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 NIR-I (700-1,000 nm) or the NIR-IIa' windows (1,000-1,300 nm), which not optimal in terms of spatial resolution and contrast since their light scattering is higher than the NIR-IIb window (1,500-1,700 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 (GAN) 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. This article is protected by copyright. All rights reserved.