Computational hemodynamics in the human aorta: A computational fluid dynamics study of three cases with patient-specific geometries and inflow rates

2008 ◽  
Vol 16 (5) ◽  
pp. 343-354 ◽  
Author(s):  
Christof Karmonik ◽  
Jean X. Bismuth ◽  
Mark G. Davies ◽  
Alan B. Lumsden
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Patient Specific ◽  
Human Aorta ◽  
Computational Hemodynamics

scholarly journals Enhancing Magnetic Resonance Imaging With Computational Fluid Dynamics

2019 ◽  
Vol 2 (4) ◽  
Author(s):  
Giacomo Annio ◽  
Ryo Torii ◽  
Ben Ariff ◽  
Declan P. O'Regan ◽  
Vivek Muthurangu ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Blood Flow ◽  
Magnetic Resonance ◽  
Patient Specific ◽  
Human Aorta ◽  
Resonance Imaging ◽  
4D Flow ◽  
4D Flow Cmr

Abstract The analysis of the blood flow in the great thoracic arteries does provide valuable information about the cardiac function and can diagnose the potential development of vascular diseases. Flow-sensitive four-dimensional flow cardiovascular magnetic resonance imaging (4D flow CMR) is often used to characterize patients' blood flow in the clinical environment. Nevertheless, limited spatial and temporal resolution hinders a detailed assessment of the hemodynamics. Computational fluid dynamics (CFD) could expand this information and, integrated with experimental velocity field, enable to derive the pressure maps. However, the limited resolution of the 4D flow CMR and the simplifications of CFD modeling compromise the accuracy of the computed flow parameters. In this article, a novel approach is proposed, where 4D flow CMR and CFD velocity fields are integrated synergistically to obtain an enhanced MR imaging (EMRI). The approach was first tested on a two-dimensional (2D) portion of a pipe, to understand the behavior of the parameters of the model in this novel framework, and afterwards in vivo, to apply it to the analysis of blood flow in a patient-specific human aorta. The outcomes of EMRI are assessed by comparing the computed velocities with the experimental one. The results demonstrate that EMRI preserves flow structures while correcting for experimental noise. Therefore, it can provide better insights into the hemodynamics of cardiovascular problems, overcoming the limitations of MRI and CFD, even when considering a small region of interest. EMRI confirmed its potential to provide more accurate noninvasive estimation of major cardiovascular risk predictors (e.g., flow patterns, endothelial shear stress) and become a novel diagnostic tool.

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.

Abstract TMP29: Prediction of Wall-thinning Area in Unruptured Intracerebral Aneurysms by Computational Fluid Dynamics

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Yoshifumi Hayashi ◽  
Takanobu Yagi ◽  
Yasutaka Tobe ◽  
Yuki Iwabuchi ◽  
Momoko Yamanashi ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Wall Pressure ◽  
Patient Specific ◽  
Unruptured Aneurysms ◽  
Divergent Flow ◽  
Wall Thinning ◽  
Risk Of Rupture ◽  
Angiographic Data ◽  
Intracerebral Aneurysm

[Background and purpose] During a clipping surgery, an unruptured intracerebral aneurysm often presented a spatially-localized red-colored "wall-thinning" area. The wall thinning was believed to be related with the risk of rupture. The present aim is given to investigate the predictability of a wall thinning area using computational fluid dynamics (CFD). [Method] We chose 16 unruptured aneurysms (12 MCA, 4 ICA) with clipping surgery and 24 wall-thinning areas were detected from the operation video. CFD study was carried out using patient-specific angiographic data. The wall shear stress (WSS) and the wall pressure were evaluated. [Results] The WSS magnitude was found to be uncorrelated with wall thinning. On the other hand, 20 wall-thinning areas (83%) exhibited a presence of “flow impingement”, which was defined to give the spatial variation of the WSS vector to be divergent with the local elevation of the wall pressure. From CFD, 27 flow impingements were detected and classified according to the degree of divergence. Seven impingements are full-divergent and all of them (100%) are located in the wall thinning areas. The remaining 20 impingements were partial-divergent and 13 impingements of them (65%) were located in the wall thinning areas. A classification of full-/partial-divergent flow impingement was statistically significant for the prediction of wall-thinning areas (P<0.01). [Conclusions] The full-divergent flow impingement was found to be a reliable predictor of the wall thinning area in unruptured intracerebral aneurysms. The present results demonstrated the malignant nature of flow impingement for promoting the thinning of aneurysmal walls.

scholarly journals A Combined Computational Fluid Dynamics and Arterial Spin Labeling MRI Modeling Strategy to Quantify Patient-Specific Cerebral Hemodynamics in Cerebrovascular Occlusive Disease

2021 ◽  
Vol 9 ◽  
Author(s):  
Jonas Schollenberger ◽  
Nicholas H. Osborne ◽  
Luis Hernandez-Garcia ◽  
C. Alberto Figueroa
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Blood Supply ◽  
Arterial Spin Labeling ◽  
Spin Labeling ◽  
Cerebral Hemodynamics ◽  
Occlusive Disease ◽  
Patient Specific ◽  
Cfd Model ◽  
Cerebrovascular Occlusive Disease

Cerebral hemodynamics in the presence of cerebrovascular occlusive disease (CVOD) are influenced by the anatomy of the intracranial arteries, the degree of stenosis, the patency of collateral pathways, and the condition of the cerebral microvasculature. Accurate characterization of cerebral hemodynamics is a challenging problem. In this work, we present a strategy to quantify cerebral hemodynamics using computational fluid dynamics (CFD) in combination with arterial spin labeling MRI (ASL). First, we calibrated patient-specific CFD outflow boundary conditions using ASL-derived flow splits in the Circle of Willis. Following, we validated the calibrated CFD model by evaluating the fractional blood supply from the main neck arteries to the vascular territories using Lagrangian particle tracking and comparing the results against vessel-selective ASL (VS-ASL). Finally, the feasibility and capability of our proposed method were demonstrated in two patients with CVOD and a healthy control subject. We showed that the calibrated CFD model accurately reproduced the fractional blood supply to the vascular territories, as obtained from VS-ASL. The two patients revealed significant differences in pressure drop over the stenosis, collateral flow, and resistance of the distal vasculature, despite similar degrees of clinical stenosis severity. Our results demonstrated the advantages of a patient-specific CFD analysis for assessing the hemodynamic impact of stenosis.

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