BACKGROUND AND OBJECTIVE: Although thoracic aortic endovascular repair (TEVAR) has shown promising outcomes in the treatment of patients with complicated type B aortic dissection, complications still occur after TEVAR that can lead to catastrophic events. Biomechanical interactions between the stent-graft (SG) and the local aortic tissue play a critical role in determining the outcome of TEVAR. Different SG design may cause different biomechanical responses in the treated aorta, but such information is not known at the time of pre-procedural planning. By developing patient-specific virtual stent-graft deployment tools, it is possible to analyse and compare the biomechanical impact of different SGs on the local aorta for individual patients. METHODS: A finite element based virtual SG deployment model was employed in this study. Computational simulations were performed on a patient-specific model of type B aortic dissection, accounting for details of the SG design and the hyperelastic behaviour of the aortic wall. Based on the geometry reconstructed from the pre-TEVAR CTA scan, the patient-specific aortic dissection model was created and pre-stressed. Parametric models of three different SG products (SG1, SG2 and SG3) were built with two different lengths for each design. The SG models incorporated different stent and graft materials, stent strut patterns, and assembly approaches. Using our validated SG deployment simulation framework, virtual trials were performed on the patient-specific aortic dissection model using different SG products and varying SG lengths. CONCLUSION: Simulation results for different SG products suggest that SG3 with a longer length (SG3-long) would be the most appropriate device for the individual patient. Compared to SG1-short (the SG deployed in the patient), SG3-long followed the true lumen tortuosity closely, resulted in a more uniform true lumen expansion and a significant reduction in peak stress in the distal landing zone. These simulation results are promising and demonstrate the feasibility of using the virtual SG deployment model to assist clinicians in pre-procedural planning.