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Deep learning-driven adaptive optics for single-molecule localization microscopy
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OA
AI
DOI：10.1038/s41592-023-02029-0.png)
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Abstract
En 中文
The inhomogeneous refractive indices of biological tissues blur and distort single-molecule emission patterns generating image artifacts and decreasing the achievable resolution of single-molecule localization microscopy (SMLM). Conventional sensorless adaptive optics methods rely on iterative mirror changes and image-quality metrics. However, these metrics result in inconsistent metric responses and thus fundamentally limit their efficacy for aberration correction in tissues. To bypass iterative trial-then-evaluate processes, we developed deep learning-driven adaptive optics for SMLM to allow direct inference of wavefront distortion and near real-time compensation. Our trained deep neural network monitors the individual emission patterns from single-molecule experiments, infers their shared wavefront distortion, feeds the estimates through a dynamic filter and drives a deformable mirror to compensate sample-induced aberrations. We demonstrated that our method simultaneously estimates and compensates 28 wavefront deformation shapes and improves the resolution and fidelity of three-dimensional SMLM through >130-mu m-thick brain tissue specimens.
Keywords:
3-DIMENSIONAL LOCALIZATION
TRANSGENIC MICE
AO-STORM
RESOLUTION
ABERRATIONS
LIMIT
