scholarly journals Bulk Flow and Near Wall Hemodynamics of the Rabbit Aortic Arch and Descending Thoracic Aorta: A 4D PC-MRI Derived Computational Fluid Dynamics Study

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
D. S. Molony ◽  
J. Park ◽  
L. Zhou ◽  
C. C. Fleischer ◽  
H. Y. Sun ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Shear Stress ◽  
Magnetic Resonance ◽  
Aortic Arch ◽  
Shape Analysis ◽  
Helical Flow ◽  
Flow Index ◽  
Statistical Shape Analysis ◽  
Statistical Shape

Animal models offer a flexible experimental environment for studying atherosclerosis. The mouse is the most commonly used animal, however, the underlying hemodynamics in larger animals such as the rabbit are far closer to that of humans. The aortic arch is a vessel with complex helical flow and highly heterogeneous shear stress patterns which may influence where atherosclerotic lesions form. A better understanding of intraspecies flow variation and the impact of geometry on flow may improve our understanding of where disease forms. In this work, we use magnetic resonance angiography (MRA) and 4D phase contrast magnetic resonance imaging (PC-MRI) to image and measure blood velocity in the rabbit aortic arch. Measured flow rates from the PC-MRI were used as boundary conditions in computational fluid dynamics (CFD) models of the arches. Helical flow, cross flow index (CFI), and time-averaged wall shear stress (TAWSS) were determined from the simulated flow field. Both traditional geometric metrics and shape modes derived from statistical shape analysis were analyzed with respect to flow helicity. High CFI and low TAWSS were found to colocalize in the ascending aorta and to a lesser extent on the inner curvature of the aortic arch. The Reynolds number was linearly associated with an increase in helical flow intensity (R = 0.85, p < 0.05). Both traditional and statistical shape analyses correlated with increased helical flow symmetry. However, a stronger correlation was obtained from the statistical shape analysis demonstrating its potential for discerning the role of shape in hemodynamic studies.

6125High wall shear stress predicts plaque rupture of the aortic arch: computational fluid dynamics model and non-obstructive general angioscopy study

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
K Kojima ◽  
T Hiro ◽  
Y Ebuchi ◽  
T Morikawa ◽  
S Migita ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Shear Stress ◽  
Wall Shear Stress ◽  
Aortic Arch ◽  
Plaque Rupture ◽  
Aortic Atherosclerosis ◽  
3D Ct ◽  
Significant Difference ◽  
Wall Shear

Abstract Background Wall shear stress (WSS) has been considered as a major determinant of aortic atherosclerosis. Recently, non-obstructive general angioscopy (NOGA) was developed to be able to visualize a variety of its atherosclerotic pathology, including in vivo ruptured plaque (RP) in the aorta. We, therefore, investigated the relationship between NOGA derived RP in the aortic arch and the stereographic distribution of WSS by using computational fluid dynamics modeling (CFD) on three-dimensional CT angiography (3D-CT). Methods We investigated 30 consecutive patients who underwent 3D-CT before and NOGA during coronary angiography. WSS in the aortic arch was measured with an application of CFD based on finite element method by using uniform inlet and outlet flow conditions. Aortic RP was detected by NOGA. Results The maximum and mean values of WSS were 67.2±29.2 Pa and 2.4±0.6 Pa. A total of 18 RPs was detected by NOGA. The patients with a distinct RP showed a significantly higher maximum WSS in the whole aortic arch, and the greater and lesser curvature of the aortic arch than those without it (73.3±29.0 Pa vs 50.4±15.2 Pa, p=0.035, 95.0±27.5 Pa vs 42.8±25.2 Pa, p=0.003, 70.8±29.3 Pa vs 46.1±11.9 Pa, p=0.013, respectively), whereas there was no significant difference in the mean WSS between those with and without it. In a multivariate analysis, the maximum value of WSS was an independent predictor of RP in the aortic arch (odds ratio 1.05, 95% confidence interval 1.01–1.13, p=0.019). Representative picture of WSS and NOGA Conclusions Aortic RP detected by NOGA was strongly associated with the higher maximum WSS in the aortic arch derived by CFD using 3D-CT. Maximum WSS may explain the underlying mechanism of not only aortic atherosclerosis, but also aortic RP.

scholarly journals ESTIMATION OF WALL SHEAR STRESS USING 4D FLOW CARDIOVASCULAR MRI AND COMPUTATIONAL FLUID DYNAMICS

2016 ◽  
Vol 17 (03) ◽  
pp. 1750046 ◽  
Author(s):  
E. SOUDAH ◽  
J. CASACUBERTA ◽  
P. J. GAMEZ-MONTERO ◽  
J. S. PÉREZ ◽  
M. RODRÍGUEZ-CANCIO ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Blood Flow ◽  
Shear Stress ◽  
Cardiovascular Magnetic Resonance ◽  
Magnetic Resonance ◽  
Wall Shear Stress ◽  
Thoracic Aorta ◽  
4D Flow ◽  
Wall Shear

In the last few years, wall shear stress (WSS) has arisen as a new diagnostic indicator in patients with arterial disease. There is a substantial evidence that the WSS plays a significant role, together with hemodynamic indicators, in initiation and progression of the vascular diseases. Estimation of WSS values, therefore, may be of clinical significance and the methods employed for its measurement are crucial for clinical community. Recently, four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) has been widely used in a number of applications for visualization and quantification of blood flow, and although the sensitivity to blood flow measurement has increased, it is not yet able to provide an accurate three-dimensional (3D) WSS distribution. The aim of this work is to evaluate the aortic blood flow features and the associated WSS by the combination of 4D flow cardiovascular magnetic resonance (4D CMR) and computational fluid dynamics technique. In particular, in this work, we used the 4D CMR to obtain the spatial domain and the boundary conditions needed to estimate the WSS within the entire thoracic aorta using computational fluid dynamics. Similar WSS distributions were found for cases simulated. A sensitivity analysis was done to check the accuracy of the method. 4D CMR begins to be a reliable tool to estimate the WSS within the entire thoracic aorta using computational fluid dynamics. The combination of both techniques may provide the ideal tool to help tackle these and other problems related to wall shear estimation.

Efficiency prediction for storage chambers using computational fluid dynamics

1996 ◽  
Vol 33 (9) ◽  
pp. 163-170 ◽  
Author(s):  
Virginia R. Stovin ◽  
Adrian J. Saul
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Shear Stress ◽  
Particle Tracking ◽  
Critical Value ◽  
Complex Geometries ◽  
Bed Shear Stress ◽  
Breadth Ratio ◽  
Computational Fluid Dynamics Cfd ◽  
Simulated Flow

Research was undertaken in order to identify possible methodologies for the prediction of sedimentation in storage chambers based on computational fluid dynamics (CFD). The Fluent CFD software was used to establish a numerical model of the flow field, on which further analysis was undertaken. Sedimentation was estimated from the simulated flow fields by two different methods. The first approach used the simulation to predict the bed shear stress distribution, with deposition being assumed for areas where the bed shear stress fell below a critical value (τcd). The value of τcd had previously been determined in the laboratory. Efficiency was then calculated as a function of the proportion of the chamber bed for which deposition had been predicted. The second method used the particle tracking facility in Fluent and efficiency was calculated from the proportion of particles that remained within the chamber. The results from the two techniques for efficiency are compared to data collected in a laboratory chamber. Three further simulations were then undertaken in order to investigate the influence of length to breadth ratio on chamber performance. The methodology presented here could be applied to complex geometries and full scale installations.

Deformation-based Statistical Shape Analysis of the Corpus Callosum in Mild Cognitive Impairment and Alzheimer’s Disease

2018 ◽  
Vol 15 (12) ◽  
pp. 1151-1160 ◽  
Author(s):  
Zihan Jiang ◽  
Huilin Yang ◽  
Xiaoying Tang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Corpus Callosum ◽  
Shape Analysis ◽  
Disease Assessment ◽  
Statistical Shape Analysis ◽  
Superior Part ◽  
Statistical Shape

Objective: In this study, we investigated the influence that the pathology of Alzheimer’s disease (AD) exerts upon the corpus callosum (CC) using a total of 325 mild cognitive impairment (MCI) subjects, 155 AD subjects, and 185 healthy control (HC) subjects. Method: Regionally-specific morphological CC abnormalities, as induced by AD, were quantified using a large deformation diffeomorphic metric curve mapping based statistical shape analysis pipeline. We also quantified the association between the CC shape phenotype and two cognitive measures; the Mini Mental State Examination (MMSE) and the Alzheimer’s Disease Assessment Scale-Cognitive Behavior Section (ADAS-cog). To identify AD-relevant areas, CC was sub-divided into three subregions; the genu, body, and splenium (gCC, bCC, and sCC). Results: We observed significant shape compressions in AD relative to that in HC, mainly concentrated on the superior part of CC, across all three sub-regions. The HC-vs-MCI shape abnormalities were also concentrated on the superior part, but mainly occurred on bCC and sCC. The significant MCI-vs-AD shape differences, however, were only detected in part of sCC. In the shape-cognition association, significant negative correlations to ADAS-cog were detected for shape deformations at regions belonging to gCC and sCC and significant positive correlations to MMSE at regions mainly belonging to sCC. Conclusion: Our results suggest that the callosal shape deformation patterns, especially those of sCC, linked tightly to the cognitive decline in AD, and are potentially a powerful biomarker for monitoring the progression of AD.

Sign in / Sign up

Export Citation Format

Share Document