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Cardiomyocyte Imaging Using Real-Time Spatial Light Interference Microscopy (SLIM)

Overview of attention for article published in PLOS ONE, February 2013
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Title
Cardiomyocyte Imaging Using Real-Time Spatial Light Interference Microscopy (SLIM)
Published in
PLOS ONE, February 2013
DOI 10.1371/journal.pone.0056930
Pubmed ID
Authors

Basanta Bhaduri, David Wickland, Ru Wang, Vincent Chan, Rashid Bashir, Gabriel Popescu

Abstract

Spatial light interference microscopy (SLIM) is a highly sensitive quantitative phase imaging method, which is capable of unprecedented structure studies in biology and beyond. In addition to the π/2 shift introduced in phase contrast between the scattered and unscattered light from the sample, 4 phase shifts are generated in SLIM, by increments of π/2 using a reflective liquid crystal phase modulator (LCPM). As 4 phase shifted images are required to produce a quantitative phase image, the switching speed of the LCPM and the acquisition rate of the camera limit the acquisition rate and, thus, SLIM's applicability to highly dynamic samples. In this paper we present a fast SLIM setup which can image at a maximum rate of 50 frames per second and provide in real-time quantitative phase images at 50/4 = 12.5 frames per second. We use a fast LCPM for phase shifting and a fast scientific-grade complementary metal oxide semiconductor (sCMOS) camera (Andor) for imaging. We present the dispersion relation, i.e. decay rate vs. spatial mode, associated with dynamic beating cardiomyocyte cells from the quantitative phase images obtained with the real-time SLIM system.

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Geographical breakdown

Country Count As %
United States 2 4%
Poland 1 2%
France 1 2%
Korea, Republic of 1 2%
Unknown 48 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 36%
Researcher 11 21%
Student > Bachelor 4 8%
Other 3 6%
Student > Master 3 6%
Other 8 15%
Unknown 5 9%
Readers by discipline Count As %
Physics and Astronomy 16 30%
Engineering 11 21%
Agricultural and Biological Sciences 9 17%
Biochemistry, Genetics and Molecular Biology 4 8%
Business, Management and Accounting 2 4%
Other 6 11%
Unknown 5 9%