scholarly journals MR-based Computational Fluid Dynamics with Patient-specific Boundary Conditions for the Initiation of a Sidewall Aneurysm of a Basilar Artery

2015 ◽  
Vol 14 (2) ◽  
pp. 139-144 ◽  
Author(s):  
Haruo ISODA ◽  
Yasuo TAKEHARA ◽  
Takashi KOSUGI ◽  
Masaki TERADA ◽  
Takehiro NAITO ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Boundary Conditions ◽  
Basilar Artery ◽  
Patient Specific

On the choice of outlet boundary conditions for patient-specific analysis of aortic flow using computational fluid dynamics

2017 ◽  
Vol 60 ◽  
pp. 15-21 ◽  
Author(s):  
S. Pirola ◽  
Z. Cheng ◽  
O.A. Jarral ◽  
D.P. O'Regan ◽  
J.R. Pepper ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Boundary Conditions ◽  
Aortic Flow ◽  
Patient Specific ◽  
Specific Analysis

MRI Based Quantification of Outflow Boundary Conditions for Computational Fluid Dynamics of Stenosed Human Carotid Arteries

2010 ◽  
Author(s):  
Harald C. Groen ◽  
Lenette Simons ◽  
E. Marielle H. Bosboom ◽  
Frans van de Vosse ◽  
Anton F. W. van der Steen ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Boundary Conditions ◽  
Carotid Arteries ◽  
Patient Specific ◽  
Murray's Law ◽  
Murray’S Law ◽  
Outflow Boundary Conditions ◽  
Outflow Boundary ◽  
Human Carotid

Many studies have been performed to investigate the contribution of wall shear stress (WSS) to pathophysiological processes related to atherosclerosis (Groen, et al., 2007; Kaazempur-Mofrad, et al., 2004; Ku, et al., 1985). To investigate these relationships in stenosed human carotid arteries, accurate assessment of WSS is required. WSS can be calculated in vivo by coupling medical imaging and computational fluid dynamics (CFD). However, often patient specific in- and outflow information is unavailable. Therefore flow through the common (CCA), internal (ICA) and external (ECA) carotid artery needs to be estimated. Murray’s law (Murray, 1926) is often used for that purpose, but it is unclear whether this law holds for stenosed arteries. The goal of this study was to determine outflow boundary conditions for WSS calculations in stenosed carotid bifurcations. Therefore we first quantified the flow (Q) in carotid arteries with different degrees of area stenosis using phase-contrast MRI and determined an empirical relation between outflow-ratios and degree of area stenosis. Secondly we compared the estimated flow ratio based on Murray’s law to the ones measured by MRI. Finally we analyzed the influence of the outflow conditions on the calculated WSS using CFD.

scholarly journals Morphological and Hemodynamic Changes during Cerebral Aneurysm Growth

2021 ◽  
Vol 11 (4) ◽  
pp. 520
Author(s):  
Emily R. Nordahl ◽  
Susheil Uthamaraj ◽  
Kendall D. Dennis ◽  
Alena Sejkorová ◽  
Aleš Hejčl ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Kinetic Energy ◽  
Swirling Flow ◽  
Morphological Changes ◽  
Cerebral Aneurysms ◽  
Patient Specific ◽  
Aneurysm Wall ◽  
Aneurysm Growth ◽  
Computational Fluid Dynamics Cfd

Computational fluid dynamics (CFD) has grown as a tool to help understand the hemodynamic properties related to the rupture of cerebral aneurysms. Few of these studies deal specifically with aneurysm growth and most only use a single time instance within the aneurysm growth history. The present retrospective study investigated four patient-specific aneurysms, once at initial diagnosis and then at follow-up, to analyze hemodynamic and morphological changes. Aneurysm geometries were segmented via the medical image processing software Mimics. The geometries were meshed and a computational fluid dynamics (CFD) analysis was performed using ANSYS. Results showed that major geometry bulk growth occurred in areas of low wall shear stress (WSS). Wall shape remodeling near neck impingement regions occurred in areas with large gradients of WSS and oscillatory shear index. This study found that growth occurred in areas where low WSS was accompanied by high velocity gradients between the aneurysm wall and large swirling flow structures. A new finding was that all cases showed an increase in kinetic energy from the first time point to the second, and this change in kinetic energy seems correlated to the change in aneurysm volume.

scholarly journals Porous Media Computational Fluid Dynamics and the Role of the First Coil in the Embolization of Ruptured Intracranial Aneurysms

2021 ◽  
Vol 10 (7) ◽  
pp. 1348
Author(s):  
Karol Wiśniewski ◽  
Bartłomiej Tomasik ◽  
Zbigniew Tyfa ◽  
Piotr Reorowicz ◽  
Ernest Bobeff ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Porous Media ◽  
Packing Density ◽  
Coil Embolization ◽  
Intracranial Aneurysms ◽  
Patient Specific ◽  
Computational Fluid Dynamics Analysis ◽  
Ruptured Intracranial Aneurysms ◽  
Statistical Results

Background: The objective of our project was to identify a late recanalization predictor in ruptured intracranial aneurysms treated with coil embolization. This goal was achieved by means of a statistical analysis followed by a computational fluid dynamics (CFD) with porous media modelling approach. Porous media CFD simulated the hemodynamics within the aneurysmal dome after coiling. Methods: Firstly, a retrospective single center analysis of 66 aneurysmal subarachnoid hemorrhage patients was conducted. The authors assessed morphometric parameters, packing density, first coil volume packing density (1st VPD) and recanalization rate on digital subtraction angiograms (DSA). The effectiveness of initial endovascular treatment was visually determined using the modified Raymond–Roy classification directly after the embolization and in a 6- and 12-month follow-up DSA. In the next step, a comparison between porous media CFD analyses and our statistical results was performed. A geometry used during numerical simulations based on a patient-specific anatomy, where the aneurysm dome was modelled as a separate, porous domain. To evaluate hemodynamic changes, CFD was utilized for a control case (without any porosity) and for a wide range of porosities that resembled 1–30% of VPD. Numerical analyses were performed in Ansys CFX solver. Results: A multivariate analysis showed that 1st VPD affected the late recanalization rate (p < 0.001). Its value was significantly greater in all patients without recanalization (p < 0.001). Receiver operating characteristic curves governed by the univariate analysis showed that the model for late recanalization prediction based on 1st VPD (AUC 0.94 (95%CI: 0.86–1.00) is the most important predictor of late recanalization (p < 0.001). A cut-off point of 10.56% (sensitivity—0.722; specificity—0.979) was confirmed as optimal in a computational fluid dynamics analysis. The CFD results indicate that pressure at the aneurysm wall and residual flow volume (blood volume with mean fluid velocity > 0.01 m/s) within the aneurysmal dome tended to asymptotically decrease when VPD exceeded 10%. Conclusions: High 1st VPD decreases the late recanalization rate in ruptured intracranial aneurysms treated with coil embolization (according to our statistical results > 10.56%). We present an easy intraoperatively calculable predictor which has the potential to be used in clinical practice as a tip to improve clinical outcomes.

scholarly journals Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David R. Rutkowski ◽  
Alejandro Roldán-Alzate ◽  
Kevin M. Johnson
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Velocity Fields ◽  
Dynamics Simulation ◽  
Patient Specific ◽  
4D Flow Mri ◽  
4D Flow ◽  
Flow Phase ◽  
Flow Mri ◽  
Trained Network

AbstractBlood flow metrics obtained with four-dimensional (4D) flow phase contrast (PC) magnetic resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular analysis. However, limitations in both quantitative and qualitative analyses can result from errors inherent to PC MRI. One method that excels in creating low-error, physics-based, velocity fields is computational fluid dynamics (CFD). Augmentation of cerebral 4D flow MRI data with CFD-informed neural networks may provide a method to produce highly accurate physiological flow fields. In this preliminary study, the potential utility of such a method was demonstrated by using high resolution patient-specific CFD data to train a convolutional neural network, and then using the trained network to enhance MRI-derived velocity fields in cerebral blood vessel data sets. Through testing on simulated images, phantom data, and cerebrovascular 4D flow data from 20 patients, the trained network successfully de-noised flow images, decreased velocity error, and enhanced near-vessel-wall velocity quantification and visualization. Such image enhancement can improve experimental and clinical qualitative and quantitative cerebrovascular PC MRI analysis.

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