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    Tomographic image reconstruction via machine learning
    43.
    发明授权
    Tomographic image reconstruction via machine learning 有权

    公开(公告)号：US10970887B2

    公开(公告)日：2021-04-06

    申请号：US16312704

    申请日：2017-06-26

    Applicant: RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Ge Wang ,  Wenxiang Cong ,  Qingsong Yang

    IPC: G06T11/00 ,  G06N20/00 ,  G06N3/08

    Abstract: Tomographic/tomosynthetic image reconstruction systems and methods in the framework of machine learning, such as deep learning, are provided. A machine learning algorithm can be used to obtain an improved tomographic image from raw data, processed data, or a preliminarily reconstructed intermediate image for biomedical imaging or any other imaging purpose. In certain cases, a single, conventional, non-deep-learning algorithm can be used on raw imaging data to obtain an initial image, and then a deep learning algorithm can be used on the initial image to obtain a final reconstructed image. All machine learning methods and systems for tomographic image reconstruction are covered, except for use of a single shallow network (three layers or less) for image reconstruction.

    SECOND ORDER NEURON FOR MACHINE LEARNING
    45.
    发明申请
    SECOND ORDER NEURON FOR MACHINE LEARNING 审中-公开

    公开(公告)号：US20190332928A1

    公开(公告)日：2019-10-31

    申请号：US16394111

    申请日：2019-04-25

    Applicant: Rensselaer Polytechnic Institute

    Inventor： Ge Wang ,  Wenxiang Cong ,  Fenglei Fan

    IPC: G06N3/063 ,  G11C11/54 ,  G06N3/08 ,  G06N20/00

    Abstract: A second order neuron for machine learning is described. The second order neuron includes a first dot product circuitry and a second dot product circuitry. The first dot product circuitry is configured to determine a first dot product of an intermediate vector and an input vector. The intermediate vector corresponds to a product of the input vector and a first weight vector or the input vector and a weight matrix. The second dot product circuitry is configured to determine a second dot product of the input vector and a second weight vector. The input vector, the intermediate vector, the first weight vector and the second weight vector each contain a number, n, elements.

    FILTRATION METHODS FOR DUAL-ENERGY X-RAY CT
    46.
    发明申请
    FILTRATION METHODS FOR DUAL-ENERGY X-RAY CT 审中-公开

    公开(公告)号：US20190269375A1

    公开(公告)日：2019-09-05

    申请号：US16294438

    申请日：2019-03-06

    Applicant: Rensselaer Polytechnic Institute

    Inventor： Daniel David Harrison ,  Ge Wang

    IPC: A61B6/00 ,  A61B6/03

    Abstract: Systems and method for performing X-ray computed tomography (CT) that can improve spectral separation and decrease motion artifacts without increasing radiation dose are provided. The systems and method can be used with either a kVp-switching source or a single-kVp source. When used with a kVp-switching source, an absorption grating and a filter grating can be disposed between the X-ray source and the sample to be imaged. Relative motion of the filter and absorption gratings can by synchronized to the kVp switching frequency of the X-ray source. When used with a single-kVp source, a combination of absorption and filter gratings can be used and can be driven in an oscillation movement that is optimized for a single-kVp X-ray source. With a single-kVp source, the absorption grating can also be omitted and the filter grating can remain stationary.

    CT big data from simulation, emulation and transfer learning
    49.
    发明授权
    CT big data from simulation, emulation and transfer learning 有权

    公开(公告)号：US12175734B2

    公开(公告)日：2024-12-24

    申请号：US16969072

    申请日：2019-02-11

    Applicant: RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Ge Wang ,  Daniel David Harrison ,  Xun Jia ,  Klaus Mueller

    IPC: G01N33/48 ,  A61B5/00 ,  G06F18/214 ,  G06N3/04 ,  G06N3/08 ,  G06N3/10 ,  G06N20/00 ,  G06T11/00 ,  G06V10/82

    Abstract: In some embodiments, a method of machine learning includes identifying, by an auto encoder network, a simulator feature based, at least in part, on a received first simulator data set and an emulator feature based, at least in part, on a received first emulator data set. The method further includes determining, by a synthesis control circuitry, a synthesized feature based, at least in part, on the simulator feature and based, at least in part, on the emulator feature; and generating, by the auto encoder network, an intermediate data set based, at least in part, on a second simulator data set and including the synthesized feature. Some embodiments of the method further include determining, by a generative artificial neural network, a synthesized data set based, at least in part, on the intermediate data set and based, at least in part, on an objective function.

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