ArticleOptics letters2020
Computational cannula microscopy of neurons using neural networks.
Article in Optics letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Article
- Overcoming the field-of-view to diameter trade-off in microendoscopy via computational optrode-array microscopy.Optics express · 2023Article
- Computational microscopy for fast widefield deep-tissue fluorescence imaging using a commercial dual-cannula probe.Optics continuum · 2022Article
- Scan-less machine-learning-enabled incoherent microscopy for minimally-invasive deep-brain imaging.Optics express · 2022Article
- 3D computational cannula fluorescence microscopy enabled by artificial neural networks.Optics express · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.