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Acad Radiol. 2002 Nov;9(11):1286-99.

Blood flow modeling in carotid arteries with computational fluid dynamics and MR imaging.

Author information

1
School of Computational Sciences, George Mason University, Fairfax, VA 22030, USA.

Abstract

RATIONALE AND OBJECTIVES:

The authors' goal was to develop a noninvasive method for detailed assessment of blood flow patterns from direct in vivo measurements of vessel anatomy and flow rates.

MATERIALS AND METHODS:

The authors developed a method to construct realistic patient-specific finite element models of blood flow in carotid arteries. Anatomic models are reconstructed from contrast material-enhanced magnetic resonance (MR) angiographic images with a tubular deformable model along each arterial branch. A surface-merging algorithm is used to create a watertight model of the carotid bifurcation for subsequent finite element grid generation, and a fully implicit scheme is used to solve the incompressible Navier-Stokes equations on unstructured grids. Physiologic boundary conditions are derived from cine phase-contrast MR flow velocity measurements at two locations below and above the bifurcation. Vessel wall compliance is incorporated by means of fluid-solid interaction algorithms.

RESULTS:

The method was tested on imaging data from a healthy subject and a patient with mild stenosis. Finite element grids were successfully generated, and pulsatile blood flow calculations were performed. Computed and measured velocity profiles show good agreement. Flow patterns and wall shear stress distributions were visualized.

CONCLUSIONS:

Patient-specific computational fluid dynamics modeling based on MR images can be performed robustly and efficiently. Preliminary validation studies in a physical flow-through model suggest that the model is accurate. This method can be used to characterize blood flow patterns in healthy and diseased arteries and may eventually help physicians to supplement imaging-based diagnosis and predict and evaluate the outcome of interventional procedures.

PMID:
12449361
[Indexed for MEDLINE]

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