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Principles of Computational Fluid Dynamics


Generating Vessel Centerlines


Remapping 4D Flow MRI and Determining the Boundary Conditions


CFD Simulations







Computational Fluid Dynamics Simulations of Blood Flow in a Cerebral Aneurysm

Source: Joseph C. Muskat, Vitaliy L. Rayz, and Craig J. Goergen, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana

The objective of this video is to describe recent advancements of computational fluid dynamic (CFD) simulations based on patient- or animal-specific vasculature. Here, subject-based vessel segmentations were created, and, using a combination of open-source and commercial tools, a high-resolution numerical solution was determined within a flow model. Numerous studies have demonstrated that the hemodynamic conditions within the vasculature affect the development and progression of atherosclerosis, aneurysms, and other peripheral artery diseases; concomitantly, direct measurements of intraluminal pressure, wall shear stress (WSS), and particle residence time (PRT) are difficult to acquire in vivo.

CFD allow such variables to be assessed non-invasively. In addition, CFD is used to simulate surgical techniques, which provides physicians better foresight regarding post-operative flow conditions. Two methods in magnetic resonance imaging (MRI), magnetic resonance angiography (MRA) with either time of flight (TOF-MRA) or contrast-enhanced MRA (CE-MRA) and phase-contrast (PC-MRI), allow us to obtain vessel geometries and time-resolved 3D velocity fields, respectively. TOF-MRA is based on the suppression of the signal from static tissue by repeated RF pulses that are applied to the imaged volume. A signal is obtained from unsaturated spins moving into the volume with the flowing blood. CE-MRA is a better technique for imaging vessels with complex recirculating flows, as it uses a contrast agent, such as gadolinium, to increase the signal.

Separately, PC-MRI utilizes bipolar gradients to generate phase shifts that are proportional to a fluid's velocity, thus providing time-resolved velocity distributions. While PC-MRI is capable of providing blood flow velocities, the accuracy of this method is affected by limited spatiotemporal resolution and velocity dynamic range. CFD provides superior resolution and can assess the range of velocities from high-speed jets to slow recirculating vortices observed in diseased blood vessels. Thus, even though the reliability of CFD depends on the modeling assumptions, it opens up the possibility for high quality, comprehensive depiction of patient-specific flow fields, which can guide diagnosis and treatment.

A precursor to the tutorial is the creation of a patient-specific vasculature model. In this demonstration, the tools Materialise Mimics, 3D Systems Geomagic Design X, and Altair HyperMesh were used to generate a tetrahedral volume mesh from MRA data.

1. Generate vessel centerlines for the model

  1. Open the vmtk-launcher python GUI. In the PypePad, type: vmtkcenterlines -ifile [STL file saved to desktop].stl -ofile [STL name]centerlines.vtp
  2. Select Run,

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In this demonstration, a subject-specific model of a cerebral aneurysm was generated and the CFD was used to simulate the flow field. By providing detailed flow features and quantifying hemodynamics forces not obtainable from imaging data, CFD can be used to augment lower resolution 4D Flow MRI data.  Figure 1 shows how CFD gives a more complete description of the flow in the near-wall, re-circulating regions.

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The framework described here can be used to perform patient-specific CFD simulations. A high-resolution mesh is used to interpolate low-resolution 4D Flow MRI data; this isolates the flow data and minimizes error associated with noise external to the vessel wall. By using patient-based boundary conditions for the inlet and outlet flows, the simulation is capable of matching the hemodynamic conditions imaged with MRI.

Novel methods for PC-MRI are capable of showing larger, dynamic ranges of vel

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Computational Fluid Dynamics

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