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    NEURAL NETWORK-BASED CORRECTOR FOR PHOTON COUNTING DETECTORS
    35.
    发明申请
    NEURAL NETWORK-BASED CORRECTOR FOR PHOTON COUNTING DETECTORS 有权

    公开(公告)号：US20230035618A1

    公开(公告)日：2023-02-02

    申请号：US17896279

    申请日：2022-08-26

    Applicant: Rensselaer Polytechnic Institute

    Inventor： Ge Wang ,  Ruibin Feng ,  David Rundle

    IPC: G01T1/17 ,  G01T1/18 ,  G06T7/00

    Abstract: A neural network based corrector for photon counting detectors is described. A method for photon count correction includes receiving, by a trained artificial neural network (ANN), a detected photon count from a photon counting detector. The detected photon count corresponds to an attenuated energy spectrum. The attenuated energy spectrum is related to characteristics of an imaging object and is based, at least in part, on an incident energy spectrum. The method further includes correcting, by the trained ANN, the detected photon count to produce a corrected photon count. The method may include reconstructing, by image reconstruction circuitry, an image based, at least in part, on the corrected photon count.

    Neural network-based corrector for photon counting detectors
    36.
    发明授权
    Neural network-based corrector for photon counting detectors 有权

    公开(公告)号：US11448778B2

    公开(公告)日：2022-09-20

    申请号：US16770675

    申请日：2018-12-07

    Applicant: RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Ge Wang ,  Ruibin Feng ,  David Rundle

    IPC: G06K9/00 ,  G01T1/17 ,  G01T1/18 ,  G06T7/00 ,  A61B6/00

    Abstract: A neural network based corrector for photon counting detectors is described. A method for photon count correction includes receiving, by a trained artificial neural network (ANN), a detected photon count from a photon counting detector. The detected photon count corresponds to an attenuated energy spectrum. The attenuated energy spectrum is related to characteristics of an imaging object and is based, at least in part, on an incident energy spectrum. The method further includes correcting, by the trained ANN, the detected photon count to produce a corrected photon count. The method may include reconstructing, by image reconstruction circuitry, an image based, at least in part, on the corrected photon count.

    CT super-resolution GAN constrained by the identical, residual and cycle learning ensemble (GAN-circle)
    37.
    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
    38.
    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.

    TRAINING A CNN WITH PSEUDO GROUND TRUTH FOR CT ARTIFACT REDUCTION
    39.
    发明申请
    TRAINING A CNN WITH PSEUDO GROUND TRUTH FOR CT ARTIFACT REDUCTION 有权

    公开(公告)号：US20210374961A1

    公开(公告)日：2021-12-02

    申请号：US17404361

    申请日：2021-08-17

    Applicant: Rensselaer Polytechnic Institute

    Inventor： Ge Wang ,  Lars Arne Gjesteby ,  Hongming Shan

    IPC: G06T7/00 ,  G06T5/00

    Abstract: Training a CNN with pseudo ground truth for CT artifact reduction is described. An estimated ground truth apparatus is configured to generate an estimated ground truth image based, at least in part, on an initial CT image that includes an artifact. Feature addition circuitry is configured to add a respective feature to each of a number, N, copies of the estimated ground truth image to create the number, N, initial training images. A computed tomography (CT) simulation circuitry is configured to generate a plurality of simulated training CT images based, at least in part, on at least some of the N initial training images. An artifact reduction circuitry is configured to generate a plurality of input training CT images based, at least in part, on the simulated training CT images. A CNN training circuitry is configured to train the CNN based, at least in part, on the input training CT images and based, at least in part, on the initial training images.

    Monochromatic CT image reconstruction from current-integrating data via machine learning
    40.
    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.

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