Why the study?
Various computational models of the circle of Willis have been developed to non-invasively estimate blood flow and hemodynamic parameters for clinical risk assessment, but their latest forms and innovative techniques required categorization and summarization.
This review provides a comprehensive categorization of 0D, 1D, and 3D computational models of the circle of Willis, highlighting the trade-offs between computational complexity and physiological accuracy for simulating intracranial hemodynamics.
Categorizes CoW models addressing rising complexity; leaves open prospective clinical validation for aneurysm and ischemia risk assessment.
Background: Various computational models of the circle of Willis (CoW) have been developed to non-invasively estimate the blood flow and hemodynamic parameters in intracranial arteries for the assessment of clinical risks such as aneurysms, ischemia, and atherosclerotic plaque growth. This review aims to categorize the latest computational models of CoW and summarize the innovative techniques. Summary of Review: In traditional computational models of CoW, the computational complexity increased from zero-dimensional models to one-dimensional and three-dimensional models. The applications extend from estimating certain hemodynamic parameters to simulating local flow field. The innovative techniques include the combination of models with different dimensions, the extension of vascular structure including heart and veins, as well as the addition of distal fractality, cerebral autoregulation, and intracranial pressure. There are some nontraditional models based on fluid-solid-interaction, control theory, and in-vitro experiments. In all kinds of models, the in-vivo data and non-Newtonian rheological models of blood have been widely applied to improve the accuracy of hemodynamic simulation. Conclusion: Firstly, the selection of model depends on its application scenario. The balance between computational complexity and physiological accuracy deserves further investigation. Secondly, the improvement of CoW models relies on the large-scale validations and the combination of various innovative modeling techniques.
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Liu et al. (2020) studied this question.
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