Indocyanine green‐based fluorescence imaging improved by deep learning

Abstract Intraoperative identification of malignancies using indocyanine green (ICG)‐based fluorescence imaging could provide real‐time guidance for surgeons. Existing ICG‐based fluorescence imaging mostly operates in the near‐infrared (NIR)‐I (700–1000 nm) or the NIR‐IIa′ windows (1000–1300 nm), which is not optimal in terms of spatial resolution and contrast as their light scattering is higher than the NIR‐IIb window (1500–1700 nm). It is highly desired to achieve ICG‐based fluorescence imaging in the NIR‐IIb window, but it is hindered by its ultra‐low NIR‐IIb emission tail of ICG. Herein, we employ a generative adversarial network to generate NIR‐IIb ICG images directly from the acquired NIR‐I ICG images. This approach was investigated by in vivo imaging of sub‐surface vascular, intestine structure, and tumors, and their results demonstrated significant improvement in spatial resolution and contrast for ICG‐based fluorescence imaging. It is potential for deep learning to improve ICG‐based fluorescence imaging in clinical diagnostics and image‐guided surgery in clinics.