes, D. F. , 2013, “ Endovascular Treatment of Intracranial Aneurysms With Flow Diverters: A Meta-Analysis,” Stroke, 44(2), pp. 442–447. [CrossRef] [PubMed]
Cebral, J. R. , Raschi, M. , Mut, F. , Ding, Y. H. , Dai, D. , Kadirvel, R. , and Kallmes, D. , 2014, “ Analysis of Flow Changes in Side Branches Jailed by Flow Diverters in Rabbit Models,” Int. J. Numer. Methods Biomed. Eng., 30(10), pp. 988–999. [CrossRef]
Hu, P. , Qian, Y. , Zhang, Y. , Zhang, H. Q. , Li, Y. , Chong, W. , and Ling, F. , 2015, “ Blood Flow Reduction of Covered Small Side Branches After Flow Diverter Treatment: A Computational Fluid Hemodynamic Quantitative Analysis,” J. Biomech., 48(6), pp. 895–898. [CrossRef] [PubMed]
Tang, A. Y. , Chung, W. C. , Liu, E. T. , Qu, J. Q. , Tsang, A. C. , Leung, G. K. , Leung, K. M. , Yu, A. C. , and Chow, K. W. , 2015, “ Computational Fluid Dynamics Study of Bifurcation Aneurysms Treated With Pipeline Embolization Device: Side Branch Diameter Study,” J. Med. Biol. Eng., 35(3), pp. 293–304. [CrossRef] [PubMed]
Makoyeva, A. , Bing, F. , Darsaut, T. E. , Salazkin, I. , and Raymond, J. , 2013, “ The Varying Porosity of Braided Self-Expanding Stents and Flow Diverters: An Experimental Study,” AJNR, 34(3), pp. 596–602. [CrossRef] [PubMed]
Karunanithi, K. , Lee, C. J. , Chong, W. , and Qian, Y. , 2015, “ The Influence of Flow Diverter's Angle of Curvature Across the Aneurysm Neck on Its Haemodynamics,” Proc. Inst. Mech. Eng., Part H, 229(8), pp. 560–569. [CrossRef]
Patel, N. V. , Gounis, M. J. , Wakhloo, A. K. , Noordhoek, N. , Blijd, J. , Babic, D. , Takhtani, D. , Lee, S. K. , and Norbash, A. , 2011, “ Contrast-Enhanced Angiographic Cone-Beam CT of Cerebrovascular Stents: Experimental Optimization and Clinical Application,” AJNR, 32(1), pp. 137–144. [PubMed]
Raymond, J. , Darsaut, T. E. , Bing, F. , Makoyeva, A. , Kotowski, M. , Gevry, G. , and Salazkin, I. , 2013, “ Stent-Assisted Coiling of Bifurcation Aneurysms May Improve Endovascular Treatment: A Critical Evaluation in an Experimental Model,” AJNR, 34(3), pp. 570–576. [CrossRef] [PubMed]
Gwilliam, M. N. , Hoggard, N. , Capener, D. , Singh, P. , Marzo, A. , Verma, P. K. , and Wilkinson, I. D. , 2009, “ MR Derived Volumetric Flow Rate Waveforms at Locations Within the Common Carotid, Internal Carotid, and Basilar Arteries,” J. Cereb. Blood Flow Metab., 29(12), pp. 1975–1982. [CrossRef] [PubMed]
Womersley, J. R. , 1955, “ Method for the Calculation of Velocity, Rate of Flow and Viscous Drag in Arteries When the Pressure Gradient is Known,” J. Physiol., 127(3), pp. 553–563. [CrossRef] [PubMed]
Bathe, K.-J. , and Bathe, K.-J. , 1996, Finite Element Procedures, Prentice Hall, Englewood Cliffs, NJ.
Fiorella, D. , Arthur, A. , Boulos, A. , Diaz, O. , Jabbour, P. , Pride, L. , Turk, A. S. , Woo, H. H. , Derdeyn, C. , Millar, J. , and Clifton, A. , 2016, “ Final Results of the U.S. Humanitarian Device Exemption Study of the Low-Profile Visualized Intraluminal Support (LVIS) Device,” J. Neurointerventional Surg., 8(9), pp. 894–897. [CrossRef]
Dholakia, R. , Drakopoulos, F. , Sadasivan, C. , Jiao, X. , Fiorella, D. J. , Woo, H. H. , Lieber, B. B. , and Chrisochoides, N. , 2015, “ High Fidelity Image-to-Mesh Conversion for Brain Aneurysm/Stent Geometries,” IEEE International Symposium on Biomedical Imaging. https://crtc.cs.odu.edu/pub/papers/conf_153.pdf
Foteinos, P. , and Chrisochoides, N. , 2013, “ High Quality Real-Time Image-to-Mesh Conversion for Finite Element Simulations,” 27th ACM International Conference on Supercomputing (ICS'13), pp. 233–242.
Foteinos, P. , and Chrisochoides, N. , 2014, “ High Quality Real-Time Image-to-Mesh Conversion for Finite Element Simulations,” J. Parallel Distrib. Comput., 74(2), pp. 2123–2140. [CrossRef]
Stewart, S. C. , Paterson, E. , Burgreen, G. , Hariharan, P. , Giarra, M. , Reddy, V. , Day, S. , Manning, K. , Deutsch, S. , Berman, M. , Myers, M. , and Malinauskas, R. , 2012, “ Assessment of CFD Performance in Simulations of an Idealized Medical Device: Results of FDA's First Computational Interlaboratory Study,” Cardiovasc. Eng. Technol., 3(2), pp. 139–160. [CrossRef]
Hariharan, P. , D'Souza, G. , Horner, M. , Malinauskas, R. A. , and Myers, M. R. , 2015, “ Verification Benchmarks to Assess the Implementation of Computational Fluid Dynamics Based Hemolysis Prediction Models,” ASME J. Biomech. Eng., 137(9), p. 094501. [CrossRef]
Trias, M. , Arbona, A. , Masso, J. , Minano, B. , and Bona, C. , 2014, “ FDA's Nozzle Numerical Simulation Challenge: Non-Newtonian Fluid Effects and Blood Damage,” PLoS One, 9(3), p. e92638. [CrossRef] [PubMed]
Steinman, D. A. , Hoi, Y. , Fahy, P. , Morris, L. , Walsh, M. T. , Aristokleous, N. , Anayiotos, A. S. , Papaharilaou, Y. , Arzani, A. , Shadden, S. C. , Berg, P. , Janiga, G. , Bols, J. , Segers, P. , Bressloff, N. W. , Cibis, M. , Gijsen, F. H. , Cito, S. , Pallares, J. , Browne, L. D. , Costelloe, J. A. , Lynch, A. G. , Degroote, J. , Vierendeels, J. , Fu, W. , Qiao, A. , Hodis, S. , Kallmes, D. F. , Kalsi, H. , Long, Q. , Kheyfets, V. O. , Finol, E. A. , Kono, K. , Malek, A. M. , Lauric, A. , Menon, P. G. , Pekkan, K. , Esmaily Moghadam, M. , Marsden, A. L. , Oshima, M. , Katagiri, K. , Peiffer, V. , Mohamied, Y. , Sherwin, S. J. , Schaller, J. , Goubergrits, L. , Usera, G. , Mendina, M. , Valen-Sendstad, K. , Habets, D. F. , Xiang, J. , Meng, H. , Yu, Y. , Karniadakis, G. E. , Shaffer, N. , and Loth, F. , 2013, “ Variability of Computational Fluid Dynamics Solutions for Pressure and Flow in a Giant Aneurysm: The ASME 2012 Summer Bioengineering Conference CFD Challenge,” ASME J. Biomech. Eng., 135(2), p. 021016.
Berg, P. , Roloff, C. , Beuing, O. , Voss, S. , Sugiyama, S. I. , Aristokleous, N. , Anayiotos, A. S. , Ashton, N. , Revell, A. , Bressloff, N. W. , Brown, A. G. , Chung, B. J. , Cebral, J. R. , Copelli, G. , Fu, W. , Qiao, A. , Geers, A. J. , Hodis, S. , Dragomir-Daescu, D. , Nordahl, E. , Suzen, Y. B. , Khan, M. O. , Valen-Sendstad, K. , Kono, K. , Menon, P. G. , Albal, P. G. , Mierka, O. , Munster, R. , Morales, H. G. , Bonnefous, O. , Osman, J. , Goubergrits, L. , Pallares, J. , Cito, S. , Passalacqua, A. , Piskin, S. , Pekkan, K. , Ramalho, S. , Marques, N. , Sanchi, S. , Schumacher, K. R. , Sturgeon, J. , Svihlova, H. , Hron, J. , Usera, G. , Mendina, M. , Xiang, J. , Meng, H. , Steinman, D. A. , and Janiga, G. , 2015, “ The Computational Fluid Dynamics Rupture Challenge 2013—Phase II: Variability of Hemodynamic Simulations in Two Intracranial Aneurysms,” ASME J. Biomech. Eng., 137(12), p. 121008. [CrossRef]
Chung, B. , Mut, F. , Kadirvel, R. , Lingineni, R. , Kallmes, D. F. , and Cebral, J. R. , 2015, “ Hemodynamic Analysis of Fast and Slow Aneurysm Occlusions by Flow Diversion in Rabbits,” J. Neurointerventional Surg., 7(12), pp. 931–935. [CrossRef]
Chong, W. , Zhang, Y. , Qian, Y. , Lai, L. , Parker, G. , and Mitchell, K. , 2014, “ Computational Hemodynamics Analysis of Intracranial Aneurysms Treated With Flow Diverters: Correlation With Clinical Outcomes,” AJNR, 35(1), pp. 136–142. [CrossRef] [PubMed]
Pereira, V. M. , Bonnefous, O. , Ouared, R. , Brina, O. , Stawiaski, J. , Aerts, H. , Ruijters, D. , Narata, A. P. , Bijlenga, P. , Schaller, K. , and Lovblad, K. O. , 2013, “ A DSA-Based Method Using Contrast-Motion Estimation for the Assessment of the Intra-Aneurysmal Flow Changes Induced by Flow-Diverter Stents,” AJNR, 34(4), pp. 808–815. [CrossRef] [PubMed]
Golitz, P. , Struffert, T. , Rosch, J. , Ganslandt, O. , Knossalla, F. , and Doerfler, A. , 2015, “ Cerebral Aneurysm Treatment Using Flow-Diverting Stents: In-Vivo Visualization of Flow Alterations by Parametric Colour Coding to Predict Aneurysmal Occlusion: Preliminary Results,” Eur. Radiol., 25(2), pp. 428–435. [CrossRef] [PubMed]
Malaspinas, O. , Turjman, A. , Ribeiro de Sousa, D. , Garcia-Cardena, G. , Raes, M. , Nguyen, P. T. , Zhang, Y. , Courbebaisse, G. , Lelubre, C. , Zouaoui Boudjeltia, K. , and Chopard, B. , 2016, “ A Spatio-Temporal Model for Spontaneous Thrombus Formation in Cerebral Aneurysms,” J. Theor. Biol., 394, pp. 68–76. [CrossRef] [PubMed]
Peach, T. W. , Ngoepe, M. , Spranger, K. , Zajarias-Fainsod, D. , and Ventikos, Y. , 2014, “ Personalizing Flow-Diverter Intervention for Cerebral Aneurysms: From Computational Hemodynamics to Biochemical Modeling,” Int. J. Numer. Methods Biomed. Eng., 30(11), pp. 1387–1407. [CrossRef]

Figures

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Fig. 1

Simplified schematic of the therapeutic mechanism of flow diversion treatment for aneurysms. (a) An aneurysm is (b) treated by implantation of a flow diverter, which reduces flow activity within the aneurysm and (c) promotes clot formation within the aneurysm over hours to days; concurrently, a new arterial lining, called neointima, starts growing over the device. (d) Over weeks to months, the vessel remodels itself by resorbing the aneurysm along with completion of neointimal coverage of the device. While the aneurysm which is essentially a “dead-end” clots off, the natural pressure gradient maintains blood flow through side branches covered by the mesh.

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Fig. 2

(a) The different mesh structures of a flow diverter (Pipeline, right) and an intracranial stent (Enterprise, left) contribute to the difference in function of the devices. (b) Theoretical variation of porosity and pore density based on deployment diameter for a “typical” flow diverter with 48 wires, about 30 μm wire diameter, in-air device diameter (dashed line) of 4 mm and approximately 70% porosity. These plots are characteristic of braided flow diverters. (c) Numerical simulation of a flow diverter deployed across a cavernous aneurysm shows the local variability that can exist in the pore structure at the aneurysm neck (inset: black border demarcates the aneurysm neck).

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Fig. 3

(a) Computational fluid dynamics results (velocity magnitudes on top and vectors on bottom) of intra-aneurysmal flow during systole in a simplified sidewall aneurysm. The flow enters the distal neck in the unstented (control) case and forms a vortex within the aneurysm. Flow activity progressively reduces after implantation of a high-porosity stent (Neuroform) and a low-porosity stent (Pipeline). (b) Average velocity measures (as percentage of unstented cases) from a few literature reports. Decreasing porosity is seen to reduce flow activity; data toward the bottom of the plot are from sidewall-type geometries while those toward the top are from bifurcation-type geometries.

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Fig. 4

Micro-CT slice of Pipeline in the aneurysm model (left) and the same slice after segmentation with the device wires cropped (right)

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Fig. 5

(a) CAD model in position across the aneurysm neck (top row), and expanded view of the anisotropic tetrahedral mesh generated using meshing tools available within adina (bottom row): (a) Pipeline and (b) FRED devices

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Fig. 6

Peak systolic speed distribution in the aneurysm (left) and velocity vector map (right) for (a) control, (b) Neuroform, (c) Enterprise, (d) LVIS, (e) FRED, and (f) Pipeline

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Fig. 7

Instantaneous intra-aneurysmal kinetic energy throughout the cardiac beat for the five commercial neurovascular devices

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Fig. 8

Comparison of intra-aneurysmal mean kinetic energies among five commercial neurovascular devices: (a) absolute values and (b) percent reductions as compared to control

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Fig. 9

The effect of measured porosity and pore density on reduction in intra-aneurysmal mean kinetic energy

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Fig. 10

Systolic velocity magnitudes in a right carotid superior hypophyseal (top) and a right carotid cavernous (bottom) aneurysm before (Pre) and after (Post) flow diversion treatment; the reductions in mean intraneurysmal kinetic energy (KE) are noted for each case

Tables

Table Grahic Jump Location
Table 1 Characteristics of the five neurovascular devices used in the study. The devices were deployed in an idealized sidewall aneurysm geometry (parent vessel diameter 4 mm, aneurysm dimensions 5 mm neck, and 7 mm diameter).
Table Footer NoteaPorosity and pore density are based on images of micro-CT reconstructed data and were calculated based on the projection of the device structure onto the plane of the aneurysm neck.

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