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Mask-based integrated fluorescence microscopy is a compact imaging technique for biomedical research. It can perform snapshot 3D imaging through a thin optical mask with a scalable field of view (FOV). Integrated microscopy uses computational algorithms for object reconstruction, but efficient reconstruction algorithms for large-scale data have been lacking. Here, we developed DeepInMiniscope, a miniaturized integrated microscope featuring a custom-designed optical mask and an efficient physics-informed deep learning model that markedly reduces computational demand. Parts of the 3D object can be individually reconstructed and combined. Our deep learning algorithm can reconstruct object volumes over 4 millimeters by 6 millimeters by 0.6 millimeters. We demonstrated substantial improvement in both reconstruction quality and speed compared to traditional methods for large-scale data. Notably, we imaged neuronal activity with near-cellular resolution in awake mouse cortex, representing a substantial leap over existing integrated microscopes. DeepInMiniscope holds great promise for scalable, large-FOV, high-speed, 3D imaging applications with compact device footprint.
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Real-time, deep-learning aided lensless microscope
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.
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- Award ID(s):
- 1730574
- PAR ID:
- 10430588
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Biomedical Optics Express
- Volume:
- 14
- Issue:
- 8
- ISSN:
- 2156-7085
- Format(s):
- Medium: X Size: Article No. 4037
- Size(s):
- Article No. 4037
- Sponsoring Org:
- National Science Foundation
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