P Bouillot1,2, O Brina1, H Yilmaz1, M Farhat2, G Erceg1, K-O Lovblad1, M I Vargas1, Z Kulcsar1, V M Pereira3,4. 1. From the Division of Neuroradiology (P.B., O.B., H.Y., G.E., K.-O.L., M.I.V., Z.K.), University Hospitals of Geneva, Geneva, Switzerland. 2. Laboratory for Hydraulic Machines (P.B., M.F.), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. 3. Division of Neuroradiology (V.M.P.), Department of Medical Imaging vitormpbr@hotmail.com. 4. Division of Neurosurgery (V.M.P.), Department of Surgery, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada.
Abstract
BACKGROUND AND PURPOSE: Intracranial stents have become extremely important in the endovascular management of complex intracranial aneurysms. Sizing and landing zone predictions are still very challenging steps in the procedure. Virtual stent deployment may help therapeutic planning, device choice, and hemodynamic simulations. We aimed to assess the predictability of our recently developed virtual deployment model by comparing in vivo and virtual stents implanted in a consecutive series of patients presenting with intracranial aneurysms. MATERIALS AND METHODS: Virtual stents were implanted in patient-specific geometries of intracranial aneurysms treated with the Pipeline Embolization Device. The length and cross-section of virtual and real stents measured with conebeam CT were compared. The influence of vessel geometry modifications occurring during the intervention was analyzed. RESULTS: The virtual deployment based on pre- and poststent implantation 3D rotational angiography overestimated (underestimated) the device length by 13% ± 11% (-9% ± 5%). These differences were highly correlated (R2 = 0.67) with the virtual-versus-real stent radius differences of -6% ± 7% (5% ± 4%) for predictions based on pre- and poststent implantation 3D rotational angiography. These mismatches were due principally to implantation concerns and vessel-shape modifications. CONCLUSIONS: The recently proposed geometric model was shown to predict accurately the deployment of Pipeline Embolization Devices when the stent radius was well-assessed. However, unpredictable delivery manipulations and variations of vessel geometry occurring during the intervention might impact the stent implantation.
BACKGROUND AND PURPOSE: Intracranial stents have become extremely important in the endovascular management of complex intracranial aneurysms. Sizing and landing zone predictions are still very challenging steps in the procedure. Virtual stent deployment may help therapeutic planning, device choice, and hemodynamic simulations. We aimed to assess the predictability of our recently developed virtual deployment model by comparing in vivo and virtual stents implanted in a consecutive series of patients presenting with intracranial aneurysms. MATERIALS AND METHODS: Virtual stents were implanted in patient-specific geometries of intracranial aneurysms treated with the Pipeline Embolization Device. The length and cross-section of virtual and real stents measured with conebeam CT were compared. The influence of vessel geometry modifications occurring during the intervention was analyzed. RESULTS: The virtual deployment based on pre- and poststent implantation 3D rotational angiography overestimated (underestimated) the device length by 13% ± 11% (-9% ± 5%). These differences were highly correlated (R2 = 0.67) with the virtual-versus-real stent radius differences of -6% ± 7% (5% ± 4%) for predictions based on pre- and poststent implantation 3D rotational angiography. These mismatches were due principally to implantation concerns and vessel-shape modifications. CONCLUSIONS: The recently proposed geometric model was shown to predict accurately the deployment of Pipeline Embolization Devices when the stent radius was well-assessed. However, unpredictable delivery manipulations and variations of vessel geometry occurring during the intervention might impact the stent implantation.
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Authors: O Brina; P Bouillot; P Reymond; A S Luthman; C Santarosa; M Fahrat; K O Lovblad; P Machi; B M A Delattre; V M Pereira; M I Vargas Journal: AJNR Am J Neuroradiol Date: 2019-11-14 Impact factor: 3.825