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- W4387130407 abstract "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-µm-thick brain tissue specimens." @default.
- W4387130407 created "2023-09-29" @default.
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- W4387130407 date "2023-09-28" @default.
- W4387130407 modified "2023-09-30" @default.
- W4387130407 title "Deep learning-driven adaptive optics for single-molecule localization microscopy" @default.
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- W4387130407 doi "https://doi.org/10.1038/s41592-023-02029-0" @default.
- W4387130407 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37770712" @default.
- W4387130407 hasPublicationYear "2023" @default.
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