Identifier
10.1364/BOE.490199
Publication Date
8-1-2023
Journal
Biomedical Optics Express
DOI
10.1364/BOE.490199
PMID
37799697
PMCID
PMC10549754
PubMedCentral® Posted Date
7-10-2023
PubMedCentral® Full Text Version
Post-print
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 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.
Published Open-Access
yes
Recommended Citation
Wu, Jimin; Boominathan, Vivek; Veeraraghavan, Ashok; et al., "Real-Time, Deep-Learning Aided Lensless Microscope" (2023). Faculty, Staff and Students Publications. 6289.
https://digitalcommons.library.tmc.edu/baylor_docs/6289