Computational modeling of the cardiovascular system plays an increasingly important role in biomedicine, as it allows for non-invasive investigations of the status-quo and studying the influence of different treatment options available. The goal is to incorporate patient-specific datasets to obtain socalled digital twins to increase relevance of virtual surgery and support clinical decision making. In this context, aortic dissection is particularly challenging, since the overall system behavior strongly depends on the interplay between tissue deformation and blood flow, giving rise to a fully coupled fluid-structure interaction problem. To account for the complex physics, several additional modeling aspects such as prestress, advanced constitutive models respecting fibre orientation and suitable boundary conditions for the fluid and solid phases have to be considered. Within this study, these special techniques are applied to a patient-specific dataset, for which first results are presented highlighting their relevance.