Browsing by Author "Ozeki, Yasuyuki"

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GHz Optical Time-Stretch Microscopy by Compressive Sensing
(2017-04-01)
Lei, Cheng
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Wu, Yi
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Sankaranarayanan, Aswin C.
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Chang, Shih-Min
;
Guo, Baoshan
Optical time-stretch microscopy has recently attracted intensive attention for its capability of acquiring images at an ultrahigh frame rate. Unfortunately, its achievable frame rate is limited by the requirement of having no overlap between consecutive frames, which leads to a tradeoff between the frame rate (pulse repetition rate) and the amount of the temporal dispersion used for optical image serialization. In this paper, we demonstrate compressive sensing on the platform of optical time-stretch microscopy to overcome the tradeoff between frame rate and temporal dispersion (time stretch) and achieve 50 times higher frame rate than conventional optical time-stretch microscopy. Specifically, we computationally perform compressed optical time-stretch microscopy with an experimental dataset acquired by conventional optical time-stretch microscopy and demonstrate its effects in terms of spatial resolution and cell classification accuracy. Our results indicate that the spatial resolution and cell classification accuracy reach 780 nm and 95% at a line scan rate of 675 MHz and 6.75 GHz, respectively, which correspond to five times and 50 times higher frame rates than what conventional optical time-stretch microscopy can achieve with the same dispersion amount and digitizer sampling rate.
Intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging
(2020-01-06)
Wu, Yunzhao
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Zhou, Yuqi
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Huang, Chun-Jung
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Kobayashi, Hirofumi
;
Yan, Sheng
Optofluidic time-stretch quantitative phase imaging (OTS-QPI) is a powerful tool as it enables high-throughput (>10,0(X)cell/s) QPI of single live cells. OTS-QPI is based on decoding temporally stretched spectral interferograms that carry the spatial profiles of cells flowing on a microfluidic chip. However, the utility of OTS-QPI is troubled by difficulties in phase retrieval from the high-frequency region of the temporal interferograms, such as phase-unwrapping errors, high instrumentation cost, and large data volume. To overcome these difficulties, we propose and experimentally demonstrate frequency-shifted OTS-QPI by bringing the phase information to the baseband region. Furthermore, to show its boosted utility, we use it to demonstrate image-based classification of leukemia cells with high accuracy over 96% and evaluation of drug-treated leukemia cells via deep learning. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Virtual-freezing fluorescence imaging flow cytometry
(2020-03-06)
Mikami, Hideharu
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Kawaguchi, Makoto
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Huang, Chun-Jung
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Matsumura, Hiroki
;
Sugimura, Takeaki
By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s(-1) without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology. High throughput imaging flow cytometry suffers from trade-offs between throughput, sensitivity and spatial resolution. Here the authors introduce a method to virtually freeze cells in the image acquisition window to enable 1000 times longer signal integration time and improve signal-to-noise ratio.