Physics-Informed Channel Modeling with Ultrasound Localization Microscopy: Reconstructing Geometry and Flow Field from Sparse Velocity Measurements

Ultrasound localization microscopy (ULM) has improved microvascular imaging by tracking microbubble (MB) trajectories to reconstruct vessel networks. However, the ensemble of trajectories produced by this technique is usually difficult to interpret, due to the underlying complexity of vascular geometries, MB motion, and unavoidable localization and tracking errors. In this work, we introduce a physics-informed channel modeling approach that reconstructs both vessel geometry and flow field exclusively from MB velocity measurements. By leveraging the inherent flow dynamics, each MB refines the vessel model, thus creating a unique, physically coherent reconstruction. We validate our method on an in vitro dataset, showing its robustness under sparse measurement conditions. This approach might allow for recovery of vessel information in tubes with low track density, while facilitating the extraction of clinically relevant global features.