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.
Wavefront-sensorless adaptive optics methods are often used to correct phase aberrations in optical systems and thus to improve imaging quality. However, sensorless methods have an intrinsic disadvantage of requiring multiple images that can lead to non-desirable photo-bleaching. We have proposed a machine learning assisted aberration correction method which could correct aberrations consisting of not fewer than five Zernike modes with as few as two images. We showed that our method could be used in microscopes to provide instant aberration predictions when imaging biological samples of non-specific structures. We showed that compared to conventional function fitting sensorless adaptive optics methods, the new method corrected much faster with observable advantages. This novel method has a great potential to be used in any adaptive optics equipped microscopy for efficient sensorless aberration correction for biomedical microscopy.
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