Type B aortic dissection is a potentially devastating disease of the aorta initiated by a tear in the inner lining of the aortic wall. Blood flow through this tear causes the aortic wall layers to separate and a secondary channel of blood flow know as the ‘false lumen’ forms. Complete thrombosis (clotting) of the false lumen, is the desired outcome of either medical or endovascular (TEVAR) treatment. However, it is currently unclear at the time of diagnosis how a specific dissection will progress with either treatment option.
Anatomical studies have identified a range of morphological factors that may be influential in disease progression, though no single parameter has been found to be independently predictive of patient prognosis. Computational fluid dynamics (CFD) studies have aimed to assess the hemodynamic state of dissection, however, studies have generally been limited due to simplified geometries and unphysiological boundary conditions due to the lack of patient-specific in vivo flow data. Thanks to recent developments in imaging technologies, in vivo flow data can now be acquired through 4D-flow magnetic resonance imaging (MRI), though detailed evaluation of dissection flow fields is limited due to poor image quality. CFD has the potential to be a useful tool in clinical practice for predicting disease progression, as long as the results are physiological to specific patients. 4D-flow MRI data could provide the patient-specific details required to build detailed and accurate CFD models.
The primary objective of this thesis is to develop clinically applicable computational models to accurately simulate hemodynamics and thrombus formation in type B dissection patients. A 4D-flow MRI based CFD workflow was developed and key model inputs were assessed in detail. The use of a patient-specific 3D inlet velocity profile was compared to commonly used idealised profiles, with the 3D profile producing results which agreed best with in vivo data. The importance of major and minor aortic branches in geometry segmentation was assessed, and results showed that exclusion of such branches can significantly impact predicted hemodynamics and thrombus formation. The finalised CFD workflow was evaluated against in vivo data and was shown to be able to faithfully reproduce dissection hemodynamics in a study of 5 patients. A hemodynamics-based thrombus predicting model was evaluated and simplified in order to improve computational efficiency for clinical application. Finally, the CFD workflow and thrombus model were utilised in studies on the influence of re-entry tears both pre- and post-TEVAR.