KEYWORDS: Adaptive optics, Super resolution microscopy, Microscopes, Super resolution, 3D image processing, Stereoscopy, Optical aberrations, Microscopy, Luminescence, Imaging systems
Super-resolution microscopy allows the observation of sub-cellular structures with a resolution beyond diffraction limit of conventional fluorescence microscopy. However, most super-resolution microscopes have a limited imaging depth due to the inhomogeneous refractive index of the sample that leads to optical aberrations. Adaptive optics has been successfully adopted by many imaging techniques, including 3D Structured illumination microscopy (SIM). We use a fast deformable mirror to modulate the wavefront of fluorescence to compensate for optical aberration and changing focus position at the same time. Adaptive optics successfully extends the depth, the range and the speed of 3D-SIM imaging.
Aberrations are a common problem in microscopes resulting in compromised imaging contrast and resolution. Adaptive optics (AO) can correct aberrations but requires either a wavefront sensor or a wavefront-sensorless AO method that requires multiple sample exposures.
We created a machine learning (ML) approach that embeds physical understanding of the imaging process into a sensorless AO method. This enables correction of aberrations with as few as two sample exposures. The method was translated across different microscope modalities. This includes two-photon microscopy and three-photon microscopy of in vivo mouse neural activity, showing robustness to specimen motion and activity related intensity variations.
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