Multidimensional light field endoscope for robotic and AI guided surgery.

Area of Science:

• Biomedical Engineering
• Surgical Technology
• Optical Imaging

Background:

• Robotic surgery and AI-driven diagnostics require advanced imaging for precision.
• Current endoscopes are integrating 3D, near-infrared (NIR) fluorescence, and AI capabilities.
• Mimicking biological vision systems offers potential for enhanced endoscopic imaging.

Purpose of the Study:

• To develop a multidimensional light field endoscope for enhanced robotic and AI-guided surgical procedures.
• To integrate stereoscopic vision, polarized spectral vision, and NIR fluorescence imaging.
• To improve surgical navigation, lesion detection, and vital organ identification.

Main Methods:

• Designed a multidimensional light field endoscope capturing multi-view, multi-polarization color images and NIR fluorescence.
• Developed algorithms for color image restoration, 3D reconstruction, and polarization extraction.
• Validated imaging performance through experimental verification of different imaging channels.

Main Results:

• The endoscope successfully acquired three primary color images with distinct polarization states from two viewpoints.
• Achieved scene color 3D reconstruction and polarization degree computation.
• Acquired Indocyanine Green (ICG) NIR fluorescence information without electronic staining, enabling clear lesion and organ distinction.

Conclusions:

• The multidimensional light field endoscope provides crucial data for intelligent robotic and AI-guided surgery.
• 3D color imaging aids surgical navigation, while polarization and fluorescence data enhance diagnostic capabilities.
• This technology represents a significant advancement towards 'wisdom-eyes' for future surgical systems.