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Related Concept Videos

Super-resolution Fluorescence Microscopy01:37

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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Related Experiment Video

Updated: Jul 19, 2025

Indocyanine Green-Guided Intraoperative Imaging to Facilitate Video-Assisted Retroperitoneal Debridement for Treating Acute Necrotizing Pancreatitis
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Indocyanine green-based fluorescence imaging improved by deep learning.

Xiao Xiong1, Li He1, Qingchao Ma1

  • 1Engineering Research Center of Molecular, Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.

Journal of Biophotonics
|August 9, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning method to enhance indocyanine green (ICG) fluorescence imaging in the NIR-IIb window, improving visualization of subsurface structures and tumors for surgical guidance.

Keywords:
ICGNIR-INIR-IIbdeep learningsurgical navigation

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Area of Science:

  • Medical imaging
  • Biomedical engineering
  • Artificial intelligence

Background:

  • Indocyanine green (ICG)-based fluorescence imaging aids intraoperative tumor identification.
  • Current imaging in NIR-I and NIR-IIa windows has limitations in spatial resolution and contrast due to light scattering.
  • Optimal imaging is desired in the NIR-IIb window (1500-1700 nm) for enhanced surgical guidance.

Purpose of the Study:

  • To develop a method for generating high-resolution ICG fluorescence images in the NIR-IIb window.
  • To overcome the challenge of ultra-low NIR-IIb emission from ICG.
  • To improve intraoperative identification of malignancies and surgical precision.

Main Methods:

  • Utilized a generative adversarial network (GAN) to synthesize NIR-IIb ICG images from acquired NIR-I ICG images.
  • Employed in vivo imaging techniques to validate the approach.
  • Focused on imaging sub-surface vascular structures, intestinal anatomy, and tumors.

Main Results:

  • The GAN approach successfully generated NIR-IIb ICG images with significantly improved spatial resolution and contrast.
  • Demonstrated enhanced visualization of deep-seated biological structures and tumors.
  • Validated the potential of the method in preclinical in vivo models.

Conclusions:

  • Deep learning-based image generation offers a promising solution for ICG fluorescence imaging in the NIR-IIb window.
  • This technique has the potential to enhance clinical diagnostics and image-guided surgery.
  • Improved visualization can lead to better surgical outcomes and patient care.