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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: Jun 30, 2025

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis
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In Vivo Intelligent Fluorescence Endo-Microscopy by Varifocal Meta-Device and Deep Learning.

Yu-Hsin Chia1,2, Wei-Hao Liao3, Sunil Vyas2

  • 1Department of Biomedical Engineering, National Taiwan University, Taipei, 10051, Taiwan.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|March 15, 2024
PubMed
Summary
This summary is machine-generated.

A novel intelligent fluorescence endo-microscope uses a varifocal meta-lens and deep learning (DL) for fast, high-resolution 3D brain imaging in vivo. This breakthrough reduces system complexity and speeds up image acquisition for improved surgical procedures.

Keywords:
HiLo fluorescence imagingdeep learningendoscopymetalensoptical sectioningtelecentric configurationthree‐dimensional imaging

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Area of Science:

  • Optics
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Conventional endo-microscopy requires axial component movement for focus, hindering miniaturization and complicating procedures.
  • Meta-devices offer advanced light manipulation capabilities for novel optical applications.

Purpose of the Study:

  • To develop an intelligent fluorescence endo-microscope utilizing a varifocal meta-lens and deep learning (DL).
  • To enable rapid, high-resolution, in vivo 3D imaging of biological tissues, specifically mouse brains.

Main Methods:

  • Integration of a varifocal meta-lens with adjustable focal length via relative rotation.
  • Application of a deep learning (DL) network for image processing and system control.
  • Development of an intelligent fluorescence endo-microscope system for in vivo imaging.

Main Results:

  • Achieved in vivo 3D imaging of mouse brains with invariant magnification and a large field-of-view.
  • Demonstrated optical sectioning with 3 µm lateral resolution and a 2 mm focal length tuning range.
  • Obtained high-resolution images of brain vasculature and perivascular space in 0.1 s, approximately 50 times faster than conventional methods.

Conclusions:

  • The developed intelligent fluorescence endo-microscope offers significant advantages in speed, resolution, and system simplicity.
  • This technology has the potential to enhance various surgical procedures, including gastrointestinal biopsies and neurosurgery.
  • The combination of meta-optics and deep learning represents a promising advancement in medical imaging.