Medium vessel occlusions (MeVOs), affecting distal cerebral artery segments, pose significant challenges to gold-standard treatments due to limited accessibility and risk of vessel injury associated with small vessel diameters. Intra-arterial thrombolysis, which delivers a thrombolytic agent to the clot site, represents a promising alternative. However, there is currently no clinical consensus regarding the optimal drug dosage or the minimum catheter-to-clot distance required for effective treatment. Moreover, previous studies suggest that clot composition and the presence of collateral vessels significantly influence thrombolysis kinetics [1, 2]. This study presents a coupled experimental–computational framework to systematically investigate the influence of procedural factors (drug concentration, catheter-to-clot distance), and biological factors (clot size and composition, collateral vessels) on intra-arterial thrombolysis efficacy. The goal is to integrate experimental measurements with computational modelling to support patient-specific treatment planning. Three patient-specific MeVO cases involving the anterior, middle, and posterior cerebral arteries are reconstructed from clinical imaging data (Fig. 1a). Vessel geometries and clot locations are extracted through image segmentation. Corresponding silicone phantoms are integrated into a controlled flow loop reproducing physiological flow conditions (Fig. 1b). Thrombi of varying lengths and compositions are introduced into the phantoms. Alteplase is administered through a 5F catheter positioned at multiple distances from the clot, and several drug concentrations are tested. Collateral circulation is mimicked by adding side branches. Thrombolysis progression is assessed at predefined time points. Treatment effectiveness is quantified using recanalization time and success. The experimental dataset will inform the development and validation of a physics-based in-silico thrombolysis model (Fig. 1c). The model will enable the exploration of a wider number of patient cases, supporting the creation of a statistical, patient-specific decision support tool for treatment planning.