Evaluation of Two Fast Virtual Stenting Algorithms for Intracranial Aneurysm Flow Diversion

Author(s): Saeb R. Lamooki, Vincent M. Tutino, Nikhil Paliwal, Robert J. Damiano, Muhammad Waqas, Setlur S.V. Nagesh, Hamidreza Rajabzadeh-Oghaz, Kunal Vakharia, Adnan H. Siddiqui, Hui Meng*

Journal Name: Current Neurovascular Research

Volume 17 , Issue 1 , 2020

Become EABM
Become Reviewer


Background: Endovascular treatment of intracranial aneurysms (IAs) by flow diverter (FD) stents depends on flow modification. Patient-specific modeling of FD deployment and computational fluid dynamics (CFD) could enable a priori endovascular strategy optimization. We developed a fast, simplistic, expansion-free balls-weeping algorithm to model FDs in patientspecific aneurysm geometry. However, since such strong simplification could result in less accurate simulations, we also developed a fast virtual stenting workflow (VSW) that explicitly models stent expansion using pseudo-physical forces.

Methods: To test which of these two fast algorithms more accurately simulates real FDs, we applied them to virtually treat three representative patient-specific IAs. We deployed Pipeline Embolization Device into 3 patient-specific silicone aneurysm phantoms and simulated the treatments using both balls-weeping and VSW algorithms in computational aneurysm models. We then compared the virtually deployed FD stents against experimental results in terms of geometry and post-treatment flow fields. For stent geometry, we evaluated gross configurations and porosity. For post-treatment aneurysmal flow, we compared CFD results against experimental measurements by particle image velocimetry.

Results: We found that VSW created more realistic FD deployments than balls-weeping in terms of stent geometry, porosity and pore density. In particular, balls-weeping produced unrealistic FD bulging at the aneurysm neck, and this artifact drastically increased with neck size. Both FD deployment methods resulted in similar flow patterns, but the VSW had less error in flow velocity and inflow rate.

Conclusion: In conclusion, modeling stent expansion is critical for preventing unrealistic bulging effects and thus should be considered in virtual FD deployment algorithms. Also endowed with its high computational efficiency and superior accuracy, the VSW algorithm is a better candidate for implementation into a bedside clinical tool for FD deployment simulation.

Keywords: Particle image velocimetry, computational fluid dynamics, validation, cerebral aneurysm, endovascular therapy, flow diverting stent.

Becske, T.; Kallmes, D.F.; Saatci, I. Pipeline for uncoilable or failed aneurysms: results from a multicenter clinical trial. Radiology, 2013, 267(3), 858-868.
[http://dx.doi.org/10.1148/radiol.13120099] [PMID: 23418004]
Brinjikji, W.; Murad, M.H.; Lanzino, G.; Cloft, H.J.; Kallmes, D.F. Endovascular treatment of intracranial aneurysms with flow diverters: A meta-analysis. Stroke, 2013, 44(2), 442-447.
[http://dx.doi.org/10.1161/STROKEAHA.112.678151] [PMID: 23321438]
Ikeda, H.; Ishii, A.; Kikuchi, T. Delayed aneurysm rupture due to residual blood flow at the inflow zone of the intracranial paraclinoid internal carotid aneurysm treated with the Pipeline embolization device: Histopathological investigation. J Perither Neuroradiol Surg Proc Relat Neurosci, 2015, 21(6), 674-683.https://www.ncbi.nlm.nih.gov/pubmed/26500232
[PMID: 26500232]
Ma, D.; Dargush, G.F.; Natarajan, S.K.; Levy, E.I.; Siddiqui, A.H.; Meng, H. Computer modeling of deployment and mechanical expansion of neurovascular flow diverter in patient-specific intracranial aneurysms. J. Biomech., 2012, 45(13), 2256-2263.
[http://dx.doi.org/10.1016/j.jbiomech.2012.06.013] [PMID: 22818662]
Ma, D.; Dumont, T.M.; Kosukegawa, H. High fidelity virtual stenting (HiFiVS) for intracranial aneurysm flow diversion: In vitro and in silico. Ann. Biomed. Eng., 2013, 41(10), 2143-2156.
[http://dx.doi.org/10.1007/s10439-013-0808-4] [PMID: 23604850]
Paliwal, N.; Yu, H.; Xu, J. Virtual stenting workflow with vessel-specific initialization and adaptive expansion for neurovascular stents and flow diverters. Comput. Methods Biomech. Biomed. Engin., 2016, 19(13), 1423-1431.
[http://dx.doi.org/10.1080/10255842.2016.1149573] [PMID: 26899135]
Zhao, L.; Chen, D.; Chen, Z. Rapid virtual stenting for intracranial aneurysms. Proc SPIE Int Soc Opt Eng 2016 9786: 97860V https://www.ncbi.nlm.nih.gov/pubmed/27346910
[PMID: 27346910]
Ma, D.; Xiang, J.; Choi, H. Enhanced aneurysmal flow diversion using a dynamic push-pull technique: An experimental and modeling study. AJNR Am. J. Neuroradiol., 2014, 35(9), 1779-1785.
[http://dx.doi.org/10.3174/ajnr.A3933] [PMID: 24763414]
Xiang, J.; Ma, D.; Snyder, K.V.; Levy, E.I.; Siddiqui, A.H.; Meng, H. Increasing flow diversion for cerebral aneurysm treatment using a single flow diverter. Neurosurgery, 2014, 75(3), 286-294.
[http://dx.doi.org/10.1227/NEU.0000000000000409] [PMID: 24867201]
Kerber, C.W.; Heilman, C.B. Flow dynamics in the human carotid artery: I. Preliminary observations using a transparent elastic model. AJNR Am. J. Neuroradiol., 1992, 13(1), 173-180.https://www.ncbi.nlm.nih.gov/pubmed/1595439
[PMID: 1595439]
Yousif, M.Y.; Holdsworth, D.W.; Poepping, T.L. A blood-mimicking fluid for particle image velocimetry with silicone vascular models. Exp. Fluids, 2011, 50(3), 769-774.
[http://dx.doi.org/10.1007/s00348-010-0958-1] [PMID: 1595439]
Hopkins, L.M.; Kelly, J.T.; Wexler, A.S.; Prasad, A.K. Particle image velocimetry measurements in complex geometries. Exp. Fluids, 2000, 29(1), 91-95.
Aladin, A.I.; Whelton, S.P.; Al-Mallah, M.H. Relation of resting heart rate to risk for all-cause mortality by gender after considering exercise capacity (the Henry Ford exercise testing project). Am. J. Cardiol., 2014, 114(11), 1701-1706.
[http://dx.doi.org/10.1016/j.amjcard.2014.08.042] [PMID: 25439450]
Zhao, M.; Amin-Hanjani, S.; Ruland, S.; Curcio, A.P.; Ostergren, L.; Charbel, F.T. Regional cerebral blood flow using quantitative MR angiography. AJNR Am. J. Neuroradiol., 2007, 28(8), 1470-1473.
[http://dx.doi.org/10.3174/ajnr.A0582] [PMID: 17846193]
Adrian, R.J.; Westerweel, J. Particle image velocimetry; Cambridge University Press: UK, 2011.
Paliwal, N.; Yu, H.; Damiano, R. Fast virtual stenting with vessel-specific initialization and collision detection. Proceedings of the 7th Frontiers in Biomedical Devices, 2014 August 17-20Buffalo, New York. USA . USA: ASME, 2015.
Jing, L.; Liu, J.; Zhang, Y. Analysis of multiple intracranial aneurysms with different outcomes in the same patient after endovascular treatment. World Neurosurg., 2016, 91, 399-408.
[http://dx.doi.org/10.1016/j.wneu.2016.04.072] [PMID: 27132177]
Liu, J.; Jing, L.; Zhang, Y. Successful retreatment of recurrent intracranial vertebral artery dissecting aneurysms after stent-assisted coil embolization: A self-controlled hemodynamic analysis. World Neurosurg., 2017, 97, 344-350.
[http://dx.doi.org/10.1016/j.wneu.2016.10.003] [PMID: 27742509]
Wang, C.; Tian, Z.; Liu, J. Flow diverter effect of LVIS stent on cerebral aneurysm hemodynamics: A comparison with Enterprise stents and the Pipeline device. J. Transl. Med., 2016, 14(1), 199.
[http://dx.doi.org/10.1186/s12967-016-0959-9] [PMID: 27370946]
Zhang, Q.; Jing, L.; Liu, J. Predisposing factors for recanalization of cerebral aneurysms after endovascular embolization: A multivariate study. J. Neurointerv. Surg., 2018, 10(3), 252-257.
[http://dx.doi.org/10.1136/neurintsurg-2017-013041] [PMID: 28377443]
Zhang, Q.; Meng, Z.; Zhang, Y. Phantom-based experimental validation of fast virtual deployment of self-expandable stents for cerebral aneurysms. Biomed. Eng. Online, 2016, 15(Suppl. 2), 125.
[http://dx.doi.org/10.1186/s12938-016-0250-6] [PMID: 28155680]
Paliwal, N.; Jaiswal, P.; Tutino, V.M. Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning. Neurosurg. Focus, 2018, 45(5)E7
[http://dx.doi.org/10.3171/2018.8.FOCUS18332] [PMID: 30453461]
Paliwal, N.; Tutino, V.M. Ostium ratio and neck ratio could predict the outcome of sidewall intracranial aneurysms treated with flow diverters. AJNR Am. J. Neuroradiol., 2019, 40(2), 288-294.https://www.ncbi.nlm.nih.gov/pubmed/30679216
[PMID: 30679216]
Hoi, Y.; Woodward, S.H.; Kim, M.; Taulbee, D.B.; Meng, H. Validation of CFD simulations of cerebral aneurysms with implication of geometric variations. J. Biomech. Eng., 2006, 128(6), 844-851.
[http://dx.doi.org/10.1115/1.2354209] [PMID: 17154684]
Antiga, L.; Piccinelli, M.; Botti, L.; Ene-Iordache, B.; Remuzzi, A.; Steinman, D.A. An image-based modeling framework for patient-specific computational hemodynamics. Med. Biol. Eng. Comput., 2008, 46(11), 1097-1112.
[http://dx.doi.org/10.1007/s11517-008-0420-1] [PMID: 19002516]
Tutino, V.M.; Liaw, N.; Spernyak, J.A. Assessment of vascular geometry for bilateral carotid artery ligation to induce early basilar terminus aneurysmal remodeling in rats. Curr. Neurovasc. Res., 2016, 13(1), 82-92.
[http://dx.doi.org/10.2174/1567202612666151027143149] [PMID: 26503026]
Damiano, R.J.; Tutino, V.M.; Paliwal, N. Compacting a single flow diverter versus overlapping flow diverters for intracranial aneurysms: A computational study. AJNR Am. J. Neuroradiol., 2017, 38(3), 603-610.
[http://dx.doi.org/10.3174/ajnr.A5062] [PMID: 28057633]
Sadasivan, C.; Cesar, L.; Seong, J. An original flow diversion device for the treatment of intracranial aneurysms: Evaluation in the rabbit elastase-induced model. Stroke, 2009, 40(3), 952-958.
[http://dx.doi.org/10.1161/STROKEAHA.108.533760] [PMID: 19150864]
Damiano, R.J.; Tutino, V.M. Compacting a single flow diverter versus overlapping flow diverters for intracranial aneurysms: A computational study. AJNR Am. J. Neuroradiol., 2017, 38(3), 603-610.
Xiang, J.; Damiano, R.J.; Lin, N. High-fidelity virtual stenting: Modeling of flow diverter deployment for hemodynamic characterization of complex intracranial aneurysms. J. Neurosurg., 2015, 123(4), 832-840.
[http://dx.doi.org/10.3171/2014.11.JNS14497] [PMID: 26090829]
Spranger, K.; Capelli, C.; Bosi, G.M.; Schievano, S.; Ventikos, Y. Comparison and calibration of a real-time virtual stenting algorithm using finite element analysis and genetic algorithms. Comput. Methods Appl. Mech. Eng., 2015, 293, 462-480.
[http://dx.doi.org/10.1016/j.cma.2015.03.022] [PMID: 26664007]
van Ooij, P.; Guédon, A.; Poelma, C. Complex flow patterns in a real-size intracranial aneurysm phantom: Phase contrast MRI compared with particle image velocimetry and computational fluid dynamics. NMR Biomed., 2012, 25(1), 14-26.
[http://dx.doi.org/10.1002/nbm.1706] [PMID: 21480417]

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2020
Page: [58 - 70]
Pages: 13
DOI: 10.2174/1567202617666200120141608
Price: $65

Article Metrics

PDF: 22
PRC: 2