The Menon Lab

Advancing Optics, Nanofabrication & Computation.



Computational cannula microscopy of neurons using neural networks.


Journal article


R. Guo, Zhimeng Pan, Andrew V. Taibi, J. Shepherd, R. Menon
Optics Letters, 2020

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APA   Click to copy
Guo, R., Pan, Z., Taibi, A. V., Shepherd, J., & Menon, R. (2020). Computational cannula microscopy of neurons using neural networks. Optics Letters.


Chicago/Turabian   Click to copy
Guo, R., Zhimeng Pan, Andrew V. Taibi, J. Shepherd, and R. Menon. “Computational Cannula Microscopy of Neurons Using Neural Networks.” Optics Letters (2020).


MLA   Click to copy
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.}
}

Abstract

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.


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