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    DEEP-LEARNING-BASED SCATTER ESTIMATION AND CORRECTION FOR X-RAY PROJECTION DATA AND COMPUTER TOMOGRAPHY (CT)
    2.
    发明申请
    DEEP-LEARNING-BASED SCATTER ESTIMATION AND CORRECTION FOR X-RAY PROJECTION DATA AND COMPUTER TOMOGRAPHY (CT) 审中-公开

    公开(公告)号：US20200234471A1

    公开(公告)日：2020-07-23

    申请号：US16252392

    申请日：2019-01-18

    Applicant: Canon Medical Systems Corporation

    Inventor： Yujie Lu ,  Zhou Yu ,  Jian Zhou ,  Tzu-Cheng Lee ,  Richard Thompson

    IPC: G06T11/00 ,  G06T7/11

    Abstract: A method and apparatus are provided for using a neural network to estimate scatter in X-ray projection images and then correct for the X-ray scatter. For example, the neural network is a three-dimensional convolutional neural network 3D-CNN to which are applied projection images, at a given view, for respective energy bins and/or material components. The projection images can be obtained by material decomposing spectral projection data, or by segmenting a reconstructed CT image into material-component images, which are then forward projected to generate energy-resolved material-component projections. The result generated by the 3D-CNN is an estimated scatter flux. To train the 3D-CNN, the target scatter flux in the training data can be simulated using a radiative transfer equation method.

    Apparatus and method combining deep learning (DL) with an X-ray computed tomography (CT) scanner having a multi-resolution detector
    4.
    发明授权
    Apparatus and method combining deep learning (DL) with an X-ray computed tomography (CT) scanner having a multi-resolution detector 有权

    公开(公告)号：US11547378B2

    公开(公告)日：2023-01-10

    申请号：US16509369

    申请日：2019-07-11

    Applicant: Canon Medical Systems Corporation

    Inventor： Ilmar Hein ,  Zhou Yu ,  Tzu-Cheng Lee

    IPC: A61B6/00 ,  A61B6/03 ,  G06T11/00 ,  G06N20/00 ,  G06N3/08

    Abstract: A method and apparatus is provided that uses a deep learning (DL) network together with a multi-resolution detector to perform X-ray projection imaging to provide improved resolution similar to a single-resolution detector but at lower cost and less demand on the communication bandwidth between the rotating and stationary parts of an X-ray gantry. The DL network is trained using a training dataset that includes input data and target data. The input data includes projection data acquired using a multi-resolution detector, and the target data includes projection data acquired using a single-resolution, high-resolution detector. Thus, the DL network is trained to improve the resolution of projection data acquired using a multi-resolution detector. Further, the DL network is can be trained to additional correct other aspects of the projection data (e.g., noise and artifacts).

    Apparatus and method for sinogram restoration in computed tomography (CT) using adaptive filtering with deep learning (DL)
    8.
    发明授权
    Apparatus and method for sinogram restoration in computed tomography (CT) using adaptive filtering with deep learning (DL) 有权

    公开(公告)号：US11176428B2

    公开(公告)日：2021-11-16

    申请号：US16372206

    申请日：2019-04-01

    Applicant: CANON MEDICAL SYSTEMS CORPORATION

    Inventor： Tzu-Cheng Lee ,  Jian Zhou ,  Zhou Yu

    IPC: G06K9/66 ,  G06K9/40 ,  G06T7/00 ,  G01T1/161 ,  A61B6/00

    Abstract: A method and apparatus is provided to reduce the noise in medical imaging by training a deep learning (DL) network to select the optimal parameters for a convolution kernel of an adaptive filter that is applied in the data domain. For example, in X-ray computed tomography (CT) the adaptive filter applies smoothing to a sinogram, and the optimal amount of the smoothing and orientation of the kernel (e.g., a bivariate Gaussian) can be determined on a pixel-by-pixel basis by applying a noisy sinogram to the DL network, which outputs the parameters of the filter (e.g., the orientation and variances of the Gaussian kernel). The DL network is trained using a training data set including target data (e.g., the gold standard) and input data. The input data can be sinograms generated by a low-dose CT scan, and the target data generated by a high-dose CT scan.

    Deep-learning-based scatter estimation and correction for X-ray projection data and computer tomography (CT)
    9.
    发明授权
    Deep-learning-based scatter estimation and correction for X-ray projection data and computer tomography (CT) 有权

    公开(公告)号：US10937206B2

    公开(公告)日：2021-03-02

    申请号：US16252392

    申请日：2019-01-18

    Applicant: Canon Medical Systems Corporation

    Inventor： Yujie Lu ,  Zhou Yu ,  Jian Zhou ,  Tzu-Cheng Lee ,  Richard Thompson

    IPC: G06T11/00 ,  G06T7/11

    Abstract: A method and apparatus are provided for using a neural network to estimate scatter in X-ray projection images and then correct for the X-ray scatter. For example, the neural network is a three-dimensional convolutional neural network 3D-CNN to which are applied projection images, at a given view, for respective energy bins and/or material components. The projection images can be obtained by material decomposing spectral projection data, or by segmenting a reconstructed CT image into material-component images, which are then forward projected to generate energy-resolved material-component projections. The result generated by the 3D-CNN is an estimated scatter flux. To train the 3D-CNN, the target scatter flux in the training data can be simulated using a radiative transfer equation method.

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