We introduce an innovative MCAM architecture using a 6x8 array of 48 lenses and sensors for simultaneous 0.624 gigapixel imaging within a few centimeters, delivering near-cellular resolution. This enables 3D video recording and radiometric fluorescence imaging of organisms using stereoscopic capture and appropriate filters. Such a feature proves advantageous when conducting combined investigations into organism behavior and functional fluorescence measurements. Moreover, the MCAM is equipped to perform birefringent imaging by incorporating suitable polarizers. We demonstrate the multimodal imaging capacity of this system using a variety of specimens, notably Drosophila, and zebrafish.
KEYWORDS: Organisms, Microscopes, Tomography, Algorithm development, 3D modeling, Detection and tracking algorithms, 3D tracking, 3D image processing, Reconstruction algorithms, Muscles
It is challenging to study behavior of and track freely-moving model organisms using conventional 3D microscopy techniques. To overcome motion artifacts and prevent the organism from leaving the field of view (FOV), existing techniques require paralyzing or otherwise immobilizing the organism. Here, we demonstrate hemispheric Fourier light field tomography, featuring a parabolic objective that enables synchronized multi-view fluorescence imaging over ~2pi steradians at up to 120 fps and across multi-millimeter 3D FOVs. Our method is not only able to track the 6D pose of freely-moving zebrafish and fruit fly larvae, but also other properties such as heartbeat, fin motion, jaw motion, and muscle contractions. We also demonstrate simultaneous multi-organism imaging.
KEYWORDS: Data modeling, Biomedical optics, Education and training, Machine learning, Imaging systems, Visualization, Systems modeling, Image quality, White blood cells, Visual process modeling
We present a scheme termed Hardware Domain Adaptation that transforms the visual appearance of biomedical images to match that of a given optical system. This allows us to exploit large publicly available datasets for the training of custom machine learning algorithms for inference on data sets captured by a different imaging hardware for the same task. Moreover, this method allows us to train models for lower-quality image datasets that are difficult or impossible to annotate manually. We demonstrate the efficacy of this method by using publicly available data to train an algorithm to identify and count white blood cells in images obtained on our custom hardware.
“Anyone who uses a microscope has likely noticed the limitation of the trade-off between the field of view and the resolution”. To acquire highly resolved images at large fields of view, existing techniques typically record sequential images at different positions and then digitally stitch composite images. There are alternatives to this mechanical scanning procedure, such as structured illumination microscopy or Fourier ptychography that record sequential images at varying illuminations prevent mechanical scanning for high-resolution image composites. However, all of these approaches require sequential images and thus compromise speed, temporal resolution and experimental throughput. Here we present the Multi-Camera Array Microscope (MCAM), which is a microscope system that utilizes an array of many synchronized cameras, each with an individual imaging lens, for simultaneous image capture. The MCAM enables enhanced imaging capabilities and novel applications in various scientific and medical fields, by combining the images acquired from each individual camera-lens pair.
We present a high-throughput computational imaging system capable of performing dense, volumetric fluorescence imaging of freely moving organisms at up to 120 volumes per second. Our method, termed 2pi Fourier light field tomography (2pi-FLIFT), consists of a planar array of 54 cameras and a parabolic mirror serving as the primary objective that allows for synchronized multi-view video capture over ~2pi steradians. 2pi-FLIFT features a novel 3D reconstruction algorithm that recovers both the 3D fluorescence distribution and attenuation map of dynamic samples. We demonstrate 2pi-FLIFT on important, freely moving model organisms, such as zebrafish and fruit fly larvae.
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