Single-Shot High-Throughput Phase Imaging with Multibeam Array Interferometric Microscopy

ACS Photonics ◽  
2021 ◽  
Author(s):  
Jie Dong ◽  
Ali K. Yetisen ◽  
Chen Zhao ◽  
Xingchen Dong ◽  
Franziska Brändle ◽  
...  
2021 ◽  
Author(s):  
Péter Kocsis ◽  
Igor Shevkunov ◽  
Vladimir Katkovnik ◽  
Heikki Rekola ◽  
Karen Egiazarian

2021 ◽  
Vol 9 ◽  
Author(s):  
José Ángel Picazo-Bueno ◽  
Javier García ◽  
Vicente Micó

Digital holographic microscopy (DHM) is a well-known microscopy technique using an interferometric architecture for quantitative phase imaging (QPI) and it has been already implemented utilizing a large number of interferometers. Among them, single-element interferometers are of particular interest due to its simplicity, stability, and low cost. Here, we present an extremely simple common-path interferometric layout based on the use of a single one-dimensional diffraction grating for both illuminating the sample in reflection and generating the digital holograms. The technique, named single-element reflective digital holographic microscopy (SER-DHM), enables QPI and topography analysis of reflective/opaque objects using a single-shot operation principle. SER-DHM is experimentally validated involving different reflective samples.


2014 ◽  
Vol 319 ◽  
pp. 85-89 ◽  
Author(s):  
P.T. Samsheerali ◽  
Kedar Khare ◽  
Joby Joseph

2016 ◽  
Vol 24 (4) ◽  
pp. 3765 ◽  
Author(s):  
Ryoichi Horisaki ◽  
Riki Egami ◽  
Jun Tanida
Keyword(s):  

2021 ◽  
Vol 9 ◽  
Author(s):  
Cindy X. Chen ◽  
Han Sang Park ◽  
Hillel Price ◽  
Adam Wax

Holographic cytometry is an ultra-high throughput quantitative phase imaging modality that is capable of extracting subcellular information from millions of cells flowing through parallel microfluidic channels. In this study, we present our findings on the application of holographic cytometry to distinguishing carcinogen-exposed cells from normal cells and cancer cells. This has potential application for environmental monitoring and cancer detection by analysis of cytology samples acquired via brushing or fine needle aspiration. By leveraging the vast amount of cell imaging data, we are able to build single-cell-analysis-based biophysical phenotype profiles on the examined cell lines. Multiple physical characteristics of these cells show observable distinct traits between the three cell types. Logistic regression analysis provides insight on which traits are more useful for classification. Additionally, we demonstrate that deep learning is a powerful tool that can potentially identify phenotypic differences from reconstructed single-cell images. The high classification accuracy levels show the platform’s potential in being developed into a diagnostic tool for abnormal cell screening.


2021 ◽  
Vol 29 (4) ◽  
pp. 4783
Author(s):  
Naru Yoneda ◽  
Aoi Onishi ◽  
Yusuke Saita ◽  
Koshi Komuro ◽  
Takanori Nomura

Sign in / Sign up

Export Citation Format

Share Document