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Project

Arterial biomechanics in 4D: understanding the long-term effects of mechanical overload

Various surgical interventions, such as vascular clamping, balloon angioplasty, or stenting, induce mechanical overload to arterial tissue, often leading to tissue damage. This overloading and resulting damage triggers growth and remodeling processes in the tissue, causing changes in mass and microstructure. This can lead to healing of the tissue, but the remodeling processes can also cause maladaptation. It remains unclear what triggers favorable over unfavorable outcomes. Therefore, we aim to gain a better understanding of growth and remodeling processes in arterial tissue after mechanical overload through in silico modeling. Currently existing models for growth and remodeling of arterial tissue lack detailed descriptions of underlying biological processes, necessary to allow in silico optimization of surgical procedures or pharmacological treatment.

The constrained mixture theory is a microstructurally-motivated framework for growth and remodeling models of soft biological tissues, that allows the use of a known in vivo reference configuration of the material, rather than the often unknown stress-free configuration. This reference shift impacts the material parameters associated with the mechanical behavior of the tissue. Therefore, a new material parameter fitting approach within the framework of the constrained mixture theory is developed. This fitting approach is verified against numerically constructed data sets of planar biaxial tensile tests, and applied to actual planar biaxial tensile test data. We show that the constrained mixture modeling approach leads to similar stress conditions in the artery, if an appropriate and compatible set of parameters is considered.

There are multiple implementation strategies associated with the constrained mixture growth and remodeling theory. The original classical theory includes a time integral, assuming that material cohorts deposited at different time points can have different natural states, while the homogenized version of the theory homogenizes the different cohort contributions to an average. We provide the theoretical details associated with both versions, explain practical implementation strategies and define multiple test cases. The results show good correspondence between the classical theory and its homogenized equivalent in terms of model outcomes. However, in terms of computational efficiency, the homogenized theory greatly outperforms the classical theory, as expected.

After these more theoretical prerequisites, the objective of this work is to develop biologically-motivated growth and remodeling algorithms that capture long-term effects of mechanical overload. We present growth and remodeling models that take into account the following biological aspects: vascular cell phenotype switch, endothelial damage, extracellular matrix production by synthetic vascular cells, enhanced by mechanical and inflammatory triggers, and vasoactivity after administration of vasoactive agents. The presented models are applied to different case-studies. Clamping experiments of mouse aortas are reproduced in silico. After parameter calibration, the results show good correspondence between the model and the experimental measurements of the functional integrity of the smooth muscle cells. The second case-study is the simulation of balloon angioplasty and the resulting material build-up and restenosis over time. We show that the model is able to predict both inward and outward remodeling, as observed in vivo. Finally, in the third case-study, the long-term remodeling of the tissue of a pulmonary artery interposition autograft in the aorta is predicted. The model parameters are calibrated based on measured constituent fractions and vessel diameters over time in sheep experiments. The results show that extracellular matrix production cannot be linearly related to tissue stress, as is assumed in many models, and is likely enhanced by inflammatory processes.

The presented models are developed as attempts at reducing the phenomenological nature of existing growth and remodeling models, allowing to provide a clearer understanding of these processes in arterial tissue. Moreover, they are a first step towards enabling in silico medicine for the optimization of long-term surgical outcomes.

Date:9 Aug 2017 →  30 Sep 2022
Keywords:arterial tissue, constitutive modeling, damage after mechanical overload
Disciplines:Orthopaedics, Biological system engineering, Biomaterials engineering, Biomechanical engineering, Medical biotechnology, Other (bio)medical engineering, Surgery, Nursing, Biomechanics
Project type:PhD project