scholarly journals Influence of Hemodynamic Factors on Rupture of Intracranial Aneurysms: Patient-Specific 3D Mirror Aneurysms Model Computational Fluid Dynamics Simulation

2011 ◽  
Vol 32 (7) ◽  
pp. 1255-1261 ◽  
Author(s):  
G. Lu ◽  
L. Huang ◽  
X.L. Zhang ◽  
S.Z. Wang ◽  
Y. Hong ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Intracranial Aneurysms ◽  
Dynamics Simulation ◽  
Patient Specific ◽  
Computational Fluid Dynamics Simulation

The importance of blood rheology in patient-specific computational fluid dynamics simulation of stenotic carotid arteries

2020 ◽  
Vol 19 (5) ◽  
pp. 1477-1490 ◽  
Author(s):  
Jessica Benitez Mendieta ◽  
Davide Fontanarosa ◽  
Jiaqiu Wang ◽  
Phani Kumari Paritala ◽  
Tim McGahan ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Carotid Arteries ◽  
Blood Rheology ◽  
Dynamics Simulation ◽  
Patient Specific ◽  
Computational Fluid Dynamics Simulation

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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