Computational microscopy for fast widefield deep-tissue fluorescence imaging using a commercial dual-cannula probe

Area of Science:

• Biomedical Optics
• Neuroimaging
• Microscopy

Background:

• Traditional micro-endoscopes face limitations in field of view and imaging depth.
• Deep neural networks offer potential for advanced image reconstruction from optical signals.

Purpose of the Study:

• To develop a micro-endoscope system capable of deep tissue fluorescence imaging with an expanded field of view.
• To leverage deep learning for reconstructing images from collected fluorescence data.

Main Methods:

• Utilized a solid-glass cannula as a micro-endoscope for light delivery and fluorescence collection.
• Employed deep neural networks for image reconstruction from intensity distributions.
• Used a dual-cannula probe and separate neural networks to double the field of view.

Main Results:

• Demonstrated ex vivo imaging of fluorescent beads and brain slices.
• Achieved in vivo imaging from whole brains with clear resolution of 4 μm beads.
• Obtained images from a depth of approximately 1.2 mm in whole brain tissue.
• Achieved a field of view of 0.2 mm (diameter) per cannula.

Conclusions:

• The developed micro-endoscope system effectively expands the field of view for deep tissue fluorescence imaging.
• Deep neural network-based image reconstruction enables high-resolution imaging without scanning.
• The system shows promise for fast, widefield fluorescence imaging in biological tissues.