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    CT super-resolution GAN constrained by the identical, residual and cycle learning ensemble (GAN-circle)
    41.
    发明授权
    CT super-resolution GAN constrained by the identical, residual and cycle learning ensemble (GAN-circle) 有权

    公开(公告)号：US11232541B2

    公开(公告)日：2022-01-25

    申请号：US16594567

    申请日：2019-10-07

    Applicant: RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Ge Wang ,  Chenyu You ,  Wenxiang Cong ,  Hongming Shan ,  Guang Li

    IPC: G06T3/40 ,  G06N3/04

    Abstract: A system for generating a high resolution (HR) computed tomography (CT) image from a low resolution (LR) CT image is described. The system includes a first generative adversarial network (GAN) and a second GAN. The first GAN includes a first generative neural network (G) configured to receive a training LR image dataset and to generate a corresponding estimated HR image dataset, and a first discriminative neural network (DY) configured to compare a training HR image dataset and the estimated HR image dataset. The second GAN includes a second generative neural network (F) configured to receive the training HR image dataset and to generate a corresponding estimated LR image dataset, and a second discriminative neural network (DX) configured to compare the training LR image dataset and the estimated LR image dataset. The system further includes an optimization module configured to determine an optimization function based, at least in part, on at least one of the estimated HR image dataset and/or the estimated LR image dataset. The optimization function contains at least one loss function. The optimization module is further configured to adjust a plurality of neural network parameters associated with at least one of the first GAN and/or the second GAN, to optimize the optimization function.

    SIMULTANEOUS EMISSION-TRANSMISSION TOMOGRAPHY IN AN MRI HARDWARE FRAMEWORK
    42.
    发明申请
    SIMULTANEOUS EMISSION-TRANSMISSION TOMOGRAPHY IN AN MRI HARDWARE FRAMEWORK 有权

    公开(公告)号：US20210389399A1

    公开(公告)日：2021-12-16

    申请号：US17279400

    申请日：2019-03-13

    Applicant: Rensselaer Polytechnic Institute

    Inventor： Ge Wang ,  Lars Arne Gjesteby ,  Wenxiang Cong

    IPC: G01R33/48 ,  G01R33/38 ,  A61B6/03 ,  A61B6/00

    Abstract: A simultaneous emission-transmission tomography in an MRI hardware framework is described. A method of multimodality imaging includes reconstructing, by a simultaneous emission transmission (SET) circuitry, a concentration image based, at least in part, on a plurality of selected γ-rays; and reconstructing, by the SET circuitry, an attenuation image based, at least in part, on the plurality of selected γ-rays. The plurality of selected γ-rays is emitted by a polarized radio tracer included in a test object. The selected γ-rays are selected based, at least in part, on a radio frequency (RF) pulse and based, at least in part, on a gradient magnetic field.

    Monochromatic CT image reconstruction from current-integrating data via machine learning
    43.
    发明授权
    Monochromatic CT image reconstruction from current-integrating data via machine learning 有权

    公开(公告)号：US11127175B2

    公开(公告)日：2021-09-21

    申请号：US16647220

    申请日：2018-09-26

    Applicant: RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Ge Wang ,  Wenxiang Cong

    IPC: G06T11/00 ,  G06N3/04

    Abstract: A machine-learning-based monochromatic CT image reconstruction method is described for quantitative CT imaging. The neural network is configured to learn a nonlinear mapping function from a training data set to map a CT image, which is reconstructed from a single spectral current-integrating projection data set, to monochromatic projections at a pre-specified energy level, realizing monochromatic CT imaging and overcoming beam hardening. An apparatus, method and/or system are configured to determine, by a trained artificial neural network (ANN), a monochromatic projection data set based, at least in part, on a measured CT image. The measured CT image may be reconstructed based, at least in part, on measured projection data. The measured projection data may be polychromatic. The apparatus, method and/or system may be further configured to reconstruct a monochromatic CT image based, at least in part, on the monochromatic projection data set.

    Tomographic image reconstruction via machine learning
    46.
    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
    48.
    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.

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