Inferior vena cava (IVC) filters have been used to mitigate pulmonary embolism (PE) in at-risk patients for nearly half a century. Despite years of innovation, IVC filter-related complications remain common, including caval perforation, filter embedment, filter fracture, filter migration, persistence or worsening of deep-vein thrombosis due to IVC thrombosis, and recurrence of PE. In this thesis, a computational workflow is developed and demonstrated for simulating IVC filter placement, hemodynamics, and embolus transport and capture in patient-specific models reconstructed from medical image data.Patient-specific IVC geometries are first segmented and reconstructed from clinical computed tomography data. Virtual IVC filter placement is then performed using nonlinear finite element analysis with vein-filter contact modeling and nonlinear constitutive models for the vein tissue and nitinol IVC filter. The blood flow and the transport and capture of emboli are simulated using a coupled computational fluid dynamics / six degree-of-freedom (CFD/6DoF) model that uses an immersed boundary method to resolve the blood flow around the emboli. The embolus-trapping performance of the filter is predicted by systematically varying embolus starting positions, embolus diameters, embolus densities, and IVC orientations in thousands of embolus transport simulations.Simulations reveal that patient-specific variations in the IVC cross-sectional shape cause a non-uniform distribution of veinfilter contact forces among the filter struts (range of 8-26 mN per strut). Patient-specific anatomical features also generate regions of locally high or low wall shear stress (WSS) and affect secondary flow features such as Dean vortices and flow recirculation regions. Non-Newtonian blood effects are found to be significant in the IVC (e.g., up to a 50% difference in WSS between results obtained using a non-Newtonian model compared with using the Newtonian approximation) due to the predominance of low shear rates. Embolus transport simulations predict that embolus trajectories and capture rates for a given filter are sensitive to the IVC morphology, the embolus-to-blood density ratio, and the direction of the gravitational force (i.e., patient orientation).With further development and validation, the computational workflow presented herein may be used to guide IVC filter design, to predict the in vivo performance of IVC filters, and to guide the selection and placement of IVC filters on a patient-specific basis. Simulations could also be used to improve preclinical testing standards such that benchtop experiments better mimic the in vivo environment and performance of IVC filters.