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We present the Fourier Light field Camera Array Microscope (FL-CAM) for high-throughput, single-snapshot 3D imaging. The FL-CAM substitutes a synchronized array of 48 independent imaging systems for micro-lens array of typical light field systems. 

We present amulti-modal fiber array snapshot technique (M-FAST)based on an array of 96 compact cameras placed behind a primary objective lens and a fiber bundle array. Our technique is capable of large-area, high-resolution, multi-channel video acquisition. The proposed design provides two key improvements to prior cascaded imaging system approaches: a novel optical arrangement that accommodates the use of planar camera arrays, and a new ability to acquire multi-modal image data acquisition. M-FAST is a multi-modal, scalable imaging system that can acquire snapshot dual-channel fluorescence images as well as differential phase contrast measurements over a large 6.59 mm × 9.74 mm field-of-view at 2.2-μm center full-pitch resolution.

 

We present a computational 3D profilometric microscope employing an array of 54 cameras and 3-axis scanning to produce multi-TB datasets per sample. Using stereo and sharpness cues, our self-supervised reconstruction algorithm generates 6-gigapixel reconstructions with micron-scale resolution across >110 cm². 

This paper experimentally examines different configurations of a multi-camera array microscope (MCAM) imaging technology. The MCAM is based upon a densely packed array of “micro-cameras” to jointly image across a large field-of-view (FOV) at high resolution. Each micro-camera within the array images a unique area of a sample of interest, and then all acquired data with 54 micro-cameras are digitally combined into composite frames, whose total pixel counts significantly exceed the pixel counts of standard microscope systems. We present results from three unique MCAM configurations for different use cases. First, we demonstrate a configuration that simultaneously images and estimates the 3D object depth across a 100×135mm²FOV at approximately 20 µm resolution, which results in 0.15 gigapixels (GP) per snapshot. Second, we demonstrate an MCAM configuration that records video across a continuous 83×123mm²FOV with twofold increased resolution (0.48 GP per frame). Finally, we report a third high-resolution configuration (2 µm resolution) that can rapidly produce 9.8 GP composites of large histopathology specimens.

 

We present a tomographic imaging technique, termed Deep Prior Diffraction Tomography (DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high resolution from a sequence of low-resolution images collected under angularly varying illumination. DP-DT processes the multi-angle data using a phase retrieval algorithm that is extended by a deep image prior (DIP), which reparameterizes the 3D sample reconstruction with an untrained, deep generative 3D convolutional neural network (CNN). We show that DP-DT effectively addresses the missing cone problem, which otherwise degrades the resolution and quality of standard 3D reconstruction algorithms. As DP-DT does not require pre-captured data or pre-training, it is not biased towards any particular dataset. Hence, it is a general technique that can be applied to a wide variety of 3D samples, including scenarios in which large datasets for supervised training would be infeasible or expensive. We applied DP-DT to obtain 3D RI maps of bead phantoms and complex biological specimens, both in simulation and experiment, and show that DP-DT produces higher-quality results than standard regularization techniques. We further demonstrate the generality of DP-DT, using two different scattering models, the first Born and multi-slice models. Our results point to the potential benefits of DP-DT for other 3D imaging modalities, including X-ray computed tomography, magnetic resonance imaging, and electron microscopy.

 

Abstract The last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow us to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. A vibrant and diverse community is now working in this field, which has revolutionized the prospect of diffraction-limited imaging at depth in tissues. This roadmap highlights several key aspects of this fast developing field, and some of the challenges and opportunities ahead.