Quantitative laser speckle blood flow imaging using event cameras

Vascular abnormalities are closely associated with the pathogenesis and progression of numerous diseases, such as thrombosis, tumors, and diabetes. Blood flow velocity serves as a critical biomarker for evaluating perfusion status. Quantitative detection of full-field blood flow variations in lesion areas holds significant scientific and clinical value for pathological studies, diagnosis, and intraoperative monitoring of related diseases. While laser speckle contrast imaging (LSCI) enables full-field blood flow visualization, its reliance on frame-based sensors necessitates handling massive data volumes, leading to inherent trade-offs among spatiotemporal resolution, real-time performance, and quantitative capabilities. Leveraging the asynchronous dynamic sensing, high temporal sampling rate, and low data redundancy of event cameras, this study proposes a quantitative blood flow imaging method termed laser speckle event imaging (LSEI). Experiments using off-the-shelf event cameras demonstrate that LSEI achieves real-time blood flow imaging with minimal computational overhead compared to frame-based LSCI. Furthermore, we investigate the relationship between event data streams and flow velocity through spatial-temporal autocorrelation analysis, enabling quantitative measurements without compromising temporal or spatial resolution. In in vivoimaging experiments of mouse ear blood flow, LSEI exhibits superior imaging details and real-time performance over conventional methods. The proposed approach holds promise as an efficient tool for diagnosis, therapeutic evaluation, and research on vascular-related diseases.