Aortic valve (AV) diseases are life-threatening conditions which affect millions of people worldwide and, if left untreated, can lead to death a few years after symptom onset.

Patients affected by AV diseases are commonly referred to surgical AV replacement (SAVR). However, more than 30% of patients are not suitable for SAVR. For this reason, transcatheter aortic valve implantation (TAVI) has been attracting growing interest. Several clinical studies compared the outcomes of these techniques, showing that TAVI could be a valid alternative to SAVR. However, there is a lack of detailed knowledge about changes in the aortic hemodynamic conditions following these procedures.

The main aim of this thesis is to develop efficient and robust methodologies to study and compare the influences of different AV replacement procedures on aortic hemodynamics. An image-based patient-specific computational model has been developed, which uses magnetic resonance images (MRI) acquired from patients to obtain realistic geometry and boundary conditions (BCs) for computational fluid dynamics (CFD) analysis. The implemented physiological BCs were compared with the most commonly used inlet and outlet BCs, and showed the best agreement with in vivo data. The model was then applied to study and compare SAVR, TAVI and aortic root replacement using a variety of prostheses. In addition, an experimental set-up was designed to further study TAVI hemodynamics by combining 3D-printing, 4D flow MRI and CFD. Finally, a preliminary analysis of valve leaflet thrombosis was conducted.
It has been shown that both TAVI and SAVR are able to greatly improve the aortic hemodynamics, but this often deviates from conditions in healthy volunteers, with the extent of abnormalities strongly dependent on the type of prostheses or valve disease. The work also demonstrated the feasibility of predicting valve leaflet thrombosis using a shear-driven model for thrombus formation and growth.