ArticleOptics express2022
Scan-less machine-learning-enabled incoherent microscopy for minimally-invasive deep-brain imaging.
Article in Optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed, 11 citations in OpenAlex.
- Demixing fluorescence time traces transmitted by multimode fibers.Nature communications · 2024Article
- 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
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Deep-brain microscopy is strongly limited by the size of the imaging probe, both in terms of achievable resolution and potential trauma due to surgery. Here, we show that a segment of an ultra-thin multi-mode fiber (cannula) can replace the bulky microscope objective inside the brain. By creating a self-consistent deep neural network that is trained to reconstruct anthropocentric images from the raw signal transported by the cannula, we demonstrate a single-cell resolution (< 10μm), depth sectioning resolution of 40 μm, and field of view of 200 μm, all with green-fluorescent-protein labelled neurons imaged at depths as large as 1.4 mm from the brain surface. Since ground-truth images at these depths are challenging to obtain in vivo, we propose a novel ensemble method that averages the reconstructed images from disparate deep-neural-network architectures. Finally, we demonstrate dynamic imaging of moving GCaMp-labelled C. elegans worms. Our approach dramatically simplifies deep-brain microscopy.
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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.