Event2Flow: Scalable imaging of peripheral and cerebral hemodynamics with event-based vision sensors

Abstract

Accurate blood flow mapping over mesoscale fields of view is essential for understanding physiological and pathological processes, yet conventional optical methods often rely on bulky high-speed cameras that generate massive datasets with excessive computation burden. Here, we introduce Event2Flow, a compact and data-efficient framework leveraging event-based vision sensors, which asynchronously capture brightness changes with sub-millisecond latency and minimal data redundancy. Event2Flow supports multiple contrast mechanisms for flow measurement, including speckle fluctuation and particle tracking. By correlating the event count with flow velocity through simulations and experiments, we first demonstrate its application in laser speckle imaging for noninvasive mapping of mouse ear vasculature and ethanol-induced hemodynamic changes. When integrated with widefield fluorescence localization microscopy and point spread function engineering, Event2Flow further enables kilohertz-rate particle tracking for rapid 3D velocity quantification in transcranial brain imaging and snapshot flow direction estimations using event polarity. Overall, Event2Flow offers a scalable alternative to conventional high-speed imaging systems for vascular and neuroimaging applications.