Computational Fluid Dynamics Analysis of Upper Airway Reconstructed from Magnetic Resonance Imaging Data

2008 ◽  
Vol 117 (4) ◽  
pp. 303-309 ◽  
Author(s):  
Mihai Mihaescu ◽  
Shanmugam Murugappan ◽  
Ephraim Gutmark ◽  
Lane F. Donnelly ◽  
Siddarth Khosla ◽  
...  
Author(s):  
Joy Edlin ◽  
Justin Nowell ◽  
Chris Arthurs ◽  
Alberto Figueroa ◽  
Marjan Jahangiri

Abstract Background Modern imaging techniques provide evermore-detailed anatomical and physiological information for use in computational fluid dynamics to predict the behaviour of physiological phenomena. Computer modelling can help plan suitable interventions. Our group used magnetic resonance imaging and computational fluid dynamics to study the haemodynamic variables in the ascending aorta in patients with bicuspid aortic valve before and after isolated tissue aortic valve replacement. Computer modelling requires turning a physiological model into a mathematical one, solvable by equations that undergo multiple iterations in four dimensions. Creating these models involves several steps with manual inputs, making the process prone to errors and limiting its inter- and intra-operator reproducibility. Despite these challenges we created computational models for each patient to study ascending aorta blood flow before and after surgery. Method Magnetic resonance imaging provided the anatomical and velocity data required for the blood flow simulation. Patient-specific in- and outflow boundary conditions were used for the computational fluid dynamics analysis. Results Haemodynamic variables pertaining to blood flow pattern and derived from the magnetic resonance imaging data were calculated. However, we encountered problems in our multi-step methodology, most notably processing the flow data. This meant that other variables requiring computation with computational fluid dynamics could not be calculated. Conclusion Creating a model for computational fluid dynamics analysis is as complex as the physiology under scrutiny. We discuss some of the difficulties associated with creating such models, along with suggestions for improvements in order to yield reliable and beneficial results.


Author(s):  
Yong He ◽  
Christi M. Terry ◽  
Scott A. Berceli ◽  
Alfred K. Cheung ◽  
Yan-Ting E. Shiu

An arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis in end-stage renal disease. However, 60% of AVFs fail to achieve sufficient lumen dilation to allow adequate blood flow for chronic dialysis [1]. Although hemodynamics is likely an important modulator of AVF maturation and remodeling, the AVF hemodynamic spatial distribution profiles and their relationship with AVF maturation and remodeling are unclear [2]. Based on data collected from magnetic resonance imaging (MRI) of an AVF and computational fluid dynamics (CFD) simulations, we developed a protocol for longitudinal (over time) and noninvasive monitoring of geometry and hemodynamics of human AVF.


Author(s):  
Asterios Toutios ◽  
Tanner Sorensen ◽  
Krishna Somandepalli ◽  
Rachel Alexander ◽  
Shrikanth S. Narayanan

2019 ◽  
Vol 13 ◽  
Author(s):  
Christoph Vogelbacher ◽  
Miriam H. A. Bopp ◽  
Verena Schuster ◽  
Peer Herholz ◽  
Andreas Jansen ◽  
...  

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