Journal article
Optics Letters, 2020
APA
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Guo, R., Pan, Z., Taibi, A. V., Shepherd, J., & Menon, R. (2020). Computational cannula microscopy of neurons using neural networks. Optics Letters.
Chicago/Turabian
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Guo, R., Zhimeng Pan, Andrew V. Taibi, J. Shepherd, and R. Menon. “Computational Cannula Microscopy of Neurons Using Neural Networks.” Optics Letters (2020).
MLA
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Guo, R., et al. “Computational Cannula Microscopy of Neurons Using Neural Networks.” Optics Letters, 2020.
BibTeX Click to copy
@article{r2020a,
title = {Computational cannula microscopy of neurons using neural networks.},
year = {2020},
journal = {Optics Letters},
author = {Guo, R. and Pan, Zhimeng and Taibi, Andrew V. and Shepherd, J. and Menon, R.}
}
Computational cannula microscopy is a minimally invasive imaging technique that can enable high-resolution imaging deep inside tissue. Here, we apply artificial neural networks to enable real-time, power-efficient image reconstructions that are more efficiently scalable to larger fields of view. Specifically, we demonstrate widefield fluorescence microscopy of cultured neurons and fluorescent beads with a field of view of 200 µm (diameter) and a resolution of less than 10 µm using a cannula of diameter of only 220 µm. In addition, we show that this approach can also be extended to macro-photography.