Journal article
R. Guo, Reed Sorenson, Robert Scharf, Austin Koch, A. Groover, L. Sieburth, S. Blair, R. Menon
Optics Express, 2022
Optics Express, 2022
Semantic Scholar
DOI
PubMed
Cite
Cite
APA
Click to copy
Guo, R., Sorenson, R., Scharf, R., Koch, A., Groover, A., Sieburth, L., … Menon, R. (2022). Overcoming the field-of-view to diameter trade-off in microendoscopy via computational optrode-array microscopy. Optics Express.
Chicago/Turabian
Click to copy
Guo, R., Reed Sorenson, Robert Scharf, Austin Koch, A. Groover, L. Sieburth, S. Blair, and R. Menon. “Overcoming the Field-of-View to Diameter Trade-off in Microendoscopy via Computational Optrode-Array Microscopy.” Optics Express (2022).
MLA
Click to copy
Guo, R., et al. “Overcoming the Field-of-View to Diameter Trade-off in Microendoscopy via Computational Optrode-Array Microscopy.” Optics Express, 2022.
BibTeX Click to copy
@article{r2022a,
title = {Overcoming the field-of-view to diameter trade-off in microendoscopy via computational optrode-array microscopy.},
year = {2022},
journal = {Optics Express},
author = {Guo, R. and Sorenson, Reed and Scharf, Robert and Koch, Austin and Groover, A. and Sieburth, L. and Blair, S. and Menon, R.}
}
Abstract
High-resolution microscopy of deep tissue with large field-of-view (FOV) is critical for elucidating organization of cellular structures in plant biology. Microscopy with an implanted probe offers an effective solution. However, there exists a fundamental trade-off between the FOV and probe diameter arising from aberrations inherent in conventional imaging optics (typically, FOV < 30% of diameter). Here, we demonstrate the use of microfabricated non-imaging probes (optrodes) that when combined with a trained machine-learning algorithm is able to achieve FOV of 1x to 5x the probe diameter. Further increase in FOV is achieved by using multiple optrodes in parallel. With a 1 × 2 optrode array, we demonstrate imaging of fluorescent beads (including 30 FPS video), stained plant stem sections and stained living stems. Our demonstration lays the foundation for fast, high-resolution microscopy with large FOV in deep tissue via microfabricated non-imaging probes and advanced machine learning.