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    METHODS AND SYSTEMS FOR CYTOMETRY
    9.
    发明申请
    METHODS AND SYSTEMS FOR CYTOMETRY 有权

    公开(公告)号：US20210190669A1

    公开(公告)日：2021-06-24

    申请号：US17115657

    申请日：2020-12-08

    Applicant: ThinkCyte, Inc. ,  University of Tokyo ,  Osaka University

    Inventor： Sadao Ota ,  Ryoichi Horisaki ,  Yoko Kawamura ,  Masashi Ugawa ,  Issei Sato

    IPC: G01N15/14

    Abstract: The present disclosure provides methods and systems for ghost cytometry (GC), which may be used to produce an image of an object without using a spatially resolving detector. This may be used to perform image-free ultrafast fluorescence “imaging” cytometry, based on, for example, a single pixel detector. Spatial information obtained from the motion of cells relative to a patterned optical structure may be compressively converted into signals that arrive sequentially at a single pixel detector. Combinatorial use of the temporal waveform with the intensity distribution of the random or pseudo-random pattern may permit computational reconstruction of cell morphology. Machine learning methods may be applied directly to the compressed waveforms without image reconstruction to enable efficient image-free morphology-based cytometry. Image-free GC may achieve accurate and high throughput cell classification as well as selective sorting based on cell morphology without a specific biomarker, which have been challenging using conventional flow cytometers.

    METHODS AND SYSTEMS FOR CYTOMETRY
    10.
    发明公开
    METHODS AND SYSTEMS FOR CYTOMETRY 审中-公开

    公开(公告)号：US20240133792A1

    公开(公告)日：2024-04-25

    申请号：US18238368

    申请日：2023-08-25

    Applicant: ThinkCyte K.K. ,  University of Tokyo ,  Osaka University

    Inventor： Sadao Ota ,  Ryoichi Horisaki ,  Yoko Kawamura ,  Masashi Ugawa ,  Issei Sato

    IPC: G01N15/14

    CPC classification number: G01N15/1434 ,  G01N15/1459 ,  G01N2015/1006

    Abstract: The present disclosure provides methods and systems for ghost cytometry (GC), which may be used to produce an image of an object without using a spatially resolving detector. This may be used to perform image-free ultrafast fluorescence “imaging” cytometry, based on, for example, a single pixel detector. Spatial information obtained from the motion of cells relative to a patterned optical structure may be compressively converted into signals that arrive sequentially at a single pixel detector. Combinatorial use of the temporal waveform with the intensity distribution of the random or pseudo-random pattern may permit computational reconstruction of cell morphology. Machine learning methods may be applied directly to the compressed waveforms without image reconstruction to enable efficient image-free morphology-based cytometry. Image-free GC may achieve accurate and high throughput cell classification as well as selective sorting based on cell morphology without a specific biomarker, which have been challenging using conventional flow cytometers.

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