A Distributed Lumped Parameter Model of Blood Flow

Overview

Journal Ann Biomed Eng

Specialty Biomedical Engineering

Date 2020 Jul 3

PMID 32613457

Citations 12

Authors

Mehran Mirramezani

Shawn C Shadden

Affiliations

Soon will be listed here.

Abstract

We propose a distributed lumped parameter (DLP) modeling framework to efficiently compute blood flow and pressure in vascular domains. This is achieved by developing analytical expressions describing expected energy losses along vascular segments, including from viscous dissipation, unsteadiness, flow separation, vessel curvature and vessel bifurcations. We apply this methodology to solve for unsteady blood flow and pressure in a variety of complex 3D image-based vascular geometries, which are typically approached using computational fluid dynamics (CFD) simulations. The proposed DLP framework demonstrated consistent agreement with CFD simulations in terms of flow rate and pressure distribution, with mean errors less than 7% over a broad range of hemodynamic conditions and vascular geometries. The computational cost of the DLP framework is orders of magnitude lower than the computational cost of CFD, which opens new possibilities for hemodynamics modeling in timely decision support scenarios, and a multitude of applications of imaged-based modeling that require ensembles of numerical simulations.

Citing Articles

SeqSeg: Learning Local Segments for Automatic Vascular Model Construction.

Cepero N, Shadden S ArXiv. 2025; .

PMID: 39975447 PMC: 11838707.

Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer.

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SeqSeg: Learning Local Segments for Automatic Vascular Model Construction.

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PMID: 39292327 PMC: 11782360. DOI: 10.1007/s10439-024-03611-z.

Accelerated simulation methodologies for computational vascular flow modelling.

MacRaild M, Sarrami-Foroushani A, Lassila T, Frangi A J R Soc Interface. 2024; 21(211):20230565.

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Non-invasive Estimation of Pressure Drop Across Aortic Coarctations: Validation of 0D and 3D Computational Models with In Vivo Measurements.

Nair P, Pfaller M, Dual S, McElhinney D, Ennis D, Marsden A Ann Biomed Eng. 2024; 52(5):1335-1346.

PMID: 38341399 DOI: 10.1007/s10439-024-03457-5.

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