O Brina1, R Ouared1, O Bonnefous2, F van Nijnatten3, P Bouillot1, P Bijlenga4, K Schaller4, K-O Lovblad1, T Grünhagen3, D Ruijters3, V Mendes Pereira5. 1. From the Interventional Neuroradiology Unit (O. Brina, R.O., P. Bouillot, K.-O.L., V.M.P.), Service of Neuroradiology. 2. Medisys Research Laboratory (O. Bonnefous). 3. Interventional X-Ray (F.v.N., T.G., D.R.), Philips Healthcare, Zürich, Switzerland. 4. Service of Neurosurgery (P. Bijlenga, K.S.), University of Geneva Hospitals and Faculty of Medicine, Geneva, Switzerland. 5. From the Interventional Neuroradiology Unit (O. Brina, R.O., P. Bouillot, K.-O.L., V.M.P.), Service of Neuroradiology Division of Neuroradiology (V.M.P.), Department of Medical Imaging Division of Neurosurgery (V.M.P.), Department of Surgery, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada. vitormpbr@hotmail.com.
Abstract
BACKGROUND AND PURPOSE: Digital subtraction angiography is the gold standard vascular imaging and it is used for all endovascular treatment of intracranial anerysms. Optical flow imaging has been described as a potential method to evaluate cerebral hemodynamics through DSA. In this study, we aimed to compare the flow patterns measured during angiography, by using an optical flow method, with those measured by using computational fluid dynamics in intracranial aneurysms. MATERIALS AND METHODS: A consecutive series of 21 patients harboring unruptured saccular intracranial aneurysms who underwent diagnostic angiography before treatment was considered. High-frame-rate digital subtraction angiography was performed to obtain an intra-aneurysmal velocity field by following the cardiac-modulated contrast wave through the vascular structures by using optical flow principles. Additionally, computational fluid dynamics modeling was performed for every case by using patient-specific inlet-boundary conditions measured with the optical flow method from both DSA and 3D rotational angiography datasets. Three independent observers compared qualitatively both the inflow direction and the apparent recirculation in regular DSA, optical flow images, and computational fluid dynamics flow patterns for each patient; κ statistics were estimated. RESULTS: We included 21 patients. In 14 of these 21, the flow patterns were conclusive and matching between the optical flow images and computational fluid dynamics within the same projection view (κ = .91). However, in only 8 of these 14 patients the optical flow images were conclusive and matching regular DSA images (observer κ = 0.87). In 7 of the 21 patients, the flow patterns in the optical flow images were inconclusive, possibly due to improper projection angles. CONCLUSIONS: The DSA-based optical flow technique was considered qualitatively consistent with computational fluid dynamics outcomes in evaluating intra-aneurysmal inflow direction and apparent recirculation. Moreover, the optical flow technique may provide the premises for new solutions for improving the visibility of flow patterns when contrast motion in DSA is not apparent. This technique is a diagnostic method to evaluate intra-aneurysmal flow patterns and could be used in the future for validation and patient evaluation.
BACKGROUND AND PURPOSE: Digital subtraction angiography is the gold standard vascular imaging and it is used for all endovascular treatment of intracranial anerysms. Optical flow imaging has been described as a potential method to evaluate cerebral hemodynamics through DSA. In this study, we aimed to compare the flow patterns measured during angiography, by using an optical flow method, with those measured by using computational fluid dynamics in intracranial aneurysms. MATERIALS AND METHODS: A consecutive series of 21 patients harboring unruptured saccular intracranial aneurysms who underwent diagnostic angiography before treatment was considered. High-frame-rate digital subtraction angiography was performed to obtain an intra-aneurysmal velocity field by following the cardiac-modulated contrast wave through the vascular structures by using optical flow principles. Additionally, computational fluid dynamics modeling was performed for every case by using patient-specific inlet-boundary conditions measured with the optical flow method from both DSA and 3D rotational angiography datasets. Three independent observers compared qualitatively both the inflow direction and the apparent recirculation in regular DSA, optical flow images, and computational fluid dynamics flow patterns for each patient; κ statistics were estimated. RESULTS: We included 21 patients. In 14 of these 21, the flow patterns were conclusive and matching between the optical flow images and computational fluid dynamics within the same projection view (κ = .91). However, in only 8 of these 14 patients the optical flow images were conclusive and matching regular DSA images (observer κ = 0.87). In 7 of the 21 patients, the flow patterns in the optical flow images were inconclusive, possibly due to improper projection angles. CONCLUSIONS: The DSA-based optical flow technique was considered qualitatively consistent with computational fluid dynamics outcomes in evaluating intra-aneurysmal inflow direction and apparent recirculation. Moreover, the optical flow technique may provide the premises for new solutions for improving the visibility of flow patterns when contrast motion in DSA is not apparent. This technique is a diagnostic method to evaluate intra-aneurysmal flow patterns and could be used in the future for validation and patient evaluation.
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