Label-Free Intraoperative Imaging of Hemodynamics using Deep Learning

Abstract

Intraoperative visualization of hemodynamics is crucial for accurate diagnosis and informed surgical decision-making. We present a label-free cross-modal generation framework to synthesize mean transition time (MTT) maps from LSCI and WLI. MTT maps encode local hemodynamics, enabling artery-vein differentiation and flow direction inference. This approach offers a fast, efficient, and contrast-free solution for continuous intraoperative surgical guidance.