Indocyanine green based fluorescence imaging improved by deep learning.
Xiao XiongLi HeQingchao MaYihan WangKe LiZhongliang WangXue-Li ChenShouping ZhuYonghua ZhanXu CaoPublished in: Journal of biophotonics (2023)
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.
Keyphrases
- fluorescence imaging
- photodynamic therapy
- deep learning
- convolutional neural network
- magnetic resonance
- high resolution
- minimally invasive
- primary care
- machine learning
- artificial intelligence
- optical coherence tomography
- risk assessment
- drug delivery
- patients undergoing
- magnetic resonance imaging
- quality improvement
- coronary artery bypass
- atrial fibrillation
- light emitting
- solid state
- bioinformatics analysis