Real-time, deep-learning aided lensless microscope

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dc.citation.firstpage4037en_US
dc.citation.issueNumber8en_US
dc.citation.journalTitleBiomedical Optics Expressen_US
dc.citation.lastpage4051en_US
dc.citation.volumeNumber14en_US
dc.contributor.authorWu, Jiminen_US
dc.contributor.authorBoominathan, Viveken_US
dc.contributor.authorVeeraraghavan, Ashoken_US
dc.contributor.authorRobinson, Jacob T.en_US
dc.date.accessioned2024-05-08T18:56:12Zen_US
dc.date.available2024-05-08T18:56:12Zen_US
dc.date.issued2023en_US
dc.description.abstractTraditional 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 mm2. This ability to reconstruct and visualize samples in real-time empowers a more user-friendly interaction with lensless microscopes. The users are able to use these microscopes much like they currently do with conventional microscopes.en_US
dc.identifier.citationWu, J., Boominathan, V., Veeraraghavan, A., & Robinson, J. T. (2023). Real-time, deep-learning aided lensless microscope. Biomedical Optics Express, 14(8), 4037–4051. https://doi.org/10.1364/BOE.490199en_US
dc.identifier.digitalboe-14-8-4037en_US
dc.identifier.doihttps://doi.org/10.1364/BOE.490199en_US
dc.identifier.urihttps://hdl.handle.net/1911/115692en_US
dc.language.isoengen_US
dc.publisherOptica Publishing Groupen_US
dc.rightsPublished under the terms of the Optica Open Access Publishing Agreementen_US
dc.rights.urihttps://opg.optica.org/library/license_v2.cfm#VOR-OAen_US
dc.titleReal-time, deep-learning aided lensless microscopeen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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