Ma Zhuoran, Wang Feifei, Wang Weizhi, Zhong Yeteng, Dai Hongjie
Department of Chemistry, Bio-X Program, Stanford University, Stanford, CA 94305.
Department of Chemistry, Bio-X Program, Stanford University, Stanford, CA 94305
Proc Natl Acad Sci U S A. 2021 Jan 5;118(1). doi: 10.1073/pnas.2021446118.
Detecting fluorescence in the second near-infrared window (NIR-II) up to ∼1,700 nm has emerged as a novel in vivo imaging modality with high spatial and temporal resolution through millimeter tissue depths. Imaging in the NIR-IIb window (1,500-1,700 nm) is the most effective one-photon approach to suppressing light scattering and maximizing imaging penetration depth, but relies on nanoparticle probes such as PbS/CdS containing toxic elements. On the other hand, imaging the NIR-I (700-1,000 nm) or NIR-IIa window (1,000-1,300 nm) can be done using biocompatible small-molecule fluorescent probes including US Food and Drug Administration-approved dyes such as indocyanine green (ICG), but has a caveat of suboptimal imaging quality due to light scattering. It is highly desired to achieve the performance of NIR-IIb imaging using molecular probes approved for human use. Here, we trained artificial neural networks to transform a fluorescence image in the shorter-wavelength NIR window of 900-1,300 nm (NIR-I/IIa) to an image resembling an NIR-IIb image. With deep-learning translation, in vivo lymph node imaging with ICG achieved an unprecedented signal-to-background ratio of >100. Using preclinical fluorophores such as IRDye-800, translation of ∼900-nm NIR molecular imaging of PD-L1 or EGFR greatly enhanced tumor-to-normal tissue ratio up to ∼20 from ∼5 and improved tumor margin localization. Further, deep learning greatly improved in vivo noninvasive NIR-II light-sheet microscopy (LSM) in resolution and signal/background. NIR imaging equipped with deep learning could facilitate basic biomedical research and empower clinical diagnostics and imaging-guided surgery in the clinic.
检测高达约1700纳米的第二近红外窗口（NIR-II）中的荧光，已成为一种新型的体内成像方式，可通过毫米级组织深度实现高空间和时间分辨率。在NIR-IIb窗口（1500 - 1700纳米）成像，是抑制光散射并最大化成像穿透深度的最有效的单光子方法，但依赖于含有有毒元素的纳米颗粒探针，如PbS/CdS。另一方面，使用生物相容性小分子荧光探针（包括美国食品药品监督管理局批准的染料，如吲哚菁绿（ICG））可以对NIR-I（700 - 1000纳米）或NIR-IIa窗口（1000 - 1300纳米）进行成像，但由于光散射，成像质量欠佳。人们非常希望使用已获批用于人体的分子探针实现NIR-IIb成像的性能。在此，我们训练了人工神经网络，将900 - 1300纳米的较短波长NIR窗口（NIR-I/IIa）中的荧光图像转换为类似于NIR-IIb图像的图像。通过深度学习转换，使用ICG进行的体内淋巴结成像实现了前所未有的>100的信噪比。使用IRDye - 800等临床前荧光团，对PD-L1或EGFR的约900纳米NIR分子成像的转换，将肿瘤与正常组织的比率从约5大幅提高到约20，并改善了肿瘤边缘定位。此外，深度学习极大地提高了体内无创NIR-II光片显微镜（LSM）的分辨率和信号/背景。配备深度学习的NIR成像可以促进基础生物医学研究，并为临床诊断和成像引导手术提供支持。
