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Real-Time High-Definition Hyperspectral Endoscopy via Spatial-Temporal Low-Frequency-Stochastic Spectral Encoding.

Xiaowei Liu1, Jiakang Shao2, Julin Xiao1,3,4

  • 1Research Centre for Frontier Fundamental Studies, Zhejiang Lab, Hangzhou, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|January 15, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hyperspectral endoscopy technique using spatial-temporal spectral encoding and an attention network. It achieves real-time imaging for improved in vivo diagnostics and monitoring during minimally invasive procedures.

Keywords:
diffuse reflectancehyperspectral endoscopylow‐frequency stochastic filterneural networkspectrum encoding

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Area of Science:

  • Biomedical optics
  • Medical imaging
  • Endoscopy

Background:

  • Hyperspectral endoscopy offers detailed in vivo visualization but is limited by low frame rates.
  • Low frame rates hinder the capture of moving tissues, impacting diagnostics and monitoring during procedures.

Purpose of the Study:

  • To develop a low-latency hyperspectral endoscopy system for real-time in vivo imaging.
  • To overcome motion artifacts and enable high-resolution spectral analysis of tissues.

Main Methods:

  • Implemented a spatial-temporal spectral encoding approach with low-frequency stochastic filters.
  • Utilized an encoding-guided spectral attention network (ESANet) for hyperspectral image reconstruction.
  • Developed a prototype system achieving 20 Hz frame rate and 67 spectral channels (420-750 nm).

Main Results:

  • Achieved real-time frame rate (20 Hz) and high-definition resolution, overcoming tissue motion.
  • Captured fine superficial features, including capillaries as small as 37 µm.
  • Visualized distinct spectral characteristics and rapid compositional changes in photodynamic therapy and hepatic ischemia models.

Conclusions:

  • The proposed hyperspectral endoscopy system provides a cost-efficient and adaptable solution.
  • Enables enhanced in vivo diagnostics, efficacy assessment, and risk monitoring.
  • Has broad applicability for various medical imaging scenarios.