Mechanical Engineering - Theses

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Author: WANG, Qingdi × Subject: Computational Fluid Dynamics ×

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    Blood Flow Dynamics in the Aortic Dissection
    WANG, Qingdi ( 2023-08)
    Aortic dissection is one of the catastrophic cardiovascular diseases that have high mortality. It refers to an intimal tear in the aortic wall that initiates the formation of a false lumen due to blood flow between the layers of the vessel wall. Decisions about medical management or surgical intervention for long-term dissections are complex and still evolving, depending largely on the individual patient’s condition. In addition to conventional clinical images, the incorporation of more comprehensive physiological data would benefit clinicians in the decision-making process. Recent advancements in four-dimensional phase-contrast magnetic resonance imaging and computational fluid dynamics are promising in providing detailed data on haemodynamic parameters in cardiovascular diseases, including those that are challenging to predict or measure safely in clinical settings. In this work, the robustness and precision of a respiratory-controlled k-space reordering four-dimensional phase-contrast magnetic resonance imaging sequence were evaluated. Imaging data and pressure measurements are used to inform the development of numerical models of dissected aortas. The influence of different inlet boundary conditions on the outcomes of our simulations has also been investigated. The present results indicate that phase-contrast magnetic resonance imaging is valuable for providing patient-specific flow data. The evaluated magnetic resonance imaging sequence is reproducible and accurate in in-vivo flow metrics measurement. Computational fluid dynamics simulations based on multiple imaging modalities hold substantial promise for identifying potential risk factors associated with disease development. To accurately represent physiological haemodynamic parameters in aortic dissection, appropriate inlet boundary conditions and MRI data should be chosen.