Preliminary study of hemodynamic distribution in patient-specific stenotic carotid bifurcation by image-based computational fluid dynamics

2008 ◽  
Vol 49 (5) ◽  
pp. 558-565 ◽  
Author(s):  
Y.-J. Xue ◽  
P.-Y. Gao ◽  
Q. Duan ◽  
Y. Lin ◽  
C.-B. Dai
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Carotid Bifurcation ◽  
Patient Specific ◽  
Preliminary Study

Analysis of Patient-Specific Carotid Bifurcation Models Using Computational Fluid Dynamics

2011 ◽  
Vol 1 (2) ◽  
pp. 116-125 ◽  
Author(s):  
Jiyuan Tu ◽  
Kelvin K. L. Wong ◽  
Sherman C. P. Cheung ◽  
Richard Beare ◽  
Thanh Phan
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Carotid Bifurcation ◽  
Patient Specific

scholarly journals Effect of Inlet Velocity Profiles on Patient-Specific Computational Fluid Dynamics Simulations of the Carotid Bifurcation

2012 ◽  
Vol 134 (5) ◽  
Author(s):  
Ian C. Campbell ◽  
Jared Ries ◽  
Saurabh S. Dhawan ◽  
Arshed A. Quyyumi ◽  
W. Robert Taylor ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Velocity Profile ◽  
Carotid Bifurcation ◽  
Velocity Profiles ◽  
Patient Specific ◽  
Flow Waveform ◽  
Specific Flow ◽  
Inlet Velocity ◽  
The Mean

Patient-specific computational fluid dynamics (CFD) is a powerful tool for researching the role of blood flow in disease processes. Modern clinical imaging technology such as MRI and CT can provide high resolution information about vessel geometry, but in many situations, patient-specific inlet velocity information is not available. In these situations, a simplified velocity profile must be selected. We studied how idealized inlet velocity profiles (blunt, parabolic, and Womersley flow) affect patient-specific CFD results when compared to simulations employing a “reference standard” of the patient’s own measured velocity profile in the carotid bifurcation. To place the magnitude of these effects in context, we also investigated the effect of geometry and the use of subject-specific flow waveform on the CFD results. We quantified these differences by examining the pointwise percent error of the mean wall shear stress (WSS) and the oscillatory shear index (OSI) and by computing the intra-class correlation coefficient (ICC) between axial profiles of the mean WSS and OSI in the internal carotid artery bulb. The parabolic inlet velocity profile produced the most similar mean WSS and OSI to simulations employing the real patient-specific inlet velocity profile. However, anatomic variation in vessel geometry and the use of a nonpatient-specific flow waveform both affected the WSS and OSI results more than did the choice of inlet velocity profile. Although careful selection of boundary conditions is essential for all CFD analysis, accurate patient-specific geometry reconstruction and measurement of vessel flow rate waveform are more important than the choice of velocity profile. A parabolic velocity profile provided results most similar to the patient-specific velocity profile.

The Effect of Inlet Flow Profile Simplifications in Computational Fluid Dynamics of the Carotid Bifurcation

2011 ◽  
Author(s):  
Jared Ries ◽  
Ian C. Campbell ◽  
Saurabh S. Dhawan ◽  
Arshed A. Quyyumi ◽  
W. Robert Taylor ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Velocity Profile ◽  
Carotid Bifurcation ◽  
Patient Specific ◽  
Flow Profile ◽  
Inlet Flow ◽  
Measured Velocity ◽  
Inlet Velocity ◽  
Parabolic Flow

Computational fluid dynamics (CFD) is emerging as a powerful tool for researching the role of blood flow in disease processes. Modern clinical imaging technology such as magnetic resonance (MR) angiography and computed tomography (CT) can provide very high resolution information about the geometry of patients’ vasculature for such modeling. However, in many situations, patient-specific inlet velocity information is not available. In these situations, a simplified velocity profile must be selected. In this study, we sought to identify how idealized inlet velocity profiles (blunt flow, parabolic flow, and Womersley flow) affect patient-specific CFD results when compared to simulations employing the real measured velocity profile for each patient. Focusing on the carotid bifurcation, a site prone to atherosclerosis because of its branching geometry and oscillatory flow patterns, we investigated the effect of inlet flow assumptions on hemodynamic parameters known to be associated with atherosclerosis and vascular disease, namely mean wall shear stress (WSS) and oscillatory shear index (OSI) [1].

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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