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

Imaging in thick biological tissues is often degraded by sample-induced aberrations, which reduce resolution and contrast, particularly in super-resolution techniques. While hardware-based adaptive optics (AO) using wavefront shaping can correct these aberrations, their complexity and cost hinder widespread adoption. Here, we present a computational AO framework for multiphoton structured illumination microscopy, enabling deep-tissue super-resolution imaging with minimal hardware modifications. By replacing the photodetector with a camera from the conventional laser-scanning multiphoton microscope, we capture a sequence of scanned images. Using virtual structured illumination, we develop a dual deconvolution algorithm that independently corrects excitation and emission aberrations, recovering an aberration-free object spectrum with an extended spatial frequency bandwidth. We experimentally validate this framework through two-photon super-resolution imaging, achieving a lateral resolution of 130 nm—one-fourth of the emission wavelength—at a depth of 180 μm in thick mouse brain tissue, where conventional deconvolution fails to maintain super-resolution capability. This approach provides a cost-effective and accessible alternative to hardware-based AO, expanding the potential for high-resolution deep-tissue imaging in biological research. The authors present a computational adaptive optics framework for multiphoton structured illumination microscopy, enabling deep-tissue super-resolution imaging with minimal hardware modifications. Their dual deconvolution algorithm independently corrects excitation and emission aberrations, and they achieve a lateral resolution one-fourth of the emission wavelength at a depth of 180 µm in mouse brain tissue.