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Results for “quantitative phase images”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

ANN↗
DOE OSTI2023

Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network

As a label‐free imaging technique, quantitative phase imaging (QPI) provides optical path length information of transparent specimens for various applications in biology, materials science, and engineering. Multispectral QPI measures quantitative phase information across multiple spectral bands, permitting the examination of wavelength‐specific phase and dispersion characteristics of samples. Herein, the design of a diffractive processor is presented that can all‐optically perform multispectral quantitative phase imaging of transparent phase‐only objects within a snapshot. The design utilizes spatially engineered diffractive layers, optimized through deep learning, to encode the phase profile of the input object at a predetermined set of wavelengths into spatial intensity variations at the output plane, allowing multispectral QPI using a monochrome focal plane array. Through numerical simulations, diffractive multispectral processors are demonstrated to simultaneously perform quantitative phase imaging at 9 and 16 target spectral bands in the visible spectrum. The generalization of these diffractive processor designs is validated through numerical tests on unseen objects, including thin Pap smear images. Due to its all‐optical processing capability using passive dielectric diffractive materials, this diffractive multispectral QPI processor offers a compact and power‐efficient solution for high‐throughput quantitative phase microscopy and spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantitative phase imaging by gradient retardance optical microscopy

Abstract Quantitative phase imaging (QPI) has become a vital tool in bioimaging, offering precise measurements of wavefront distortion and, thus, of key cellular metabolism metrics, such as dry mass and density. However, only a few QPI applications have been demonstrated in optically thick specimens, where scattering increases background and reduces contrast. Building upon the concept of structured illumination interferometry, we introduce Gradient Retardance Optical Microscopy (GROM) for QPI of both thin and thick samples. GROM transforms any standard Differential Interference Contrast (DIC) microscope into a QPI platform by incorporating a liquid crystal retarder into the illumination path, enabling independent phase-shifting of the DIC microscope's sheared beams. GROM greatly simplifies related configurations, reduces costs, and eradicates energy losses in parallel imaging modalities, such as fluorescence. We successfully tested GROM on a diverse range of specimens, from microbes and red blood cells to optically thick (~ 300 μm) plant roots without fixation or clearing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Deep learning classification of lipid droplets in quantitative phase images

We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all implemented machine learning methods, and their performance with respect to computational efficiency, required training resources, and relative method performance measured across multiple metrics. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity, and deeper insight into the thermodynamics of metabolism of single cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Fusing Quantitative-Phase Imaging with Airy Light- Sheet Microscopy

We report the integration of quantitative-phase imaging (QPI) with light-sheet (LS) fluorescent microscopy on to a standard inverted microscope that retains compatibility with microfluidics. QPI enables label-free imaging and number-density quantification of single cells and their organelles. Conversely, LS yields considerable speed and phototoxicity gains in quantifying the 4D dynamics of gene-encoded fluorescent biomarkers. We will detail the system design that relied on spatial light interferometry for QPI and an accelerating Airy- beam light-sheet for fluorescence, its performance, as well as results of a representative multivariate imaging analysis of single-cell metabolism.

imaging, lattice light sheet, Yarrowia lipolytica,↗
DOE OSTI2022

Comprehensive single-shot biophysical cytometry using simultaneous quantitative phase imaging and Brillouin spectroscopy

Abstract Single-cell analysis, or cytometry, is a ubiquitous tool in the biomedical sciences. Whereas most cytometers use fluorescent probes to ascertain the presence or absence of targeted molecules, biophysical parameters such as the cell density, refractive index, and viscosity are difficult to obtain. In this work, we combine two complementary techniques—quantitative phase imaging and Brillouin spectroscopy—into a label-free image cytometry platform capable of measuring more than a dozen biophysical properties of individual cells simultaneously. Using a geometric simplification linked to freshly plated cells, we can acquire the cellular diameter, volume, refractive index, mass density, non-aqueous mass, fluid volume, dry volume, the fractional water content of cells, both by mass and by volume, the Brillouin shift, Brillouin linewidth, longitudinal modulus, longitudinal viscosity, the loss modulus, and the loss tangent, all from a single acquisition, and with no assumptions of underlying parameters. Our methods are validated across three cell populations, including a control population of CHO-K1 cells, cells exposed to tubulin-disrupting nocodazole, and cells under hypoosmotic shock. Our system will unlock new avenues of research in biophysics, cell biology, and medicine.

Steelman, Zachary A.↗
DOE OSTI2025

Quantitative phase imaging around water vapor microbubbles under pulsed laser illumination

We measured water temperature around a self-oscillating water vapor-rich bubble using diffraction phase microscopy equipped with a pulsed laser. The water vapor-rich bubble, which causes a strong water flow around it, was generated by locally heating degassed water through the photothermal conversion properties of an FeSi2 thin film. The generation of the flow is related to the self-oscillation of the bubble at sub-MHz frequencies and the temperature distribution around it. Here, the refractive index of the surrounding water was measured using a pulsed laser with a 3.5-ns pulse duration, which guarantees a time resolution of less than 1/200 of the oscillation cycle of the bubble. The temperature distribution around the bubble was calculated from the measured refractive index.

Narihira, Kyoshiro [Kyoto University, Japan]↗
DOE OSTI2021

Deep learning for lipid droplet recognition in quantitative phase images

This library of Python code is used for performing semantic segmentation of images using 6 different machine learning methods. Five of the methods are implemented entirely within the scikit-learn framework. The Convolutional Neural Network (CNN) method requires Keras with a TensorFlow backend and generally uses a different set of scripts in order to perform the complete training and evaluation.

Sheneman, Lucas↗
DOE OSTI2020

Integrative Quantitative-Phase and Airy Light-Sheet Imaging

Light-sheet microscopy enables considerable speed and phototoxicity gains, while quantitative-phase imaging confers label-free organelle recognition and metabolic information that are inaccessible by conventional methods. We report the fusion of these two modalities onto a standard inverted microscope that retains compatibility with microfluidics. We describe the utilization of an accelerating Airy-beam light-sheet yielding identical imaging areas with interferometry, and an application in unmasking the effects of cellular noise on metabolic compartmentalization.

Biological sciences, Biological techniques, Micros↗
DOE OSTI2023

Quantum-enhanced phase imaging without coincidence counting

Quantitative phase imaging provides a way to image transparent objects, such as biological cells, and measure their thickness. We report on a phase-imaging method that achieves twice the phase shift and approximately 1.7 times the spatial resolution of an equivalent spatially and temporally coherent classical quantitative phase-imaging system by using quantum interference between successive spontaneous parametric downconversion events in a nonlinear crystal. Furthermore, our method is approximately 1000 times faster than imaging the parametric downconversion photons in coincidence, which requires measurement times on the order of tens of hours. Our method may be useful for imaging sensitive transparent objects that require low illumination intensities at near-infrared and longer illumination wavelengths, such as photosensitive biological samples.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Transport of intensity equation: a tutorial

When it comes to “phase measurement” or “quantitative phase imaging”, many people will automatically connect them with “laser” and “interferometry”. Indeed, conventional quantitative phase imaging and phase measurement techniques generally rely on the superposition of two beams with a high degree of coherence: complex interferometric configurations, stringent requirements on the environmental stabilities, and associated laser speckle noise severely limit their applications in optical imaging and microscopy. On a different note, as one of the most well-known phase retrieval approaches, the transport of intensity equation (TIE) provides a new non-interferometric way to access quantitative phase information through intensity only measurement. Despite the insufficiency for interferometry, TIE is applicable under partially coherent illuminations (like the Köhler’s illumination in a conventional microscope), permitting optimum spatial resolution, higher signal-to-noise ratio, and better image quality. In this tutorial, we give an overview of the basic principle, research fields, and representative applications of TIE, focus particularly on optical imaging, metrology, and microscopy. The purpose of this tutorial is twofold. It should serve as a self-contained introduction to TIE for readers with little or no knowledge of TIE. On the other hand, it attempts to give an overview of recent developments in this field. These results highlight a new era in which strict coherence and interferometry are no longer prerequisites for quantitative phase imaging and diffraction tomography, paving the way toward new generation label-free three-dimensional microscopy, with applications in all branches of biomedicine.

42 ENGINEERING↗
DOE OSTI2024

Label-free functional analysis of root-associated microbes with dynamic quantitative oblique back-illumination microscopy

The increasing global demand for food, coupled with concerns about the environmental impact of synthetic fertilizers, underscores the urgency of developing sustainable agricultural practices. Nitrogen-fixing bacteria, known as diazotrophs, offer a potential solution by converting atmospheric nitrogen into bioavailable forms, reducing the reliance on synthetic fertilizers. However, a deeper understanding of their interactions with plants and other microbes is needed. In this study, we introduce a recently developed label-free 3D quantitative phase imaging technology called dynamic quantitative oblique back-illumination microscopy (DqOBM) to assess the functional dynamic activity of diazotrophs in vitro and in situ. Our experiments involved three different diazotrophs (Sinorhizobium meliloti, Azotobacter vinelandii, and Rahnella aquatilis) cultured on media with amendments of carbon and nitrogen sources. Over 5 days, we observed increased dynamics in nutrient-amended media. These results suggest that the observed bacterial dynamics correlate with their metabolic activity. Furthermore, we applied qOBM to visualize microbial dynamics within the root cap and elongation zone of Arabidopsis thaliana primary roots. This allowed us to identify distinct areas of microbial infiltration in plant roots without the need for fluorescent markers. Our findings demonstrate that DqOBM can effectively characterize microbial dynamics and provide insights into plant-microbe interactions in situ, offering a valuable tool for advancing our understanding of sustainable agriculture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Quantitative phase microscopy monitors subcellular dynamics in single cells exposed to nanosecond pulsed electric fields

A substantial body of literature exists to study the dynamics of single cells exposed to short duration (<1 μs), high peak power (~1 MV/m) transient electric fields. Much of this research is limited to traditional fluorescence-based microscopy techniques, which introduce exogenous agents to the culture and are only sensitive to a single molecular target. Quantitative phase imaging (QPI) is a coherent imaging modality which uses optical path length as a label-free contrast mechanism, and has proven highly effective for the study of single-cell dynamics. In this work, we introduce QPI as a useful imaging tool for the study of cells undergoing cytoskeletal remodeling after nanosecond pulsed electric field (nsPEF) exposure. In particular, we use cell swelling, dry mass and disorder strength measurements derived from QPI phase images to monitor the cellular response to nsPEFs. We hope this demonstration of QPI's utility will lead to a further adoption of the technique for the study

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Single-shot x-ray phase-contrast and dark-field imaging based on coded binary phase mask

We introduce a coded-mask-based multi-contrast imaging method for high-resolution phase-contrast and dark-field imaging. The method uses a binary phase mask designed to provide an ultra-high-contrast pattern and reference-free single-shot measurement and an algorithm based on maximum-likelihood optimization and automatic differentiation to perform simultaneous reconstruction of absorption, phase, and dark-field object images. Further, we demonstrate that the method has great potential for real-time quantitative phase imaging and wavefront sensing when combined with deep learning.

Qiao, Zhi (ORCID:0000000286285320)↗
DOE OSTI2023

Integrated imaging assembly and method for using

Certain disclosed embodiments concern an integrated imaging system that combined light-sheet microscopy, which enables considerable speed and phototoxicity gains, with quantitative-phase imaging. A method for using such imaging systems also is disclosed. In an exemplary embodiment, an integrated imaging system was used for multivariate investigation of live-cells in microfluidics.

Vasdekis, Andreas E.↗
DOE OSTI2021

Airy light-sheet Raman imaging

Light-sheet fluorescence microscopy has greatly improved the speed and overall photostability of optically sectioning cellular and multi-cellular specimens. Similar gains have also been conferred by light-sheet Raman imaging; these schemes, however, rely on diffraction limited Gaussian beams that hinder the uniformity and size of the imaging field-of-view, and, as such, the resulting throughput rates. Here, we demonstrate that a digitally scanned Airy beam increases the Raman imaging throughput rates by more than an order of magnitude than conventional diffraction-limited beams. Overall, this, spectrometer-less, approach enabled 3D imaging of microparticles with high contrast and 1 µm axial resolution at 300 msec integration times per plane and orders of magnitude lower irradiation density than coherent Raman imaging schemes. We detail the apparatus and its performance, as well as its compatibility with fluorescence light-sheet and quantitative-phase imaging towards rapid and low phototoxicity multimodal imaging.

47 OTHER INSTRUMENTATION↗