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    APPARATUS AND METHOD USING DEEP LEARNING (DL) TO COMPENSATE FOR LARGE FOCAL SPOT SIZE IN X-RAY PROJECTION IMAGING
    1.
    发明申请
    APPARATUS AND METHOD USING DEEP LEARNING (DL) TO COMPENSATE FOR LARGE FOCAL SPOT SIZE IN X-RAY PROJECTION IMAGING 有权

    公开(公告)号：US20210007702A1

    公开(公告)日：2021-01-14

    申请号：US16510594

    申请日：2019-07-12

    Applicant: Canon Medical Systems Corporation

    Inventor： Tzu-Cheng LEE ,  Jian ZHOU ,  Zhou YU

    IPC: A61B6/00 ,  G06N3/08 ,  G06T7/00

    Abstract: A method and apparatus is provided that uses a deep learning (DL) network to correct projection images acquired using an X-ray source with a large focal spot size. The DL network is trained using a training dataset that includes input data and target data. The input data includes large-focal-spot-size X-ray projection data, and the output data includes small-focal-spot-size X-ray projection data (i.e., smaller than the focal spot of the input data). Thus, the DL network is trained to improve the resolution of projection data acquired using a large focal spot size, and obtain a resolution similar to what is achieved using a small focal spot size. Further, the DL network is can be trained to additional correct other aspects of the projection data (e.g., denoising the projection data).

    AI-AIDED COMPUTED TOMOGRAPHY USING 3D SCANOGRAM FOR AUTOMATED LENS PROTECTION
    3.
    发明申请
    AI-AIDED COMPUTED TOMOGRAPHY USING 3D SCANOGRAM FOR AUTOMATED LENS PROTECTION 有权

    公开(公告)号：US20240389961A1

    公开(公告)日：2024-11-28

    申请号：US18323016

    申请日：2023-05-24

    Applicant: CANON MEDICAL SYSTEMS CORPORATION

    Inventor： Tzu-Cheng LEE ,  Liang CAI ,  Jian ZHOU

    IPC: A61B6/00 ,  A61B6/03 ,  A61B6/04

    Abstract: A method of controlling computed tomography (CT) scanning includes performing a scout CT scan of a 3D region of a head of a subject to be examined, using a CT gantry having an X-ray source and an X-ray detector both rotatably supported thereby, to produce image data. Anatomical landmarks are detected for identifying an orbitomeatal base line (OMBL), by inputting cross-sectional image data of the 3D region generated from the image data to a trained machine learning model. A tilt angle of the CT gantry is determined based on the detected anatomical landmarks. A diagnostic CT scan of the object is performed using the CT gantry tilted at the determined tilt angle.

    APPARATUS AND METHOD FOR SINOGRAM RESTORATION IN COMPUTED TOMOGRAPHY (CT) USING ADAPTIVE FILTERING WITH DEEP LEARNING (DL)
    5.
    发明申请
    APPARATUS AND METHOD FOR SINOGRAM RESTORATION IN COMPUTED TOMOGRAPHY (CT) USING ADAPTIVE FILTERING WITH DEEP LEARNING (DL) 审中-公开

    公开(公告)号：US20200311490A1

    公开(公告)日：2020-10-01

    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.

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