Real-time halo correction in phase contrast imaging

Kandel, Mikhail E.; Fanous, Michael J.; Best-Popescu, Catherine; Popescu, Gabriel

doi:10.1364/boe.9.000623

Cited by 42 publications

(23 citation statements)

References 65 publications

(74 reference statements)

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“…Fig. 3 shows both grayscale and colored (for easier RI visualization after halo-removal [45]) lateral cross-sections at different depths through the reconstruction volume ( = 0.0, 1.05, and 2.10 um, where = 0.0 um designates the attachment interface of the fibroblast cell to the coverslip). As adherent, migratory cells, fibroblast cells are known to form dynamic membrane protrusions [46].…”

Section: B Fibroblast Cellsmentioning

confidence: 99%

High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images

Chowdhury

Chen

Eckert

et al. 2019

Optica

160

103

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Optical diffraction tomography (ODT) reconstructs a sample's volumetric refractive index (RI) to create high-contrast, quantitative 3D visualizations of biological samples. However, standard implementations of ODT use interferometric systems, and so are sensitive to phase instabilities, complex mechanical design, and coherent noise. Furthermore, their reconstruction framework is typically limited to weaklyscattering samples, and thus excludes a whole class of multiple-scattering samples. Here, we implement a new 3D RI microscopy technique that utilizes a computational multi-slice beam propagation method to invert the optical scattering process and reconstruct high-resolution (NA>1.0) 3D RI distributions of multiple-scattering samples. The method acquires intensity-only measurements from different illumination angles, and then solves a non-linear optimization problem to recover the sample's 3D RI distribution. We experimentally demonstrate reconstruction of samples with varying amounts of multiple scattering: a 3T3 fibroblast cell, a cluster of C. elegans embryos, and a whole C. elegans worm, with lateral and axial resolutions of ≤250 nm and ≤900 nm, respectively. for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved. Fluorescent imaging has enabled stunning visualizations of biological processes at a variety of size scales and resolutions, for studies of gene expression, protein interactions, intracellular dynamics, etc [1][2][3][4]. However, the fluorescent techniques require exogenous biological labels, and so do not directly give endogenous information about a sample's biological structure.Optical diffraction tomography (ODT) also targets 3D biological imaging. In contrast to fluorescent methods, ODT avoids the use of exogenous biological labels, and instead utilizes the intrinsic optical variation within a sample to reconstruct its 3D refractive-index (RI) distribution [5][6][7][8][9][10][11]. Hence, ODT avoids some of fluorescent imaging's main drawbacks, such as photobleaching, slow acquisition speed, low signal-to-noise (SNR) ratio, and complex samplepreparation protocol. Furthermore, RI imaging enables examination of the structural, mechanical, and biochemical properties of a sample, which are important for studies in morphology, mass, shear stiffness, and spectroscopy [9,[12][13][14][15].Standard implementations of ODT use either a rotating sample or a scanning laser beam to capture the angle-specific scattering arising from the sample [5,7,[16][17][18]. Under the assumption of weak scattering (i.e., 1st Born or Rytov approximations), 2D electric-field measurements directly yield information about the sample's 3D scattering potential [19][20][21]. Standard ODT reconstruction algorithms utilize the Fourier diffraction theorem to project the information contained in each electric-field measurement onto spherical shells (i.e., Ewald surfaces) in the 3D Fourier space of the sample's scattering potential [22,23]. ...

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Section: B Fibroblast Cellsmentioning

confidence: 99%

High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images

Chowdhury

Chen

Eckert

et al. 2019

Optica

160

103

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“…Here, we use our recently devised color spatial light interference microscopy (cSLIM) 23 as a quantitative phase imaging (QPI) technique [24][25][26][27][28][29][30][31][32][33][34] to quantify myelin in brain tissue. QPI is a technique that can evaluate nanometer scale pathlength changes in biological specimens.…”

mentioning

confidence: 99%

Quantifying myelin content in brain tissue using color spatial light interference microscopy (cSLIM)

Popescu

Caputo

Lee

et al. 2020

Preprint

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Deficient myelination of the brain is associated with neurodevelopmental delays, particularly in high-risk infants, such as those born small in relation to their gestational age (SGA). New methods are needed to further study this condition. Here, we employ Color Spatial Light Interference Microscopy (cSLIM), which uses a brightfield objective and RGB camera to generate pathlength-maps with nanoscale sensitivity in conjunction with a regular brightfield image. Using tissue sections stained with Luxol Fast Blue, the myelin structures were segmented from a brightfield image. Using a binary mask, those portions were quantitatively analyzed in the corresponding phase maps. We first used the CLARITY method to remove tissue lipids and validate the sensitivity of cSLIM to lipid content. We then applied cSLIM to brain histology slices. These specimens are from a previous MRI study, which demonstrated that appropriate for gestational age (AGA) piglets have increased internal capsule myelination (ICM) compared to small for gestational age (SGA) piglets and that a hydrolyzed fat diet improved ICM in both. The identity of samples was blinded until after statistical analyses.

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“…The scatter channel was chosen for feature extraction, as it is less susceptible to optical phase artifacts. These appear in a subset of bright‐field images in the form of bright or dark halos around the cell and are due to small deviations of cell position within the flow stream. A linear mapping of the 85 image features to a 3‐dimensional PCA plot is shown in Figure B.…”

Section: Resultsmentioning

confidence: 99%

Diffusion Mapping of Eosinophil‐Activation State

et al. 2019

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Eosinophils are granular leukocytes that play a role in mediating inflammatory responses linked to infection and allergic disease. Their activation during an immune response triggers spatial reorganization and eventual cargo release from intracellular granules. Understanding this process is important in diagnosing eosinophilic disorders and in assessing treatment efficacy; however, current protocols are limited to simply quantifying the number of eosinophils within a blood sample. Given that high optical absorption and scattering by the granular structure of these cells lead to marked image features, the physical changes that occur during activation should be trackable using image analysis. Here, we present a study in which imaging flow cytometry is used to quantify eosinophil activation state, based on the extraction of 85 distinct spatial features from dark‐field images formed by light scattered orthogonally to the illuminating beam. We apply diffusion mapping, a time inference method that orders cells on a trajectory based on similar image features. Analysis of exogenous cell activation using eotaxin and endogenous activation in donor samples with elevated eosinophil counts shows that cell position along the diffusion‐path line correlates with activation level (99% confidence level). Thus, the diffusion mapping provides an activation metric for each cell. Assessment of activated and control populations using both this spatial image‐based, activation score and the integrated side‐scatter intensity shows an improved Fisher discriminant ratio rd = 0.7 for the multivariate technique compared with an rd = 0.47 for the traditional whole‐cell scatter metric. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

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Real-time halo correction in phase contrast imaging

Cited by 42 publications

References 65 publications

High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images

High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images

Quantifying myelin content in brain tissue using color spatial light interference microscopy (cSLIM)

Diffusion Mapping of Eosinophil‐Activation State

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