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 cirulatory 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.

Non-invasive characterization of complex coronary lesions

“…More recent studies have leveraged LBM-based CFD solvers to derive pressure fields required to compute FFR 23 , 24 . In contrast to other CFD solvers, LBM-based solvers allow efficient distribution of computational load and high-resolution simulations on millions of processors 13 , 17 . HARVEY (v2) used the 3D coronary arterial mesh in standard stereolithography (STL) format as input to the model.…”

“…CFD simulations were performed using HARVEY (v2), a computational hemodynamics software package 12 , 13 , 17 . HARVEY (v2) (v2) is based on the LBM, an alternate approach to solving the Navier-Stokes equations governing the fluid flow.…”

“…The pressure in lattice units was converted into physical units (mmHg) by using the pressure coefficient equation: where is the pressure difference, and and u are the reference density and velocity, respectively. Further details about the numerical implementation, parallelization, and scaling of HARVEY (v2) can be found in 12 , 13 , 17 .…”

“…Using parallel computing, this framework enables ultra-high resolution physiological modeling in reasonable execution times, thus meeting the high computational demand needed for these in silico experiments. Fast and accurate non-invasive functional arterial assessment is achieved by using the massively parallel CFD solver HARVEY (v2) 12 , 13 . With this CFD-CA framework, we expect to non-invasively derive diagnostic metrics, such as FFR with lower errors and reclassification rates, and resolve local hemodynamics for pathophysiological analysis in all identifiable vessels in an angiogram.…”

A data-driven approach to modeling cancer cell mechanics during microcirculatory transport

“…Simulations are performed using HARVEY, a 3D lattice Boltzmann method (LBM)-based computational fluid dynamics solver 37 , 40 , 56 – 58 . HARVEY has been used to study a wide range of physiological applications, such as the hemodynamics of a growing cerebral aneurysm 59 , computing the ankle-brachial index for diagnosing conditions such as peripheral artery disease 60 , cell adhesion in microvessels and impacts on wall shear stress 47 , and large-scale RBC simulations in a cerebral vasculature 40 . For modeling flows of deformable cells, HARVEY utilizes the immersed boundary method (IBM), and with this approach cancer cells are modeled as membranes of infinitesimal thickness surrounded by and enclosing a viscous fluid with which they flow.…”