Vessel Stress Mapping and Preventive Stroke-Risk Ultrasound Localization Microscopy (VSM-ULM)

This project builds on recent advances in ultrasound localization microscopy (ULM/sULM) to treat stroke as a mechanical fatigue problem of the microvasculature. Using contrast microbubbles as Lagrangian tracers, we reconstruct the microvascular network and compute vessel-level stress and fragility metrics (Vessel Stress Index, Network Fragility Score) from track-derived features such as velocity, recirculation, normalized distance, and dwell time. To make ULM clinically trustworthy, we propose a Birth-Time Cone Ledger: a physically-constrained, audit-ready multi-object tracker that maintains per-bubble identity with organ-specific speed/acceleration priors and explicit quality flags (“clean / suspicious / broken” trajectories). On the sensing side, we outline a stress-reporting tracer concept: a second-layer contrast agent (shear- or inflammation-responsive) that irreversibly changes its US/PA signal in mechanically or biochemically stressed segments. Its per-segment signal (SRI) is fused with the ULM-derived stress metrics into a Composite Risk Index for each vessel. Finally, we specify a cheap, CEUS-based clinical workflow that runs on existing high-end ultrasound systems, with realistic scan times, GPU-accelerated processing, coverage/QC reporting, and simple outputs (heatmaps + scores) suitable for preventive screening and follow-up in high-risk patients. What is new compared to current research – We turn ULM track data into an explicit microvascular fatigue geometry (VSI + NFS), rather than only mapping perfusion or velocity. – We introduce a physically-gated tracking ledger tailored for clinical auditability, complementary to existing Kalman/GLMB/LSTM tracking schemes. – We frame stress-responsive contrast as a diagnostic, repeatable “risk paint” layer on top of CEUS, instead of purely theranostic nanoparticles. – We design a full, low-cost stroke-risk exam that could realistically be implemented on current CEUS equipment, rather than a single algorithm or phantom study.