Computational Fluid Dynamics (CFD) is a powerful tool for cardiac hemodynamics quantification. Fluid–Structure Interaction (FSI) models offer a biomechanically complete description, but they require careful calibration of uncertain tissue parameters and epicardial boundary conditions. Image-driven CFD provides an alternative by encoding cardiac motion directly from imaging data, particularly valuable for congenital right-heart diseases. Pulmonary Valve Replacement (PVR), performed to restore valve competence in these cases, differentially affect right ventricular loading and flow patterns, making detailed computational assessment essential. In this work, we apply a patient-specific computational framework reconstructing three-dimensional cardiac geometries and displacements from multi-stack time-resolved cine Magnetic Resonance Imaging(MRI). Starting from the workflow of Renzi et al.[1], we introduce an improved segmentation pipeline: after manual tracing of the endocardium on cine-MRI slices for one time frame, an optical flow[2] algorithm automatically contours remaining frames, enhancing efficiency. The valves are explicitly traced across all time frames to accurately capture their dynamics. Segmented anatomies are processed with the Multi-Series Morphing technique [1] to recover anatomy and dynamics. CFD simulations allow to discretize Navier–Stokes equations in an Arbitrary Lagrangian Eulerian formulation relying on the Finite Element library lifex [3]. Valve dynamics is modeled by means of physiological opening patterns through the resistive method; turbulence is captured using a Large Eddy Simulation model[4]. The computational framework, previously validated for pre-operative Tetralogy of Fallot hemodynamics [4], is extended to systematically model pre- and post-PVR conditions across multiple patients. This enables consistent comparison between pre- and post operative scenarios. For the first time, patients undergoing different PVR types—including homograft, mechanical, and biological valve implantation—are investigated, allowing analysis of blood-flow dynamics across distinct post-intervention configurations. Hemodynamic variables are analyzed to assess PVR impact on right ventricular loading and pulmonary flow patterns, offering insights for personalized treatment planning.