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Review Article

Computational Fluid Dynamics of Vascular Disease in Animal Models

[+] Author and Article Information
Andrea Acuna

Weldon School of Biomedical Engineering,
Purdue University,
206 S. Martin Jischke Drive,
West Lafayette, IN 47907
e-mail: aacuna@purdue.edu

Alycia G. Berman

Weldon School of Biomedical Engineering,
Purdue University,
206 S. Martin Jischke Drive,
West Lafayette, IN 47907
e-mail: berman1@purdue.edu

Frederick W. Damen

Weldon School of Biomedical Engineering,
Purdue University,
206 S. Martin Jischke Drive,
West Lafayette, IN 47907
e-mail: fdamen@purdue.edu

Brett A. Meyers

School of Mechanical Engineering,
Purdue University,
585 Purdue Mall,
West Lafayette, IN 47907
e-mail: meyers18@purdue.edu

Amelia R. Adelsperger

Weldon School of Biomedical Engineering,
Purdue University,
206 S. Martin Jischke Drive,
West Lafayette, IN 47907
e-mail: jacks228@purdue.edu

Kelsey C. Bayer

Weldon School of Biomedical Engineering,
Purdue University,
206 S. Martin Jischke Drive,
West Lafayette, IN 47907
e-mail: kbayer@purdue.edu

Melissa C. Brindise

School of Mechanical Engineering,
Purdue University,
585 Purdue Mall,
West Lafayette, IN 47907
e-mail: mbrindis@purdue.edu

Brittani Bungart

Weldon School of Biomedical Engineering,
Purdue University,
206 S. Martin Jischke Drive,
West Lafayette, IN 47907
e-mail: bbungart@purdue.edu

Alexander M. Kiel

Weldon School of Biomedical Engineering,
Purdue University,
206 S. Martin Jischke Drive,
West Lafayette, IN 47907
e-mail: amkiel@purdue.edu

Rachel A. Morrison

Weldon School of Biomedical Engineering,
Purdue University,
206 S. Martin Jischke Drive,
West Lafayette, IN 47907
e-mail: morri107@purdue.edu

Joseph C. Muskat

Weldon School of Biomedical Engineering,
Purdue University,
206 S. Martin Jischke Drive,
West Lafayette, IN 47907
e-mail: jmuskat@purdue.edu

Kelsey M. Wasilczuk

Weldon School of Biomedical Engineering,
Purdue University,
206 S. Martin Jischke Drive,
West Lafayette, IN 47907
e-mail: kwasilcz@purdue.edu

Yi Wen

Department of Agricultural and Biological Engineering,
Purdue University,
225 South University Street,
West Lafayette, IN 47907
e-mail: wen16@purdue.edu

Jiacheng Zhang

School of Mechanical Engineering,
Purdue University,
585 Purdue Mall,
West Lafayette, IN 47907
e-mail: zhan1589@purdue.edu

Patrick Zito

Weldon School of Biomedical Engineering,
Purdue University,
206 S. Martin Jischke Drive,
West Lafayette, IN 47907
e-mail: pzito@purdue.edu

Craig J. Goergen

ASME Membership
Bioengineering Division,
Weldon School of Biomedical Engineering,
Purdue University,
206 S. Martin Jischke Drive,
West Lafayette, IN 47907
e-mail: cgoergen@purdue.edu

1Corresponding author.

2These authors contributed equally to this work.

Manuscript received October 5, 2017; final manuscript received March 3, 2018; published online July 12, 2018. Assoc. Editor: Kristen Billiar.

J Biomech Eng 140(8), 080801 (Jul 12, 2018) (14 pages) Paper No: BIO-17-1451; doi: 10.1115/1.4039678 History: Received October 05, 2017; Revised March 03, 2018

Recent applications of computational fluid dynamics (CFD) applied to the cardiovascular system have demonstrated its power in investigating the impact of hemodynamics on disease initiation, progression, and treatment outcomes. Flow metrics such as pressure distributions, wall shear stresses (WSS), and blood velocity profiles can be quantified to provide insight into observed pathologies, assist with surgical planning, or even predict disease progression. While numerous studies have performed simulations on clinical human patient data, it often lacks prediagnosis information and can be subject to large intersubject variability, limiting the generalizability of findings. Thus, animal models are often used to identify and manipulate specific factors contributing to vascular disease because they provide a more controlled environment. In this review, we explore the use of CFD in animal models in recent studies to investigate the initiating mechanisms, progression, and intervention effects of various vascular diseases. The first section provides a brief overview of the CFD theory and tools that are commonly used to study blood flow. The following sections are separated by anatomical region, with the abdominal, thoracic, and cerebral areas specifically highlighted. We discuss the associated benefits and obstacles to performing CFD modeling in each location. Finally, we highlight animal CFD studies focusing on common surgical treatments, including arteriovenous fistulas (AVF) and pulmonary artery grafts. The studies included in this review demonstrate the value of combining CFD with animal imaging and should encourage further research to optimize and expand upon these techniques for the study of vascular disease.

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Figures

Grahic Jump Location
Fig. 1

Basic progression highlighting the steps from MRI visualization to CFD simulation results. (a) Vessel rendering by simvascular allows for simple and fast visualization [10]. (b) Acloser view of the mesh reveals the individual elements. (c) Arrows (top) represent the outflow of blood while arrows (bottom) show inflow. The inflow of blood was prescribed by volume flow rate waveforms, while outflows are often determined by scaled cross section mass flow, resistor–capacitor–resistor models, or simplified resistor-only boundary conditions. (d) Velocity streamlines used to visualize the results of CFD simulations (unpublished data).

Grahic Jump Location
Fig. 2

Pressure distributions down a mouse aorta with a saccular dissecting AAA. The inclusion of (a) no branching vessels, (b) only abdominal branching vessels, (c) only thoracic branching vessels, and (d) both thoracic and abdominal branching vessels had substantial effects. Expected physiological pressures are seen around the AAA only when abdominal branching vessels are included; otherwise, overestimations of pressure were observed (unpublished data).

Grahic Jump Location
Fig. 3

Volumetric rendering and mean velocity waveforms from two mice with moderate (a) and severe (b) dissecting aortic aneurysms. Simulations show the magnitude of the velocity at systole (left) and diastole (right). Solid lines represent simulated velocity waveforms compared to ultrasound measurements, shown in dashed lines, at all major inlets and outlets and at an intermediate proximal location used to validate the models [28].

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Fig. 4

Surface and cross-sectional reconstruction of a mouse aortic arch: (a) showing the ascending aorta (AA), descending aorta (DA), brachiocephalic artery (BCA), left common carotid artery (LCC), and left subclavian artery (LS) afflicted by atherosclerotic plaques. Cross-sectional image from a μCT scan (b) from which geometries are derived. CFD results were used to quantify time-averaged wall shear stress (TAWSS; (c)), oscillatory shear index (OSI; (d)), and relative residence time (RRT; (e)) [42].

Grahic Jump Location
Fig. 5

Time-averaged wall shear stress distributions at varying levels of proximal stenosis on a porcine LAD model. Points of maximal WSS (black arrows) shift from the ostium of branching vessels to the point of stricture when the stenosis reaches 75%. At 90% stenosis, regions of maximal WSS are also observed distal to the stricture along the outer curvature of the LAD [71]. (Reprinted with permission from Elsevier copyright 2014).

Grahic Jump Location
Fig. 6

Wall shear stress distribution before (a) and after (b) implantation of a flow diverter. Relative residence time distribution before (c) and after (d) implantation illustrates higher values within the aneurysm after the device was deployed [96]. (Reprinted with permission from Wolters Kluwer Health, Inc., copyright 2013).

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