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  • Microadditive manufacturing enables the creation of intricate nano- and microscale components, leading to advancements in various industries.
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Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.

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Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Maryland, USA.

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations.

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Current dorsal skin flap window chambers with flat glass windows are compatible with optical coherence tomography (OCT) and multiphoton microscopy (MPM) imaging. However, light sheet fluorescence microscopy (LSFM) performs best with a cylindrical or spherical sample located between its two 90° objectives and when all sample materials have the same index of refraction (). A modified window chamber with a domed viewing window made from fluorinated ethylene propylene (FEP), with n similar to water and tissue, was designed.

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