CFD mesh generation for biological flows: Geometry reconstruction using diagnostic images

A new thrust in the use of CFD techniques for simulation of biological flows has necessitated the demand for robust grid generation techniques to characterize the complex geometries. While the techniques of image manipulation required are simple, most researchers in this field use proprietary 3rd party software for image manipulation and grid generation. In the current study, we propose a simple MATLAB based grid generation techniques suitable for CFD studies of external and internal biological flows such as blood flow and respiration and flows around the human body. As an example, the flow inside two specific intracranial aneurysms is modeled by generating CFD grids from 3D rotational angiography images. Specific issues of modeling, such as boundary conditions and location of flow inlets and outlets, in relation to the reconstructed geometry are discussed. The reconstructed arterial geometry including the aneurysm matches the visual representation generated by the angiogram software (Leonardo software). The calculated CFD flow patterns also show a good correlation to the flow visualization presented by the Leonardo software. Areas of high pressure and wall shear stress are identified. The same technique is also used to generate the CFD grid of a human trachea to study the particle dispersion patterns during a human cough cycle. The fluid is modeled using an actual human cough signal with the particles simulating the influenza virus. The flow pattern out of the mouth along with the dispersion pattern of the particles is validated against similar human experimental studies to track the spread of the disease through cough. Work is also currently underway to use the present grid generation program to construct a superficial mesh of the human body from MRI/CAT scan images of cadavers. The goal is to build an accurate and scalable model of the human body surface with articulate joints which can be posed in any environment to model the air flow patterns around the body.

[1]  M Dosemeci,et al.  Occupation and the risk of laryngeal cancer in Turkey. , 2001, Scandinavian journal of work, environment & health.

[2]  C Renotte,et al.  Numerical 3D analysis of oscillatory flow in the time-varying laryngeal channel. , 2000, Journal of biomechanics.

[3]  J. Rossmann,et al.  Influence of Shape on Saccular Aneurysm Hemodynamics and Risk of Rupture , 2006, Proceedings of the IEEE 32nd Annual Northeast Bioengineering Conference.

[4]  Alejandro F. Frangi,et al.  Efficient pipeline for image-based patient-specific analysis of cerebral aneurysm hemodynamics: technique and sensitivity , 2005, IEEE Transactions on Medical Imaging.

[5]  D. Holdsworth,et al.  Image-based computational simulation of flow dynamics in a giant intracranial aneurysm. , 2003, AJNR. American journal of neuroradiology.

[6]  Shigeo Wada,et al.  Growth of Intracranial Aneurysms Arised from Curved Vessels under the Influence of Elevated Wall Shear Stress-A Computer Simulation Study , 2004 .

[7]  Ismail Celik,et al.  A Simple Model for Fluid Flow and Particle Motion Inside the Human Larynx , 2004 .

[8]  Alastair J. Martin,et al.  Estimating the Hemodynamic Impact of Interventional Treatments of Aneurysms: Numerical Simulation with Experimental Validation: Technical Case Report , 2006, Neurosurgery.

[9]  W. M. Blackshear,et al.  Laser Doppler anemometer measurements of pulsatile flow in a model carotid bifurcation: Ku DN, Giddens DP. J Biomech 1987;20:407–421 , 1988 .

[10]  R. Metcalfe The promise of computational fluid dynamics as a tool for delineating therapeutic options in the treatment of aneurysms. , 2003, AJNR. American journal of neuroradiology.

[11]  Alastair J. Martin,et al.  Computational approach to quantifying hemodynamic forces in giant cerebral aneurysms. , 2003, AJNR. American journal of neuroradiology.

[12]  Thomas J. R. Hughes,et al.  Finite element modeling of blood flow in arteries , 1998 .

[13]  Alejandro F. Frangi,et al.  CFD Analysis Incorporating the Influence of Wall Motion: Application to Intracranial Aneurysms , 2006, MICCAI.

[14]  X. Li,et al.  Convective mass transfer at the carotid bifurcation. , 1997, Journal of biomechanics.

[15]  N J Pelc,et al.  Visualization of hemodynamics in a silicon aneurysm model using time-resolved, 3D, phase-contrast MRI. , 2006, AJNR. American journal of neuroradiology.

[16]  Ismail Celik,et al.  Dispersion Study in a Giant Intracranial Aneurysm Using Computational Fluid Dynamics Techniques , 2007 .

[17]  Ralph R. Martin,et al.  Reverse engineering of geometric models - an introduction , 1997, Comput. Aided Des..

[18]  D. Ku,et al.  Pulsatile velocity measurements in a model of the human abdominal aorta under resting conditions. , 1994, Journal of biomechanical engineering.

[19]  Rainald Löhner,et al.  From Medical Images to CFD Meshes , 1999, IMR.

[20]  D. Ku,et al.  Laser Doppler anemometer measurements of pulsatile flow in a model carotid bifurcation. , 1987, Journal of biomechanics.

[21]  Michael M. Resch,et al.  Three-dimensional numerical analysis of pulsatile flow and wall shear stress in the carotid artery bifurcation. , 1991, Journal of biomechanics.

[22]  N. Alberto Borghese,et al.  A portable modular system for automatic acquisition of 3D objects , 2000, IEEE Trans. Instrum. Meas..

[23]  K. Katada,et al.  Magnitude and Role of Wall Shear Stress on Cerebral Aneurysm: Computational Fluid Dynamic Study of 20 Middle Cerebral Artery Aneurysms , 2004, Stroke.

[24]  V. T. Turitto,et al.  Hemodynamics of intracranial lateral aneurysms : flow simulation studies , 1997, Proceedings of the 1997 16 Southern Biomedical Engineering Conference.

[25]  Charles Hirsch,et al.  Anatomically based three-dimensional model of airways to simulate flow and particle transport using computational fluid dynamics. , 2005, Journal of applied physiology.