Light field microscopy is a powerful tool for fast volumetric image acquisition in biology which requires a computationally demanding and artefact-prone image reconstruction process. I will present a novel framework consisting of a hybrid light-field light-sheet microscope and deep learning-based volume reconstruction, where single light-sheet acquisitions continuously serve as training data and validation for the convolutional neural network reconstructing the LFM volume. Our framework produces video-rate reconstructions; their fidelity can be verified on demand and the network can be fine-tuned as necessary.
Capturing highly dynamic biological processes at cellular resolution is a recurring challenge in biology. Here we show that combining selective-volume illumination with simultaneous acquisition of orthogonal light-fields yields 3D images with high, isotropic spatial resolution and a significant reduction of reconstruction artefacts, thereby overcoming current limitations of light-field microscopy implementations. We demonstrate Medaka heart and blood flow imaging with single-cell resolution and free of motion artefacts at volume rates <200Hz.
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