Browsing by Author "Goda, Keisuke"

Now showing 1 - 7 of 7

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.
;
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 classification of platelet aggregates by agonist type
(2020-05-12)
Zhou, Yuqi
;
Yasumoto, Atsushi
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Lei, Cheng
;
Huang, Chun-Jung
;
Kobayashi, Hirofumi
Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.
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
Intelligent Image De-Blurring for Imaging Flow Cytometry
(2019-05-01)
Zhang, Fangzheng
;
Lei, Cheng
;
Huang, Chun-Jung
;
Kobayashi, Hirofumi
;
Sun, Chia-Wei
By virtue of the combined merits of optical microscopy and flow cytometry, imaging flow cytometry is a powerful tool for rapid, high-content analysis of single cells in large heterogeneous populations. However, its efficiency (defined by the ratio of the number of clearly imaged cells to the total cell population) is not high (typically 50-80%), due to out-of-focus image blurring caused by imperfect fluidic focusing of cells, a common drawback that not only reduces the number of cell images useable for high-content analysis but also increases the probability of false events and missed rare cells. To address this challenge and expand the efficacy of imaging flow cytometry, here, we propose and demonstrate intelligent deblurring of out-of-focus cell images in imaging flow cytometry. Specifically, by using our machine learning algorithms, we show an 11% increase in variance and a 95% increase in first-order gradient summation of cell images taken with an optofluidic time-stretch microscope. Without strict hardware requirements, our intelligent de-blurring method provides a promising solution to the out-of-focus blurring problem of imaging flow cytometers and holds promise for significantly improving their performance. (C) 2019 International Society for Advancement of Cytometry
Intelligent whole-blood imaging flow cytometry for simple, rapid, and cost-effective drug-susceptibility testing of leukemia
(2019-08-21)
Kobayashi, Hirofumi
;
Lei, Cheng
;
Wu, Yi
;
Huang, Chun-Jung
;
Yasumoto, Atsushi
Drug susceptibility (also called chemosensitivity) is an important criterion for developing a therapeutic strategy for various cancer types such as breast cancer and leukemia. Recently, functional assays such as high-content screening together with genomic analysis have been shown to be effective for predicting drug susceptibility, but their clinical applicability is poor since they are time-consuming (several days long), labor-intensive, and costly. Here we present a highly simple, rapid, and cost-effective liquid biopsy for ex vivo drug-susceptibility testing of leukemia. The method is based on an extreme-throughput (>1 million cells per second), label-free, whole-blood imaging flow cytometer with a deep convolutional autoencoder, enabling image-based identification of the drug susceptibility of every single white blood cell in whole blood within 24 hours by simply flowing a drug-treated whole blood sample as little as 500 mu L into the imaging flow cytometer without labeling. Our results show that the method accurately evaluates the drug susceptibility of white blood cells from untreated patients with acute lymphoblastic leukemia. Our method holds promise for affordable precision medicine.
Interplay of hot electrons from localized and propagating plasmons
(2017-10-03)
Chung V. Hoang
;
Hayashi, Koki
;
Ito, Yasuo
;
Gorai, Naoki
;
Allison, Giles
Plasmon-induced hot-electron generation has recently received considerable interest and has been studied to develop novel applications in optoelectronics, photovoltaics and green chemistry. Such hot electrons are typically generated from either localized plasmons in metal nanoparticles or propagating plasmons in patterned metal nanostructures. Here we simultaneously generate these heterogeneous plasmon-induced hot electrons and exploit their cooperative interplay in a single metal-semiconductor device to demonstrate, as an example, wavelength-controlled polarity-switchable photoconductivity. Specifically, the dual-plasmon device produces a net photocurrent whose polarity is determined by the balance in population and directionality between the hot electrons from localized and propagating plasmons. The current responsivity and polarity-switching wavelength of the device can be varied over the entire visible spectrum by tailoring the hot-electron interplay in various ways. This phenomenon may provide flexibility to manipulate the electrical output from light-matter interaction and offer opportunities for biosensors, long-distance communications, and photoconversion applications.
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.