A multiphysics modeling approach for in-stent restenosis: Theoretical aspects and finite element implementation. (November 2022)

• Record Type: Journal Article
• Authors: Manjunatha, Kiran; Behr, Marek; Vogt, Felix; Reese, Stefanie
• Is Part Of: Computers in biology and medicine. Volume 150(2022)
• Publication Date: 2022-11
• Subjects: Restenosis -- Stents -- Multiphysics -- Platelet-derived growth factor -- Transforming growth factor–β -- Extracellular matrix -- Smooth muscle cells -- Continuum growth modeling; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106166
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Development of in silico models that capture progression of diseases in soft biological tissues are intrinsic in the validation of the hypothesized cellular and molecular mechanisms involved in the respective pathologies. In addition, they also aid in patient-specific adaptation of interventional procedures. In this regard, a fully-coupled high-fidelity Lagrangian finite element framework is proposed within this work which replicates the pathology of in-stent restenosis observed post stent implantation in a coronary artery. Advection–reaction–diffusion equations are set up to track the concentrations of the platelet-derived growth factor, the transforming growth factor- β, the extracellular matrix, and the density of the smooth muscle cells. A continuum mechanical description of volumetric growth involved in the restenotic process, coupled to the evolution of the previously defined vessel wall constituents, is presented. Further, the finite element implementation of the model is discussed, and the behavior of the computational model is investigated via suitable numerical examples. Qualitative validation of the computational model is presented by emulating a stented artery. Patient-specific data are intended to be integrated into the model to predict the risk of in-stent restenosis, and thereby assist in the tuning of stent implantation parameters to mitigate the risk. Highlights: A fully-coupled Lagrangian finite element framework is presented. Cellular motility, specifically chemotaxis and haptotaxis, are captured. Continuum growth modeling in the presence of transversal isotropy is discussed. An interface element transfers quantities between fluid and solid domains. The framework can incorporate patient-specific immunohistochemical data.