A novel contact optimization algorithm for endomicroscopic surface scanning

Area of Science:

• Medical imaging
• Optical engineering
• Biomedical technology

Background:

• Probe-based confocal laser endomicroscopy (pCLE) enables real-time, cell-level imaging crucial for early cancer detection.
• Limitations in pCLE include the narrow field-of-view, necessitating large area surface scanning.
• Maintaining stable probe-tissue contact is essential for acquiring high-quality images during scanning.

Purpose of the Study:

• To develop and validate a novel contact optimization algorithm for high-quality pCLE image acquisition.
• To address the challenge of maintaining optimal probe-tissue distance during endomicroscopic surface scanning.
• To improve the diagnostic potential of pCLE through enhanced image quality.

Main Methods:

• A contact optimization algorithm utilizing whale optimization algorithm (a swarm intelligence technique) was developed.
• The algorithm dynamically adjusts probe position based on real-time image quality assessment.
• Total co-occurrence entropy was employed as a quantitative metric for evaluating pCLE image quality.

Main Results:

• The algorithm demonstrated effectiveness in scanning experiments on various tissues, including sponge, ex vivo swine skin, and stomach tissue.
• High-quality, detailed mosaic images were generated across different scanning trajectories (spiral, circle, raster) without blurring.
• Consistent and optimal probe-tissue contact was achieved, leading to superior image quality.

Conclusions:

• The developed contact optimization algorithm successfully determines the optimal probe-tissue distance for pCLE imaging.
• This method significantly improves the quality of pCLE images, particularly during surface scanning.
• The findings highlight the method's high potential for advancing endomicroscopic surface scanning applications in diagnostics.