Three-dimensional fluorescence microscopy through virtual refocusing using a recursive light propagation network

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Three-dimensional fluorescence microscopy has an intrinsic performance limit set by the number of photons that can be collected from the sample in a given time interval. Here, we extend our earlier work - a recursive light propagation network (RLP-Net) - which is a computational microscopy technique that overcomes such limitations through virtual refocusing that enables volume reconstruction from two adjacent 2-D wide-field fluorescence images. RLP-Net employs a recursive inference scheme in which the network progressively predicts the subsequent planes along the axial direction. This recursive inference scheme reflects that the law of physics for the light propagation remains spatially invariant and therefore a fixed function (i.e., a neural network) for a short distance light propagation can be recursively applied for a longer distance light propagation. In addition, we employ a self-supervised denoising method to enable accurate virtual light propagation over a long distance. We demonstrate the capability of our method through high-speed volumetric imaging of neuronal activity of a live zebrafish brain. The source code used in the paper is available at https://github.com/NICALab/rlpnet.
Publisher
ELSEVIER
Issue Date
2022-11
Language
English
Article Type
Article
Citation

MEDICAL IMAGE ANALYSIS, v.82

ISSN
1361-8415
DOI
10.1016/j.media.2022.102600
URI
http://hdl.handle.net/10203/298959
Appears in Collection
EE-Journal Papers(저널논문)
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