Computational Hemodynamics in Intracranial Vessels Reconstructed from Biplane Angiograms

Scalzo, Fabien; Hao, Qing; Walczak, Alan M.; Hu, Xiao; Hoi, Yiemeng; Hoffmann, Kenneth R.; Liebeskind, David S

doi:10.1007/978-3-642-17277-9_37

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…(iii) Generic boundary conditions and blood properties were defined in ANSYS CFX-Pre, with a pressure of 110 mmHg at the ICA inlet and a mass flow rate at the MCA and ACA outlets estimated on the basis of generic mean flow velocities from a population-based study [17]. Rigid vessel walls with no-slip conditions were assumed, and blood was considered as an incompressible Newtonian fluid as with previous studies (density 1.060 kg/m 3 and viscosity 0.0035 kg/m/s) [18,19]. (iv) Blood flow simulation was conducted under NavierÀStokes equations in ANSYS CFX.…”

Section: Computational Fluid Dynamics Modeling and Pressure Gradient Assessmentmentioning

confidence: 99%

Translesional pressure gradient and leptomeningeal collateral status in symptomatic middle cerebral artery stenosis

Leng

Lan

et al. 2017

Euro J of Neurology

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Section: Computational Fluid Dynamics Modeling and Pressure Gradient Assessmentmentioning

confidence: 99%

Translesional pressure gradient and leptomeningeal collateral status in symptomatic middle cerebral artery stenosis

Leng

Lan

et al. 2017

Euro J of Neurology

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“…Brain imaging techniques have undergone substantial advancements in the past 2 decades, providing more efficient, precise and reproducible assessments of brain parenchyma and vessels [66]. Neuroimaging in stroke extends beyond its role in diagnosis to reveal underlying pathophysiological mechanisms as well as to guiding clinical treatment decisions.…”

Section: Imaging Of Injury In Strokementioning

confidence: 99%

Ischemia-Reperfusion Injury in Stroke

2012

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Despite ongoing advances in stroke imaging and treatment, ischemic and hemorrhagic stroke continue to debilitate patients with devastating outcomes at both the personal and societal levels. While the ultimate goal of therapy in ischemic stroke is geared towards restoration of blood flow, even when mitigation of initial tissue hypoxia is successful, exacerbation of tissue injury may occur in the form of cell death, or alternatively, hemorrhagic transformation of reperfused tissue. Animal models have extensively demonstrated the concept of reperfusion injury at the molecular and cellular levels, yet no study has quantified this effect in stroke patients. These preclinical models have also demonstrated the success of a wide array of neuroprotective strategies at lessening the deleterious effects of reperfusion injury. Serial multimodal imaging may provide a framework for developing therapies for reperfusion injury.

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“…Despite these limitations, the proposed framework could be used to register perfusion angiography [17] maps to MRA, and compare/map Computational Fluid Dynamics (CFD) parameters extracted from DSA [18] with the ones computed from 3D MRA/CTA [19,20]. …”

Section: Discussionmentioning

confidence: 99%

Similarity Metric Learning for 2D to 3D Registration of Brain Vasculature

Tang

Scalzo

2016

Advances in Visual Computing

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2D to 3D image registration techniques are useful in the treatment of neurological diseases such as stroke. Image registration can aid physicians and neurosurgeons in the visualization of the brain for treatment planning, provide 3D information during treatment, and enable serial comparisons. In the context of stroke, image registration is challenged by the occluded vessels and deformed anatomy due to the ischemic process. In this paper, we present an algorithm to register 2D digital subtraction angiography (DSA) with 3D magnetic resonance angiography (MRA) based upon local point cloud descriptors. The similarity between these local descriptors is learned using a machine learning algorithm, allowing flexibility in the matching process. In our experiments, the error rate of 2D/3D registration using our machine learning similarity metric (52.29) shows significant improvement when compared to a Euclidean metric (152.54). The proposed similarity metric is versatile and could be applied to a wide range of 2D/3D registration.

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Computational Hemodynamics in Intracranial Vessels Reconstructed from Biplane Angiograms

Cited by 10 publications

References 14 publications

Translesional pressure gradient and leptomeningeal collateral status in symptomatic middle cerebral artery stenosis

Translesional pressure gradient and leptomeningeal collateral status in symptomatic middle cerebral artery stenosis

Ischemia-Reperfusion Injury in Stroke

Similarity Metric Learning for 2D to 3D Registration of Brain Vasculature

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