Patient-specific carotid plaque progression simulation using 3D meshless generalized finite difference models with fluid-structure interactions based on serial in vivo mRI data

Chun Yang, Dalin Tang, Satya Atluri

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Previously, we introduced a computational procedure based on threedimensional meshless generalized finite difference (MGFD) method and serial magnetic resonance imaging (MRI) data to quantify patient-specific carotid atherosclerotic plaque growth functions and simulate plaque progression. Structure-only models were used in our previous report. In this paper, fluid-stricture interaction (FSI) was added to improve on prediction accuracy. One participating patient was scanned three times (T1, T2, and T3, at intervals of about 18 months) to obtain plaque progression data. Blood flow was assumed to laminar, Newtonian, viscous and incompressible. The Navier-Stokes equations with arbitrary Lagrangian- Eulerian (ALE) formulation were used as the governing equations. Plaque material was assumed to be uniform, homogeneous, isotropic, linear, and nearly incompressible. The linear elastic model was used. The 3D FSI plaque model was discretized and solved using a meshless generalized finite difference (GFD) method. Growth functions with a) morphology alone; b) morphology and plaque wall stress (PWS); morphology and flow shear stress (FSS), and d) morphology, PWS and FSS were introduced to predict future plaque growth based on previous time point data. Starting from the T2 plaque geometry, plaque progression was simulated by solving the FSI model and adjusting plaque geometry using plaque growth functions iteratively until T3 is reached. Numerically simulated plaque progression agreed very well with the target T3 plaque geometry with errors ranging from 8.62%, 7.22%, 5.77% and 4.39%, with the growth function including morphology, plaque wall stress and flow shear stress terms giving the best predictions. Adding flow shear stress term to the growth function improved the prediction error from 7.22% to

Original languageEnglish
Pages (from-to)53-77
Number of pages25
JournalCMES - Computer Modeling in Engineering and Sciences
Volume72
Issue number1
StatePublished - 2011

Keywords

  • Artery
  • Atherosclerosis
  • Fluid-structure interaction
  • Generalized finite difference
  • Meshless
  • Plaque progression

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