Dual deconvolution in multiphoton structured illumination microscopy for deep-tissue super-resolution imaging.

Area of Science:

• Biomedical imaging
• Microscopy
• Optics

Background:

• Biological tissue imaging is hindered by aberrations, reducing resolution and contrast, especially for super-resolution microscopy.
• Hardware-based adaptive optics (AO) can correct aberrations but are complex and expensive, limiting their use.
• Deep-tissue imaging requires advanced techniques to overcome scattering and aberrations.

Purpose of the Study:

• To develop a computational adaptive optics (AO) framework for multiphoton structured illumination microscopy.
• To enable cost-effective, deep-tissue super-resolution imaging with minimal hardware modifications.
• To overcome the limitations of conventional imaging techniques in thick biological samples.

Main Methods:

• A computational AO framework was developed for multiphoton structured illumination microscopy.
• A camera replaced the photodetector to capture scanned image sequences.
• A dual deconvolution algorithm was used to correct excitation and emission aberrations, recovering an aberration-free object spectrum.

Main Results:

• The framework achieved deep-tissue super-resolution imaging with minimal hardware changes.
• A lateral resolution of 130 nm was obtained at a depth of 180 μm in mouse brain tissue.
• The method successfully maintained super-resolution capability where conventional deconvolution failed.

Conclusions:

• The computational AO framework offers a cost-effective and accessible alternative to hardware-based AO.
• This approach significantly expands the potential for high-resolution deep-tissue imaging in biological research.
• The technique overcomes sample-induced aberrations, improving image quality and resolution in thick specimens.