scholarly journals The Impact of Cardiac Motion on Aortic Valve Flow Used in Computational Simulations of the Thoracic Aorta

2016 ◽  
Vol 138 (9) ◽  
Author(s):  
David C. Wendell ◽  
Margaret M. Samyn ◽  
Joseph R. Cava ◽  
Mary M. Krolikowski ◽  
John F. LaDisa
Keyword(s):  
Blood Flow ◽  
Aortic Valve ◽  
Coarctation Of The Aorta ◽  
Outer Wall ◽  
Cardiac Motion ◽  
Patient Specific ◽  
Flow Measurements ◽  
Computational Fluid Dynamics Cfd ◽  
Blood Flow Measurements ◽  
The Impact

Advancements in image-based computational modeling are producing increasingly more realistic representations of vasculature and hemodynamics, but so far have not compensated for cardiac motion when imposing inflow boundary conditions. The effect of cardiac motion on aortic flow is important when assessing sequelae in this region including coarctation of the aorta (CoA) or regurgitant fraction. The objective of this investigation was to develop a method to assess and correct for the influence of cardiac motion on blood flow measurements through the aortic valve (AoV) and to determine its impact on patient-specific local hemodynamics quantified by computational fluid dynamics (CFD). A motion-compensated inflow waveform was imposed into the CFD model of a patient with repaired CoA that accounted for the distance traveled by the basal plane during the cardiac cycle. Time-averaged wall shear stress (TAWSS) and turbulent kinetic energy (TKE) values were compared with CFD results of the same patient using the original waveform. Cardiac motion resulted in underestimation of flow during systole and overestimation during diastole. Influences of inflow waveforms on TAWSS were greatest along the outer wall of the ascending aorta (AscAo) (∼30 dyn/cm2). Differences in TAWSS were more pronounced than those from the model creation or mesh dependence aspects of CFD. TKE was slightly higher for the motion-compensated waveform throughout the aortic arch. These results suggest that accounting for cardiac motion when quantifying blood flow through the AoV can lead to different conclusions for hemodynamic indices, which may be important if these results are ultimately used to predict patient outcomes.

PATIENT-SPECIFIC SIMULATION MODEL TO RESEARCH THE IMPACT OF AORTIC VALVE PROSTHESES ON BLOOD FLOW

2008 ◽  
Vol 41 ◽  
pp. S242
Author(s):  
Markus Bongert ◽  
Marius Geller ◽  
Werner Pennekamp ◽  
Daniela Roggenland ◽  
Volkmar Nicolas
Keyword(s):  
Blood Flow ◽  
Aortic Valve ◽  
Simulation Model ◽  
Patient Specific ◽  
Valve Prostheses ◽  
The Impact

The impact of prompt gamma compensation on myocardial blood flow measurements with rubidium-82 dynamic PET

2016 ◽  
Vol 25 (2) ◽  
pp. 596-605 ◽  
Author(s):  
Ian S. Armstrong ◽  
Matthew J. Memmott ◽  
Christine M. Tonge ◽  
Parthiban Arumugam
Keyword(s):  
Blood Flow ◽  
Myocardial Blood Flow ◽  
Prompt Gamma ◽  
Dynamic Pet ◽  
Flow Measurements ◽  
Blood Flow Measurements ◽  
The Impact ◽  
Rubidium 82

scholarly journals From imaging to hemodynamics – how reconstruction kernels influence the blood flow predictions in intracranial aneurysms

2016 ◽  
Vol 2 (1) ◽  
pp. 679-683 ◽  
Author(s):  
Sylvia Glaßer ◽  
Philipp Berg ◽  
Samuel Voß ◽  
Steffen Serowy ◽  
Gabor Janiga ◽  
...  
Keyword(s):  
Blood Flow ◽  
Intracranial Aneurysms ◽  
Medical Community ◽  
Patient Specific ◽  
Therapy Planning ◽  
Rotational Angiography ◽  
Mean Velocity ◽  
3D Images ◽  
Computational Fluid Dynamics Cfd ◽  
The Impact

AbstractComputational fluid dynamics (CFD) is increasingly used by biomedical engineering groups to understand and predict the blood flow within intracranial aneurysms and support the physician during therapy planning. However, due to various simplifications, its acceptance remains limited within the medical community. To quantify the influence of the reconstruction kernels employed for reconstructing 3D images from rotational angiography data, different kernels are applied to four datasets with patient-specific intracranial aneurysms. Sharp, normal and smooth reconstructions were evaluated. Differences of the resulting 24 segmentations and the impact on the hemodynamic predictions are quantified to provide insights into the expected error ranges. A comparison of the segmentations yields strong differences regarding vessel branches and diameters. Further, sharp kernels lead to smaller ostium areas than smooth ones. Analyses of hemodynamic predictions reveal a clear time and space dependency, while mean velocity deviations range from 3.9 to 8%. The results reveal a strong influence of reconstruction kernels on geometrical aneurysm models and the subsequent hemodynamic parameters. Thus, patient-specific blood flow predictions require a carefully selected reconstruction kernel and appropriate recommendations need to be formulated.

scholarly journals Model Verification and Error Sensitivity of Turbulence-Related Tensor Characteristics in Pulsatile Blood Flow Simulations

Fluids ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Magnus Andersson ◽  
Matts Karlsson
Keyword(s):  
Blood Flow ◽  
Model Verification ◽  
Patient Specific ◽  
Flow Modeling ◽  
Spatiotemporal Resolution ◽  
Flow Simulations ◽  
Phase Averaging ◽  
Confidence Levels ◽  
Cardiovascular Flow ◽  
Computational Fluid Dynamics Cfd

Model verification, validation, and uncertainty quantification are essential procedures to estimate errors within cardiovascular flow modeling, where acceptable confidence levels are needed for clinical reliability. While more turbulent-like studies are frequently observed within the biofluid community, practical modeling guidelines are scarce. Verification procedures determine the agreement between the conceptual model and its numerical solution by comparing for example, discretization and phase-averaging-related errors of specific output parameters. This computational fluid dynamics (CFD) study presents a comprehensive and practical verification approach for pulsatile turbulent-like blood flow predictions by considering the amplitude and shape of the turbulence-related tensor field using anisotropic invariant mapping. These procedures were demonstrated by investigating the Reynolds stress tensor characteristics in a patient-specific aortic coarctation model, focusing on modeling-related errors associated with the spatiotemporal resolution and phase-averaging sampling size. Findings in this work suggest that attention should also be put on reducing phase-averaging related errors, as these could easily outweigh the errors associated with the spatiotemporal resolution when including too few cardiac cycles. Also, substantially more cycles are likely needed than typically reported for these flow regimes to sufficiently converge the phase-instant tensor characteristics. Here, higher degrees of active fluctuating directions, especially of lower amplitudes, appeared to be the most sensitive turbulence characteristics.

Clinical Utility of Quantitative Cerebral Blood Flow Measurements during Internal Carotid Artery Test Occlusions

Neurosurgery ◽  
2002 ◽  
Vol 50 (5) ◽  
pp. 996-1005 ◽  
Author(s):  
Randolph S. Marshall ◽  
Ronald M. Lazar ◽  
William L. Young ◽  
Robert A. Solomon ◽  
Shailendra Joshi ◽  
...  
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Carotid Artery ◽  
Internal Carotid Artery ◽  
Clinical Utility ◽  
Internal Carotid ◽  
Flow Measurements ◽  
Blood Flow Measurements

Doppler Blood Flow Measurements in Chronic Animal Preparations

1975 ◽  
Vol 49 (3) ◽  
pp. 17P-17P
Author(s):  
Maurice R. Cross ◽  
Clive Weller ◽  
E. B. Raftery
Keyword(s):  
Blood Flow ◽  
Flow Measurements ◽  
Blood Flow Measurements
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