Deep Learning Based Algorithm to Identify Large Vessel Stenosis and Occlusion on Contrast Agent-free Magnetic Resonance Imaging

Large vessel occlusion detection based on clinical scales is of low sensitivity and that based on CTA needs contrast agent exposure. This study aims to develop a deep learning (DL) algorithm for detecting intracranial large vessel steno-occlusion on contrast agent-free MR techniques including DWI and ASL. The accuracy of the DL algorithm was 88.2% with a sensitivity of 88.0%, comparable to CTA-based DL algorithms with sensitivity ranging from 67% to 94%. The MR-based DL algorithm is feasible to accurately detect intracranial large vessel steno-occlusion without intervention, radiation exposure and contrast agent, which could optimize stroke workflow and guide clinical decision-making.