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Abstract
Introduction Giant intracranial aneurysms present a danger of hemorrhage, mass effect, and thromboembolism. Fusiform or wide-necked aneurysms not amenable to clipping or coiling can be sometimes treated by indirect trapping or by deployment of a flow-diverter device. Such interventions are aimed at reducing the flow through the aneurysm in the hope that this would inhibit its progression. Patient-specific computational models based on medical imaging data can predict postoperative flow fields resulting from alternative interventional options and thus may help avoid complications related to thrombotic occlusion of distal arteries emanating from the aneurysmal vessel.
Methods Contrast-enhanced MR angiography data acquired prior to intervention were used to generate computational fluid dynamics (CFD) models of the preoperative geometries. The inflow conditions were measured in the proximal arteries with 2D phase-contrast MRI. Preoperative flow computations were validated in comparison to in vivo 4D Flow MRI measurements, providing time-resolved flow fields. The models were modified according to the proposed interventions, e.g. clipping of proximal or distal vessels and adding bypasses, or deploying flow-diverter devices. Postoperative flows were computed and used to simulate transport of a virtual contrast agent. This allowed evaluation of the changes in filling and washout times caused by different interventions.
Results We illustrate this approach with retrospective analyzes of two intracranial aneurysms. In the first case, a fusiform basilar aneurysm was treated by placing a clip across the superior cerebellar arteries, leaving one SCA attached to the basilar apex and the other to the basilar trunk. A bypass was performed to supply the basilar apex from the anterior circulation. The CFD model showed that this intervention would result in substantial increase of the flow residence time in the distal basilar trunk (Figure 1). This indicated a high likelihood of pontine perforators thrombosis, which was observed following the procedure. In the second case, a giant internal carotid aneurysm was treated by placing a flow-diverter. The image-based computational model incorporated the device modeled as a porous tube. The flow field predicted with CFD (Figure 2a) is in agreement with that measured postoperatively with 4D Flow MRI (Figure 2b) and show a region of slow, recirculating flow with increased washout time which will facilitate thrombotic occlusion of the lesion.
Conclusions The study indicates that computational models constructed from medical imaging data can be used to predict postoperative flow in cerebral aneurysms. This information, available prior to intervention, may help improve the outcome of surgical procedures.
Disclosures V. Rayz: 1; C; NHLBI R01 HL115267. O. Zaidat: None. V. Halbach: None. D. Saloner: None. M. Lawton: None.