Open Access

ARTICLE

Meshless Generalized Finite Difference Method and Human Carotid Atherosclerotic Plaque Progression Simulation Using Multi-Year MRI Patient-Tracking Data

Chun Yang¹, Dalin Tang², Chun Yuan³, William Kerwin², Fei Liu³, Gador Canton³, Thomas S. Hatsukami^3,4, Satya Atluri⁵

1 School of Math, Beijing Normal University, Beijing, China
2 Corresponding author, dtang@wpi.edu, Math Dept, WPI, Worcester, MA 01609 USA
3 Department of Radiology, University of Washington, Seattle, WA 98195
4 Division of Vascular Surgery, University of Washington,Seattle, WA 98195
5 Center for Aerospace Research & Education, University of California, Irvine, CA 92612, USA.

Computer Modeling in Engineering & Sciences 2008, 28(2), 95-108. https://doi.org/10.3970/cmes.2008.028.095

Abstract

Atherosclerotic plaque rupture and progression have been the focus of intensive investigations in recent years. Plaque rupture is closely related to most severe cardiovascular syndromes such as heart attack and stroke. A computational procedure based on meshless generalized finite difference (MGFD) method and serial magnetic resonance imaging (MRI) data was introduced to quantify patient-specific carotid atherosclerotic plaque growth functions and simulate plaque progression. Participating patients were scanned three times (T₁,T₂, and T₃, at intervals of about 18 months) to obtain plaque progression data. Vessel wall thickness (WT) changes were used as the measure for plaque progression. Since there was insufficient data with the current technology to quantify individual plaque component growth, the whole plaque was assumed to be uniform, homogeneous, hyperelastic, isotropic and nearly incompressible. The linear elastic model was used. The 2D plaque model was discretized and solved using a meshless generalized finite difference (GFD) method. Starting from the T₂ plaque geometry, plaque progression was simulated by solving the solid model and adjusting wall thickness using plaque growth functions iteratively until T₃ is reached. Numerically simulated plaque progression agreed very well with actual plaque geometry at T₃ given by MRI data. We believe this is the first time plaque progression simulation based on multi-year patient-tracking data was reported. Serial MRI-based progression simulation adds time dimension to plaque vulnerability assessment and will improve prediction accuracy for potential plaque rupture risk.

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