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We report a deep learning-based volumetric imaging framework that uses sparse 2D-scans captured by standard wide-field fluorescence microscopy at arbitrary axial positions within the sample. Through the design of a recurrent neural network, the information from different input planes is blended, and virtually propagated in space to rapidly reconstruct the sample volume over an extended axial range. We validated this deep-learning-based volumetric imaging framework using C. Elegans and nanobead samples to demonstrate a 30-fold reduction in the number of required scans. This versatile and rapid volumetric imaging technique reduces the photon dose on the sample and improves the temporal resolution.
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