ArticleBiomedical optics express2023
Real-time, deep-learning aided lensless microscope.
Article in Biomedical optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- Deep-learning endomicroscope with large field-of-view and depth-of-field for real-time in vivo imaging of epithelial cancer hallmarks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Coordinate-conditioned Deconvolution for Scalable Spatially Varying High-Throughput Imaging.IEEE transactions on computational imaging · 2026Article
- A golay metalens for long-range, large aperture, thermal imaging via sparse aperture computational imaging.Nature communications · 2025Article
- DeepInMiniscope: Deep learning-powered physics-informed integrated miniscope.Science advances · 2025Article
- Lensless imaging with a programmable Fresnel zone aperture.Science advances · 2025Article
- Advances in Portable Optical Microscopy Using Cloud Technologies and Artificial Intelligence for Medical Applications.Sensors (Basel, Switzerland) · 2024Review
- Wide-field, high-resolution reconstruction in computational multi-aperture miniscope using a Fourier neural network.Optica · 2024Article
- Mesoscopic calcium imaging in a head-unrestrained male non-human primate using a lensless microscope.Nature communications · 2024Article
- Real-time, deep-learning aided lensless microscope.Biomedical optics express · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Traditional miniaturized fluorescence microscopes are critical tools for modern biology. Invariably, they struggle to simultaneously image with a high spatial resolution and a large field of view (FOV). Lensless microscopes offer a solution to this limitation. However, real-time visualization of samples is not possible with lensless imaging, as image reconstruction can take minutes to complete. This poses a challenge for usability, as real-time visualization is a crucial feature that assists users in identifying and locating the imaging target. The issue is particularly pronounced in lensless microscopes that operate at close imaging distances. Imaging at close distances requires shift-varying deconvolution to account for the variation of the point spread function (PSF) across the FOV. Here, we present a lensless microscope that achieves real-time image reconstruction by eliminating the use of an iterative reconstruction algorithm. The neural network-based reconstruction method we show here, achieves more than 10000 times increase in reconstruction speed compared to iterative reconstruction. The increased reconstruction speed allows us to visualize the results of our lensless microscope at more than 25 frames per second (fps), while achieving better than 7 µm resolution over a FOV of 10 mm
Identifiers
What OpenQuestion holds
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.