Flipping interferometry with doubled imaging area

Rotman-Nativ, Noa; Turko, Nir A.; Shaked, Natan T.

doi:10.1364/ol.43.005543

Cited by 17 publications

(13 citation statements)

References 8 publications

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“…It is an external interferometric module that is compact, nearly common path, and does not use any lenses or polarizer inside, hence easing its alignment. As in our previous flipping interferometry module that is used for field of view multiplexing [13], here too, the empty half of the optical spot illuminating the sample is used as the reference beam. It is optically flipped over the sample field of view to create an off-axis hologram of the sample on the monochromatic camera.…”

Section: The F-2wl Setupmentioning

confidence: 99%

“…Since the two interference fringe patterns recorded on the camera are orthogonal with respect to each other, the resulting CC terms are located on orthogonal axes in the spatial spectrum. In contrast to field-of-view multiplexing [13], no cross-terms occur between the wavelength channels, since the two different lasers do not create mutual interference resulted in unwanted terms between the useful CC terms.…”

Section: Phase Reconstruction Algorithmmentioning

confidence: 99%

“…Still, in contrast to most shearing interferometers, the off-axis angle, determining the fringe spatial frequency, can be fully controlled, without the requirement for sample sparsity to avoid ghost image overlaps. In [13], we proposed flipping interferometry with doubled imaging area, an external portable module that combines simultaneous acquisition of two fields of view of the sample by spatial off-axis holographic multiplexing, without using a pinhole alignment. It projects two holographic fields of view on the camera at once, where each field of view is encoded into interference fringes of different orientation.…”

Section: Introductionmentioning

confidence: 99%

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Flipping Interferometric Module for Simultaneous Dual-Wavelength Unwrapping of Quantitative Phase Maps of Biological Cells

et al. 2021

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We present an imaging platform for stain-free quantitative imaging of biological cells using a simultaneous dual-wavelength holographic module. We use this module to experimentally solve the problem of 2π phase ambiguities that occurs in spatial locations where the optical thickness of the sample is larger than the wavelength. Thus, the process does not require using digital phase unwrapping that is computational heavy and impairs real-time processing. The proposed method is not limited to sequential acquisition of two quantitative phase maps in different times, but rather allows optical multiplexing of two off-axis holograms on the camera at once, enabling acquisition of fast dynamic processes. The module is simple and portable, making it attractive for clinical use. We demonstrate using the module for quantitative phase imaging of cancer and sperm cells.

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Section: The F-2wl Setupmentioning

confidence: 99%

Section: Phase Reconstruction Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Flipping Interferometric Module for Simultaneous Dual-Wavelength Unwrapping of Quantitative Phase Maps of Biological Cells

et al. 2021

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“…In optical multiplexing [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], multiple sample and reference beam pairs with different 𝜃 𝑥 and 𝜃 𝑦 combinations are projected onto the digital camera simultaneously, each of which creating an off-axis hologram with a different interference fringe direction that positions one wavefront in the SFD without overlapping other terms. The simultaneous projection of all beams on the camera may create unwanted interference between nonmatching pairs.…”

Section: Optical Multiplexingmentioning

confidence: 99%

Is multiplexed off-axis holography for quantitative phase imaging more spatial bandwidth-efficient than on-axis holography? [Invited]

Dardikman

Shaked

2018

J. Opt. Soc. Am. A

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Digital holographic microcopy is a thriving imaging modality that attracts considerable research interest due to its ability to not only create excellent label-free contrast, but also supply valuable physical information regarding the density and dimensions of the sample with nanometer-scale axial sensitivity. Three basic holographic recording geometries currently exist, including on-axis, off-axis and slightly off-axis holography, each of them enabling a variety of architectures in terms of bandwidth use and compression capacity. Specifically, off-axis holography and slightly off-axis holography allow spatial hologram multiplexing, enabling compressing more information into the same digital hologram. In this paper, we define an efficiency score used to analyze the various possible architectures, and compare the signal-to-noise ratio and mean squared error obtained using each of them, determining the optimal holographic method.

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“…Quantitative phase imaging (QPI) clinical modules can record, without using cell staining, the fluctuation maps of live biological cells based on their quantitative phase map, which is proportional to the optical path delay (OPD) map of the cell [26][27][28][29][30]. Reflection phase microscope with coherence gating have shown success to measure cell membrane temporal fluctuation [31].…”

Section: Introductionmentioning

confidence: 99%

Live Cancer Cell Classification Based on Quantitative Phase Spatial Fluctuations and Deep Learning With a Small Training Set

Rotman-Nativ

Shaked

2021

Front. Phys.

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We present an analysis method that can automatically classify live cancer cells from cell lines based on a small data set of quantitative phase imaging data without cell staining. The method includes spatial image analysis to extract the cell phase spatial fluctuation map, derived from the quantitative phase map of the cell measured without cell labeling, thus without prior knowledge on the biomarker. The spatial fluctuations are indicative of the cell stiffness, where cancer cells change their stiffness as cancer progresses. In this paper, the quantitative phase spatial fluctuations are used as the basis for a deep-learning classifier for evaluating the cell metastatic potential. The spatial fluctuation analysis performed on the quantitative phase profiles before inputting them to the neural network was proven to increase the classification results in comparison to inputting the quantitative phase profiles directly, as done so far. We classified between primary and metastatic cancer cells and obtained 92.5% accuracy, in spite of using a small training set, demonstrating the method potential for objective automatic clinical diagnosis of cancer cells in vitro.

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Flipping interferometry with doubled imaging area

Cited by 17 publications

References 8 publications

Flipping Interferometric Module for Simultaneous Dual-Wavelength Unwrapping of Quantitative Phase Maps of Biological Cells

Flipping Interferometric Module for Simultaneous Dual-Wavelength Unwrapping of Quantitative Phase Maps of Biological Cells

Is multiplexed off-axis holography for quantitative phase imaging more spatial bandwidth-efficient than on-axis holography? [Invited]

Live Cancer Cell Classification Based on Quantitative Phase Spatial Fluctuations and Deep Learning With a Small Training Set

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