Influences of flow parameters on pressure drop in a patient specific right coronary artery with two stenoses

Biyue Liu, Jie Zheng, Richard Bach, Dalin Tang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Blood pressure loss along the coronary arterial length and the local magnitude of the spatial wall pressure gradient (WPG) are important factors for atherosclerosis initiation and intimal hyperplasia development. The pressure drop coefficient (CDP) isdefined as the ratio of mean trans-stenotic pressure drop to proximal dynamic pressure. It is a unique non-dimensional flow resistance parameter useful in clinical practice for evaluating hemodynamic impact of coronary stenosis. It is expected that patients with the same stenosis severity may be at different risk level due to their blood pressure situations. The aim of this study is to numerically examine the dependence of CDP and WPG on flow rate and blood viscosity using a patient-specific atherosclerotic right coronary artery model with two stenoses. Our simulation results indicate that the coronary model with a lower flow rate yields a greater CDP across a stenosis, while the model with a higher flow rate yields a greater pressure drop and a greater WPG. Increased blood viscosity results in a greater CDP. Quantitatively, CDP for each stenosis appears to be a linear function of blood viscosity and a decreasing quadratic function of flow rate. Simulations with varying size and location of the distal stenosis show that the influence of the distal stenosis on the CDP across the proximal stenosis is insignificant. In a right coronary artery segment with two moderate stenoses of the same size, the distal stenosis causes a larger drop of CPD than the proximal stenosis does. A distal stenosis located in a further downstream position causes a larger drop in the CDP.

Original languageEnglish
Title of host publicationComputational Science and Its Applications - ICCSA 2017 - 17th International Conference, 2017
EditorsBeniamino Murgante, Bernady O. Apduhan, Giuseppe Borruso, Elena Stankova, Osvaldo Gervasi, Sanjay Misra, David Taniar, Ana Maria A.C. Rocha, Alfredo Cuzzocrea, Carmelo M. Torre
PublisherSpringer Verlag
Pages56-70
Number of pages15
ISBN (Print)9783319623917
DOIs
StatePublished - 2017
Event17th International Conference on Computational Science and Its Applications, ICCSA 2017 - Trieste, Italy
Duration: Jul 3 2017Jul 6 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10404
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Computational Science and Its Applications, ICCSA 2017
Country/TerritoryItaly
CityTrieste
Period07/3/1707/6/17

Keywords

  • Blood viscosity
  • Flow rate
  • Pressure drop coefficient
  • Spatial wall pressure gradient

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