Medical-image-based aorta modeling with zero-stress-state estimation

    Research output: Contribution to journalArticle

    2 Citations (Scopus)

    Abstract

    Because the medical-image-based geometries used in patient-specific arterial fluid–structure interaction computations do not come from the zero-stress state (ZSS) of the artery, we need to estimate the ZSS required in the computations. The task becomes even more challenging for arteries with complex geometries, such as the aorta. In a method we introduced earlier the estimate is based on T-spline discretization of the arterial wall and is in the form of integration-point-based ZSS (IPBZSS). The T-spline discretization enables dealing with complex arterial geometries, such as an aorta model with branches, while retaining the desirable features of isogeometric discretization. With higher-order basis functions of the isogeometric discretization, we may be able to achieve a similar level of accuracy as with the linear basis functions, but using larger-size and fewer elements. In addition, the higher-order basis functions allow representation of more complex shapes within an element. The IPBZSS is a convenient representation of the ZSS because with isogeometric discretization, especially with T-spline discretization, specifying conditions at integration points is more straightforward than imposing conditions on control points. The method has two main components. 1. An iteration technique, which starts with a calculated ZSS initial guess, is used for computing the IPBZSS such that when a given pressure load is applied, the medical-image-based target shape is matched. 2. A design procedure, which is based on the Kirchhoff–Love shell model of the artery, is used for calculating the ZSS initial guess. Here we increase the scope and robustness of the method by introducing a new design procedure for the ZSS initial guess. The new design procedure has two features. (a) An IPB shell-like coordinate system, which increases the scope of the design to general parametrization in the computational space. (b) Analytical solution of the force equilibrium in the normal direction, based on the Kirchhoff–Love shell model, which places proper constraints on the design parameters. This increases the estimation accuracy, which in turn increases the robustness of the iterations and the convergence speed. To show how the new design procedure for the ZSS initial guess performs, we first present 3D test computations with a straight tube and a Y-shaped tube. Then we present a 3D computation where the target geometry is coming from medical image of a human aorta, and we include the branches in the model.

    Original languageEnglish
    JournalComputational Mechanics
    DOIs
    Publication statusPublished - 2019 Jan 1

    Fingerprint

    Aorta
    State Estimation
    State estimation
    Medical Image
    Discretization
    Zero
    Guess
    Modeling
    Arteries
    Splines
    Spline
    Basis Functions
    Shell Model
    Geometry
    Complex Geometry
    Tube
    Branch
    Higher Order
    Robustness
    Iteration

    Keywords

    • Aorta
    • Integration-point-based zero-stress state
    • Isogeometric wall discretization
    • Medical-image-based geometry
    • Patient-specific arterial FSI
    • Shell-model-based initial guess
    • T-spline basis functions
    • Zero-stress state

    ASJC Scopus subject areas

    • Computational Mechanics
    • Ocean Engineering
    • Mechanical Engineering
    • Computational Theory and Mathematics
    • Computational Mathematics
    • Applied Mathematics

    Cite this

    Medical-image-based aorta modeling with zero-stress-state estimation. / Sasaki, Takafumi; Takizawa, Kenji; Tezduyar, Tayfun E.

    In: Computational Mechanics, 01.01.2019.

    Research output: Contribution to journalArticle

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