Computing the ankle-brachial index with parallel computational fluid dynamics

John Gounley; Erik W. Draeger; Tomas Oppelstrup; William D. Krauss; John A. Gunnels; Rafeed Chaudhury; Priya Nair; David Frakes; Jane A. Leopold; Amanda Randles

doi:10.1016/j.jbiomech.2018.10.007

Computing the ankle-brachial index with parallel computational fluid dynamics

John Gounley, Erik W. Draeger, Tomas Oppelstrup, William D. Krauss, John A. Gunnels, Rafeed Chaudhury, Priya Nair, David Frakes, Jane A. Leopold, Amanda Randles

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

The ankle-brachial index (ABI), a ratio of arterial blood pressure in the ankles and upper arms, is used to diagnose and monitor circulatory conditions such as coarctation of the aorta and peripheral artery disease. Computational simulations of the ABI can potentially determine the parameters that produce an ABI indicative of ischemia or other abnormalities in blood flow. However, 0- and 1-D computational methods are limited in describing a 3-D patient-derived geometry. Thus, we present a massively parallel framework for computational fluid dynamics (CFD) simulations in the full arterial system. Using the lattice Boltzmann method to solve the Navier–Stokes equations, we employ highly parallelized and scalable methods to generate the simulation domain and efficiently distribute the computational load among processors. For the first time, we compute an ABI with 3-D CFD. In this proof-of-concept study, we investigate the dependence of ABI on the presence of stenoses, or narrowed regions of the arteries, by directly modifying the arterial geometry. As a result, our framework enables the computation a hemodynamic factor characterizing flow at the scale of the full arterial system, in a manner that is extensible to patient-specific imaging data and holds potential for treatment planning.

Original language	English
Pages (from-to)	28-37
Number of pages	10
Journal	Journal of Biomechanics
Volume	82
DOIs	https://doi.org/10.1016/j.jbiomech.2018.10.007
State	Published - Jan 3 2019
Externally published	Yes

Funding

Research reported in this publication was supported by the Office of the Director, National Institutes of Health under Award Number DP5OD019876. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This material is based upon work supported by the National Science Foundation under Grant No. 1512553 . Support was provided by the Big Data-Scientist Training Enhancement Program (BD-STEP) of the Department of Veterans Affairs. Research reported in this publication was supported by the Office of the Director, National Institutes of Health under Award Number DP5OD019876. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This material is based upon work supported by the National Science Foundation under Grant No. 1512553. Support was provided by the Big Data-Scientist Training Enhancement Program (BD-STEP) of the Department of Veterans Affairs. We thank Don Frederick and the Livermore Computing Operations team at Lawrence Livermore National Laboratory (LLNL) for helping with the full system runs on Vulcan. This work was performed under the auspices of the U.S. Department of Energy by LLNL under Contract DE-AC52-07NA27344. Computing support for this work came from the LLNL Institutional Computing Grand Challenge program. Support was also provided by the LLNL Laboratory Directed Research and Development (LDRD) program.Research reported in this publication was supported by the Office of the Director, National Institutes of Health under Award Number DP5OD019876. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This material is based upon work supported by the National Science Foundation under Grant No. 1512553. Support was provided by the Big Data-Scientist Training Enhancement Program (BD-STEP) of the Department of Veterans Affairs. We thank Don Frederick and the Livermore Computing Operations team at Lawrence Livermore National Laboratory (LLNL) for helping with the full system runs on Vulcan. This work was performed under the auspices of the U.S. Department of Energy by LLNL under Contract DE-AC52-07NA27344. Computing support for this work came from the LLNL Institutional Computing Grand Challenge program. Support was also provided by the LLNL Laboratory Directed Research and Development (LDRD) program.

Funders	Funder number
Don Frederick
National Science Foundation	1512553
National Institutes of Health
U.S. Department of Energy
NIH Office of the Director	DP5OD019876
Office of the Director
U.S. Department of Veterans Affairs
Lawrence Livermore National Laboratory	DE-AC52-07NA27344
Laboratory Directed Research and Development
National Science Foundation

Keywords

Ankle-brachial index
Computational fluid dynamics
Hemodynamics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.jbiomech.2018.10.007

Cite this

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title = "Computing the ankle-brachial index with parallel computational fluid dynamics",

abstract = "The ankle-brachial index (ABI), a ratio of arterial blood pressure in the ankles and upper arms, is used to diagnose and monitor circulatory conditions such as coarctation of the aorta and peripheral artery disease. Computational simulations of the ABI can potentially determine the parameters that produce an ABI indicative of ischemia or other abnormalities in blood flow. However, 0- and 1-D computational methods are limited in describing a 3-D patient-derived geometry. Thus, we present a massively parallel framework for computational fluid dynamics (CFD) simulations in the full arterial system. Using the lattice Boltzmann method to solve the Navier–Stokes equations, we employ highly parallelized and scalable methods to generate the simulation domain and efficiently distribute the computational load among processors. For the first time, we compute an ABI with 3-D CFD. In this proof-of-concept study, we investigate the dependence of ABI on the presence of stenoses, or narrowed regions of the arteries, by directly modifying the arterial geometry. As a result, our framework enables the computation a hemodynamic factor characterizing flow at the scale of the full arterial system, in a manner that is extensible to patient-specific imaging data and holds potential for treatment planning.",

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AU - Gounley, John

AU - Draeger, Erik W.

AU - Oppelstrup, Tomas

AU - Krauss, William D.

AU - Gunnels, John A.

AU - Chaudhury, Rafeed

AU - Nair, Priya

AU - Frakes, David

AU - Leopold, Jane A.

AU - Randles, Amanda

PY - 2019/1/3

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N2 - The ankle-brachial index (ABI), a ratio of arterial blood pressure in the ankles and upper arms, is used to diagnose and monitor circulatory conditions such as coarctation of the aorta and peripheral artery disease. Computational simulations of the ABI can potentially determine the parameters that produce an ABI indicative of ischemia or other abnormalities in blood flow. However, 0- and 1-D computational methods are limited in describing a 3-D patient-derived geometry. Thus, we present a massively parallel framework for computational fluid dynamics (CFD) simulations in the full arterial system. Using the lattice Boltzmann method to solve the Navier–Stokes equations, we employ highly parallelized and scalable methods to generate the simulation domain and efficiently distribute the computational load among processors. For the first time, we compute an ABI with 3-D CFD. In this proof-of-concept study, we investigate the dependence of ABI on the presence of stenoses, or narrowed regions of the arteries, by directly modifying the arterial geometry. As a result, our framework enables the computation a hemodynamic factor characterizing flow at the scale of the full arterial system, in a manner that is extensible to patient-specific imaging data and holds potential for treatment planning.

AB - The ankle-brachial index (ABI), a ratio of arterial blood pressure in the ankles and upper arms, is used to diagnose and monitor circulatory conditions such as coarctation of the aorta and peripheral artery disease. Computational simulations of the ABI can potentially determine the parameters that produce an ABI indicative of ischemia or other abnormalities in blood flow. However, 0- and 1-D computational methods are limited in describing a 3-D patient-derived geometry. Thus, we present a massively parallel framework for computational fluid dynamics (CFD) simulations in the full arterial system. Using the lattice Boltzmann method to solve the Navier–Stokes equations, we employ highly parallelized and scalable methods to generate the simulation domain and efficiently distribute the computational load among processors. For the first time, we compute an ABI with 3-D CFD. In this proof-of-concept study, we investigate the dependence of ABI on the presence of stenoses, or narrowed regions of the arteries, by directly modifying the arterial geometry. As a result, our framework enables the computation a hemodynamic factor characterizing flow at the scale of the full arterial system, in a manner that is extensible to patient-specific imaging data and holds potential for treatment planning.

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Computing the ankle-brachial index with parallel computational fluid dynamics

Abstract

Funding

Keywords

UN SDGs

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