Deep-tissue anatomical imaging of mice using carbon nanotube fluorophores in the second near-infrared window

By Kevin D. Welsher, S. Sherlock, Hongjie Dai. Proceedings of the National Academy of Sciences of the United States of America, 2011

858 citations recorded by the source corpus.

Fluorescent imaging in the second near-infrared window (NIR II, 1–1.4 μm) holds much promise due to minimal autofluorescence and tissue scattering. Here, using well-functionalized biocompatible single-walled carbon nanotubes (SWNTs) as NIR II fluorescent imaging agents, we performed high-frame-rate video imaging of mice during intravenous injection of SWNTs and investigated the path of SWNTs through the mouse anatomy. We observed in real-time SWNT circulation through the lungs and kidneys several seconds postinjection, and spleen and liver at slightly later time points. Dynamic contrast-enhanced imaging through principal component analysis (PCA) was performed and found to greatly increase the anatomical resolution of organs as a function of time postinjection. Importantly, PCA was able to discriminate organs such as the pancreas, which could not be resolved from real-time raw images. Tissue phantom studies were performed to compare imaging in the NIR II region to the traditional NIR I biological transparency window (700–900 nm). Examination of the feature sizes of a common NIR I dye (indocyanine green) showed a more rapid loss of feature contrast and integrity with increasing feature depth as compared to SWNTs in the NIR II region. The effects of increased scattering in the NIR I versus NIR II region were confirmed by Monte Carlo simulation. In vivo fluorescence imaging in the NIR II region combined with PCA analysis may represent a powerful approach to high-resolution optical imaging through deep tissues, useful for a wide range of applications from biomedical research to disease diagnostics.

Subfields: Biomedical Engineering, Physiology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/950d6b367673d148b260e9c32d9766e00625da56

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In Biomedical Engineering

  1. A. Antaris
  2. Guosong Hong
  3. Shuo Diao
  4. Qiangbin Wang
  5. Yejun Zhang
  6. Guangcun Chen
  7. Hongjie Dai
  8. Zhuang Liu

In Physiology

  1. Guosong Hong
  2. Hongjie Dai

In Radiology, Nuclear Medicine and Imaging

  1. Guosong Hong
  2. A. Antaris
  3. Qiangbin Wang
  4. Hongjie Dai

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