We report a label-free, field-portable, holographic imaging flow cytometer that can automatically detect and count Giardia lamblia cysts in water samples with a throughput of 100 mL/h. Our cytometer has the dimensions of 19×19×16 cm and a laptop computer-connected to it reconstructs the phase and intensity images of the flowing microparticles in the sample at three different wavelengths and classifies them by a trained convolutional neural network, thereby detecting the Giardia cysts in real time. We experimentally demonstrated that our system can detect Giardia contamination in fresh and seawater samples containing as low as <10 cysts/50 mL.
Label-free optical imaging is valuable for studying fragile biological phenomena where chemical and/or optical damages associated with exogenous labelling of biomolecules are not wanted. Molecular vibrational (MVI) and quantitative phase imaging (QPI) are the two most-established label-free imaging methods that provide biochemical and morphological information of the sample, respectively. While these methods have pioneered numerous important biological analyses along their intensive technological development over the past twenty years, their inherent limitations are still left unresolved. In this contribution, we present a unified imaging scheme that bridges the technological gap between MVI and QPI, achieving simultaneous and in-situ integration of the two complementary label-free contrasts using the midinfrared (MIR) photothermal effect. Our method is a super-resolution MIR imaging where vibrational resonances induced by wide-field MIR excitation and the resulting photothermal RI changes are detected and localized with the spatial resolution determined by a visible-light-based QPI system. We demonstrate applicability of this method, termed MV-sensitive QPI (MV-QPI), to live-cell imaging. Our MV-QPI method could allow for quantitative mapping of subcellular biomolecular distributions within the global cellular morphology in a label-free and damage-less manner, providing more comprehensive pictures of complex and fragile biological activities.
We introduce a field-portable and cost-effective holographic imaging flow cytometer, which provides phase contrast microscopic images of the contents of water samples at a throughput of 100 ml/h. This imaging cytometer uses a high power multi-colored LED and a custom designed circuit to illuminate continuously flowing water samples with short-pulses of red, green, and blue light that are simultaneously on, thereby eliminating motion blur and making the system vibration resistant. The recorded color holograms are segmented and reconstructed in real time and are phase recovered using a deep learning-based algorithm. Weighing 1kg with the dimensions of 15.5 cm × 15 cm × 12.5 cm, our label-free imaging flow-cytometer is controlled by a laptop computer equipped with a graphical processing unit. We tested the capabilities of our field-portable device by imaging micro- and nano-plankton inside ocean water samples collected at six beaches along the California coastline. We also determined Pseudo-Nitzschia algae concentration of these samples, providing a good agreement with the measurements made by the California Department of Public Health. Our device represents 1-2 orders of magnitude reduction in the cost and size of an imaging flow cytometer compared to state-of-the-art designs, while providing a similar or better performance in terms of volumetric throughput, detection limit and imaging resolution.
The Sparsity of the Gradient (SoG) is a robust autofocusing criterion for holography, where the gradient modulus of the complex refocused hologram is calculated, on which a sparsity metric is applied. Here, we compare two different choices of sparsity metrics used in SoG, specifically, the Gini index (GI) and the Tamura coefficient (TC), for holographic autofocusing on dense/connected or sparse samples. We provide a theoretical analysis predicting that for uniformly distributed image data, TC and GI exhibit similar behavior, while for naturally sparse images containing few high-valued signal entries and many low-valued noisy background pixels, TC is more sensitive to distribution changes in the signal and more resistive to background noise. These predictions are also confirmed by experimental results using SoG-based holographic autofocusing on dense and connected samples (such as stained breast tissue sections) as well as highly sparse samples (such as isolated Giardia lamblia cysts). Through these experiments, we found that ToG and GoG offer almost identical autofocusing performance on dense and connected samples, whereas for naturally sparse samples, GoG should be calculated on a relatively small region of interest (ROI) closely surrounding the object, while ToG offers more flexibility in choosing a larger ROI containing more background pixels.
Forest fires are a major source of particulate matter (PM) air pollution on a global scale. The composition and impact of PM are typically studied using only laboratory instruments and extrapolated to real fire events owing to a lack of analytical techniques suitable for field-settings. To address this and similar field test challenges, we developed a mobilemicroscopy- and machine-learning-based air quality monitoring platform called c-Air, which can perform air sampling and microscopic analysis of aerosols in an integrated portable device. We tested its performance for PM sizing and morphological analysis during a recent forest fire event in La Tuna Canyon Park by spatially mapping the PM. The result shows that with decreasing distance to the fire site, the PM concentration increases dramatically, especially for particles smaller than 2 µm. Image analysis from the c-Air portable device also shows that the increased PM is comparatively strongly absorbing and asymmetric, with an aspect ratio of 0.5–0.7. These PM features indicate that a major portion of the PM may be open-flame-combustion-generated element carbon soot-type particles. This initial small-scale experiment shows that c-Air has some potential for forest fire monitoring.
We present a coherent Raman scattering (CRS) spectroscopy technique achieving a CRS spectral acquisition rate of 50,000 spectra/second over a Raman spectral region of 200 - 1430 cm-1 with a resolution of 4.2 cm-1. This ultrafast, broadband and high-resolution CRS spectroscopic performance is realized by a polygonal Fourier-domain delay line serving as an ultra-rapid optical-path-length scanner in a broadband Fourier-transform coherent anti-Stokes Raman scattering (CARS) spectroscopy platform. We present a theoretical description of the technique and demonstrate continuous, ultrafast, broadband, and high-resolution CARS spectroscopy on a liquid toluene sample using our proof-of-concept setup.
We propose and demonstrate an optical component that overcomes critical limitations in our previously demonstrated high-speed multispectral videography—a method in which an array of periscopes placed in a prism-based spectral shaper is used to achieve snapshot multispectral imaging with the frame rate only limited by that of an image-recording sensor. The demonstrated optical component consists of a slicing mirror incorporated into a 4f-relaying lens system that we refer to as a spectrum slicer (SS). With its simple design, we can easily increase the number of spectral channels without adding fabrication complexity while preserving the capability of high-speed multispectral videography. We present a theoretical framework for the SS and its experimental utility to spectral imaging by showing real-time monitoring of a dynamic colorful event through five different visible windows.
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