Methodological inaccuracies in clinical aortic valve severity assessment: insights from computational fluid dynamic modeling of CT-derived aortic valve anatomy

Traeger, Brad James; Srivatsa, Sanjay S.; Beussman, Kevin M.; Wang, Yechun; Suzen, Yildirim; Rybicki, Frank J.; Mazur, Wojciech; Miszalski−Jamka, Tomasz

doi:10.1007/s00162-015-0370-9

Cited by 8 publications

(8 citation statements)

References 63 publications

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“…Due to the larger cross-sectional area, velocity in the AA will be lower, and more kinetic energy is converted back into static pressure. (Traeger et al, 2015).…”

Section: Valve Resistance Indexmentioning

confidence: 99%

Estimation of valvular resistance of segmented aortic valves using computational fluid dynamics

Hoeijmakers

Soto

Waechter-Stehle

et al. 2019

Journal of Biomechanics

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Aortic valve stenosis is associated with an elevated left ventricular pressure and transaortic pressure drop. Clinicians routinely use Doppler ultrasound to quantify aortic valve stenosis severity by estimating this pressure drop from blood velocity. However, this method approximates the peak pressure drop, and is unable to quantify the partial pressure recovery distal to the valve. As pressure drops are flow dependent, it remains difficult to assess the true significance of a stenosis for low-flow low-gradient patients. Recent advances in segmentation techniques enable patient-specific Computational Fluid Dynamics (CFD) simulations of flow through the aortic valve. In this work a simulation framework is presented and used to analyze data of 18 patients. The ventricle and valve are reconstructed from 4D Computed Tomography imaging data. Ventricular motion is extracted from the medical images and used to model ventricular contraction and corresponding blood flow through the valve. Simplifications of the framework are assessed by introducing two simplified CFD models: a truncated time-dependent and a steady-state model. Model simplifications are justified for cases where the simulated pressure drop is above 10 mmHg. Furthermore, we propose a valve resistance index to quantify stenosis severity from simulation results. This index is compared to established metrics for clinical decision making, i.e. blood velocity and valve area. It is found that velocity measurements alone do not adequately reflect stenosis severity. This work demonstrates that combining 4D imaging data and CFD has the potential to provide a physiologically relevant diagnostic metric to quantify aortic valve stenosis severity.

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“…Due to the larger cross-sectional area, velocity in the AA will be lower, and more kinetic energy is converted back into static pressure. (Traeger et al, 2015).…”

Section: Valve Resistance Indexmentioning

confidence: 99%

Estimation of valvular resistance of segmented aortic valves using computational fluid dynamics

Hoeijmakers

Soto

Waechter-Stehle

et al. 2019

Journal of Biomechanics

View full text Add to dashboard Cite

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“…RBC pressure was represented by p s symbol. Restitution coefficient was depicted as e ss , g 0 , ss was radial distribution function which in this configuration was calculated from (8). The diffusion coefficient for granular energy k θ s can be expressed by (7).…”

Section: Numerical Modelmentioning

confidence: 99%

“…In this approach properties of blood are represented by average density and viscosity for plasma and morphotic elements, whereas vessels are represented by rigid walls [5,6]. In literature, some numerical models describe flow within a selected section of the human circulatory system and an additionally implement a coupled lumped model for simulations of both -a part and a whole cardiovascular system [7][8][9]. For the CFD simulations, it is crucial to establish a precise geometry of the flow field.…”

Section: Introductionmentioning

confidence: 99%

Multiphase simulation of blood flow within main thoracic arteries of 8-year-old child with coarctation of the aorta

et al. 2017

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In the research, a numerical Computational Fluid Dynamics (CFD) model of the pulsatile blood flow was created and analysed. A real geometry of aorta and its thoracic branches of an 8-year old patient diagnosed with a congenital heart defect -coarctation of the aorta was used. The inlet boundary condition was implemented as the User Define Function according to measured values of volumetric blood flow. The blood flow was treated as multiphase using Euler-Euler approach. Plasma was set as the primary and dominant fluid phase, with the volume fraction of 0.585. The morphological elements (RBC and WBC) were set as dispersed phases being the remaining volume fraction.

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“…All except for two papers address issues in cardiovascular hemodynamics, and this reflects, to some degree, the large inroads that fluid mechanicians have made into the field of cardiology. Among the six papers in the area of cardiovascular hemodynamics, topics range from the hemodynamics of natural [4,8] and prosthetic valves [1], to fundamental investigations of normal [3,9] and diseased states [7] of the cardiovascular system. The paper of Dillard et al [2] describes a new method for going from videos to CFD ready models and this method is applicable to a wide variety of biological systems.…”

Section: The Papersmentioning

confidence: 99%

Recent developments in multiphysics computational models of physiological flows

Eldredge

Mittal

2016

Theor. Comput. Fluid Dyn.

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A mini-symposium on computational modeling of fluid-structure interactions and other multiphysics in physiological flows was held at the 11th World Congress on Computational Mechanics in July 2014 in Barcelona, Spain. This special issue of Theoretical and Computational Fluid Dynamics contains papers from among the participants of the mini-symposium. The present paper provides an overview of the mini-symposium and the special issue. 1 Overview of the mini-symposium Capabilities for numerical simulations of physiological flows have evolved tremendously in the last decade. This evolution is primarily due to notable advances in computational architecture, numerical methodologies, modeling of the rheology and elastic characteristics of biological materials, and pathways from medical imaging to flow computation. Within the advancement of numerical methodologies, there are two fronts on which developments have been particularly notable. One of these fronts involves strategies for the immersion of complex geometries in simple static grids. These so-called immersed boundary methods, which were first introduced in the 1970s but have seen a resurgence in the last decade, have gone far to address the challenges of simulation with complex moving boundaries, which are particularly troublesome in physiology. On the other front, researchers are devising new methods for coupling the fluid dynamics with other relevant physics: the structural dynamics of soft tissues, the electrophysiology of the heart muscles, and the biochemistry of fluid components, such as thrombi or aerosol particulates. The developments on these fronts have opened a wide new world for computational mechanics. Many of these advances have been fueled by productive partnerships that have emerged between engineers, physicians, and biomedical researchers, to the point that simulations are even starting to provide guidance on patient-specific diagnosis and surgical planning. The authors of this introductory paper organized a mini-symposium on the topic of multiphysical modeling of physiological flows at the 11th World Congress on Computational Mechanics, held in Barcelona in July 2014, in which the results of several such partnerships were presented over the course of two sessions. The mini-symposium brought together several researchers who are making novel contributions to the computation of flows and associated multiphysics problems in a variety of physiological contexts. The twofold objective of the mini-symposium was to highlight the state of the art in modeling of physiological flows and to provide

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Methodological inaccuracies in clinical aortic valve severity assessment: insights from computational fluid dynamic modeling of CT-derived aortic valve anatomy

Cited by 8 publications

References 63 publications

Estimation of valvular resistance of segmented aortic valves using computational fluid dynamics

Estimation of valvular resistance of segmented aortic valves using computational fluid dynamics

Multiphase simulation of blood flow within main thoracic arteries of 8-year-old child with coarctation of the aorta

Recent developments in multiphysics computational models of physiological flows

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