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    APPARATUS AND METHOD FOR BEAM-HARDENING CORRECTION IN COMPUTED TOMOGRAPHY
    32.
    发明申请
    APPARATUS AND METHOD FOR BEAM-HARDENING CORRECTION IN COMPUTED TOMOGRAPHY 有权

    公开(公告)号：US20250005818A1

    公开(公告)日：2025-01-02

    申请号：US18343608

    申请日：2023-06-28

    Applicant: CANON MEDICAL SYSTEMS CORPORATION

    Inventor： Yujie Lu ,  Liang Cai ,  Zhou Yu ,  Jian Zhou

    IPC: G06T11/00

    Abstract: According to some embodiments, a method comprises obtaining a group of reconstructed-image data; converting the group of reconstructed-image data to a derivative of Radon space, thereby generating Radon-space data, wherein the Radon-space data have a radial sampling pattern in the derivative of Radon space; and generating resampled data by inputting the Radon-space data into a first trained machine-learning model for resampling Radon-space data, wherein the resampled data have a cone-beam-projection-geometry-shaped sampling pattern in the derivative of Radon space.

    Method and apparatus for spectral computed tomography (CT) with multi-material decomposition into three or more material components
    34.
    发明授权
    Method and apparatus for spectral computed tomography (CT) with multi-material decomposition into three or more material components 有权

    公开(公告)号：US11238585B2

    公开(公告)日：2022-02-01

    申请号：US16839733

    申请日：2020-04-03

    Applicant: The University of Chicago ,  CANON MEDICAL SYSTEMS CORPORATION

    Inventor： Dimple Modgil ,  Patrick La Riviere ,  Yan Liu ,  Zhou Yu

    IPC: G06T7/00 ,  A61B6/03

    Abstract: A method and apparatus uses multi-material decomposition of three or more material components to generate material-component images from spectral images reconstructed from spectral computed tomography data. In three-component material decomposition e.g., the Mendonça method is used for multi-material decomposition when the attenuation values satisfy an assumed volume fraction condition (i.e., for a given voxel, the attenuation values are within a triangle having vertices given by unit volume fractions of three respective material components). However, when the volume fraction condition fails (e.g., the attenuation values are outside the triangle), either a shortest-Hausdorff-distance method or a closest-edge method is used for multi-material decomposition. For example, the attenuation values of the voxel are projected onto a lower-dimensional space (e.g., the space of a closest edge) and decomposed into a pair/single material component(s) of the lower-dimensional space.

    APPARATUS AND METHOD USING PHYSICAL MODEL BASED DEEP LEARNING (DL) TO IMPROVE IMAGE QUALITY IN IMAGES THAT ARE RECONSTRUCTED USING COMPUTED TOMOGRAPHY (CT)
    35.
    发明申请
    APPARATUS AND METHOD USING PHYSICAL MODEL BASED DEEP LEARNING (DL) TO IMPROVE IMAGE QUALITY IN IMAGES THAT ARE RECONSTRUCTED USING COMPUTED TOMOGRAPHY (CT) 有权

    公开(公告)号：US20210007695A1

    公开(公告)日：2021-01-14

    申请号：US16510632

    申请日：2019-07-12

    Applicant: Canon Medical Systems Corporation

    Inventor： Yujie LU ,  Zhou Yu ,  Jian Zhou

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

    Abstract: A method and apparatus is provided that uses a deep learning (DL) network to improve the image quality of computed tomography (CT) images, which were reconstructed using an analytical reconstruction method. The DL network is trained to use physical-model information in addition to the analytical reconstructed images to generate the improved images. The physical-model information can be generated, e.g., by estimating a gradient of the objective function (or just the data-fidelity term) of a model-based iterative reconstruction (MBIR) method (e.g., by performing one or more iterations of the MBIR method). The MBIR method can incorporate physical models for X-ray scatter, detector resolution/noise/non-linearities, beam-hardening, attenuation, and/or the system geometry. The DL network can be trained using input data comprising images reconstructed using the analytical reconstruction method and target data comprising images reconstructed using the MBIR method.

    APPARATUS AND METHOD COMBINING DEEP LEARNING (DL) WITH AN X-RAY COMPUTED TOMOGRAPHY (CT) SCANNER HAVING A MULTI-RESOLUTION DETECTOR
    36.
    发明申请
    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：US20210007694A1

    公开(公告)日：2021-01-14

    申请号：US16509369

    申请日：2019-07-11

    Applicant: Canon Medical Systems Corporation

    Inventor： Ilmar HEIN ,  Zhou Yu ,  Efren Lee

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

    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).

    METHOD AND APPARATUS FOR FAST SCATTER SIMULATION AND CORRECTION IN COMPUTED TOMOGRAPHY (CT)
    37.
    发明申请
    METHOD AND APPARATUS FOR FAST SCATTER SIMULATION AND CORRECTION IN COMPUTED TOMOGRAPHY (CT) 审中-公开

    公开(公告)号：US20200340932A1

    公开(公告)日：2020-10-29

    申请号：US16392177

    申请日：2019-04-23

    Applicant: Canon Medical Systems Corporation

    Inventor： Yujie Lu ,  Zhou Yu ,  Richard Thompson

    IPC: G01N23/046 ,  G06N20/00 ,  G06N3/08

    Abstract: X-ray scatter simulations to correct computed tomography (CT) data can be accelerated using a non-uniform discretization of the RTE, reducing the number of computations without sacrificing precision. For example, a coarser discretization can be used for higher-order/multiple-scatter flux, than for first-order-scatter flux. Similarly, precision is preserved when coarser angular resolution is used to simulate scatter within a patient, and finer angular resolution used for the scatter flux incident on detectors. Finer energy resolution is more beneficial at lower X-ray energies, and coarser spatial resolution can be applied to regions exhibiting less X-ray scatter (e.g., air and regions with low radiodensity). Further, predefined non-uniform discretization can be learned from scatter simulations on training data (e.g., a priori compressed grids learned from non-uniform grids generated by adaptive mesh methods).

    Medical image processing apparatus and medical image processing system
    38.
    发明授权
    Medical image processing apparatus and medical image processing system 有权

    公开(公告)号：US10803984B2

    公开(公告)日：2020-10-13

    申请号：US16143161

    申请日：2018-09-26

    Applicant: Canon Medical Systems Corporation

    Inventor： Jian Zhou ,  Zhou Yu ,  Yan Liu

    IPC: G16H30/20 ,  G06T5/00 ,  G06T5/50 ,  G06T11/00 ,  G06T7/00 ,  G06K9/62 ,  G06K9/68 ,  G06K9/66 ,  G06N3/08

    Abstract: A medical image processing apparatus according to an embodiment comprises a memory and processing circuitry. The memory is configured to store a plurality of neural networks corresponding to a plurality of imaging target sites, respectively, the neural networks each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired for the corresponding imaging target site. The processing circuitry is configured to process first data into second data using, among the neural networks, the neural network corresponding to the imaging target site for the first data, wherein the first data is input to the input layer and the second data is output from the output layer.

    APPARATUSES AND A METHOD FOR ARTIFACT REDUCTION IN MEDICAL IMAGES USING A NEURAL NETWORK
    39.
    发明申请
    APPARATUSES AND A METHOD FOR ARTIFACT REDUCTION IN MEDICAL IMAGES USING A NEURAL NETWORK 审中-公开

    公开(公告)号：US20200305806A1

    公开(公告)日：2020-10-01

    申请号：US16370230

    申请日：2019-03-29

    Applicant: Canon Medical Systems Corporation

    Inventor： Qiulin TANG ,  Jian Zhou ,  Zhou Yu

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

    Abstract: A method and apparatuses are provided that use a neural network to correct artifacts in computed tomography (CT) images, especially cone-beam CT (CBCT) artifacts. The neural network is trained using a training dataset of artifact-minimized images paired with respective artifact-exhibiting images. In some embodiments, the artifact-minimized images are acquired using a small cone angle for the X-ray beam, and the artifact-exhibiting images are acquired either by forwarding projecting the artifact-minimized images using a large-cone-angle CBCT configuration or by performing a CBCT scan. In some embodiments, the network is a 2D convolutional neural network, and an artifact-exhibiting image is applied to the neural network as 2D slices taken for the coronal and/or sagittal views. Then the 2D image results from the neural network are reassembled as a 3D imaging having reduced imaging artifacts.

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