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    Deep learning medical systems and methods for image reconstruction and quality evaluation
    31.
    发明授权
    Deep learning medical systems and methods for image reconstruction and quality evaluation 有权

    公开(公告)号：US10896352B2

    公开(公告)日：2021-01-19

    申请号：US16697904

    申请日：2019-11-27

    Applicant: General Electric Company

    Inventor： Jiang Hsieh ,  Gopal Avinash ,  Saad Sirohey ,  Xin Wang ,  Zhye Yin ,  Bruno De Man

    IPC: G06K9/00 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06K9/03 ,  G06K9/46 ,  G06T7/00

    Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.

    Deep learning medical systems and methods for image reconstruction and quality evaluation
    33.
    发明授权
    Deep learning medical systems and methods for image reconstruction and quality evaluation 有权

    公开(公告)号：US10565477B2

    公开(公告)日：2020-02-18

    申请号：US16511972

    申请日：2019-07-15

    Applicant: General Electric Company

    Inventor： Jiang Hsieh ,  Gopal Avinash ,  Saad Sirohey ,  Xin Wang ,  Zhye Yin ,  Bruno De Man

    IPC: G06K9/00 ,  G06K9/62 ,  G06K9/03 ,  G06K9/46 ,  G06N3/04 ,  G06N3/08 ,  G06T7/00

    Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.

    DEEP LEARNING MEDICAL SYSTEMS AND METHODS FOR IMAGE ACQUISITION
    34.
    发明申请
    DEEP LEARNING MEDICAL SYSTEMS AND METHODS FOR IMAGE ACQUISITION 审中-公开

    公开(公告)号：US20190050987A1

    公开(公告)日：2019-02-14

    申请号：US16154870

    申请日：2018-10-09

    Applicant: General Electric Company

    Inventor： Jiang Hsieh ,  Gopal Avinash ,  Saad Sirohey

    IPC: G06T7/00 ,  G06N3/08 ,  G06F19/00 ,  G06N3/04 ,  G16H40/40 ,  G16H30/40

    Abstract: Methods and apparatus for improved deep learning for image acquisition are provided. An imaging system configuration apparatus includes a training learning device including a first processor to implement a first deep learning network (DLN) to learn a first set of imaging system configuration parameters based on a first set of inputs from a plurality of prior image acquisitions to configure at least one imaging system for image acquisition, the training learning device to receive and process feedback including operational data from the plurality of image acquisitions by the at least one imaging system. The example apparatus includes a deployed learning device including a second processor to implement a second DLN, the second DLN generated from the first DLN of the training learning device, the deployed learning device configured to provide a second imaging system configuration parameter to the imaging system in response to receiving a second input for image acquisition.

    DEEP LEARNING MEDICAL SYSTEMS AND METHODS FOR IMAGE ACQUISITION
    35.
    发明申请
    DEEP LEARNING MEDICAL SYSTEMS AND METHODS FOR IMAGE ACQUISITION 审中-公开

    公开(公告)号：US20180144466A1

    公开(公告)日：2018-05-24

    申请号：US15360626

    申请日：2016-11-23

    Applicant: General Electric Company

    Inventor： Jiang Hsieh ,  Gopal Avinash ,  Saad Sirohey

    IPC: G06T7/00 ,  G06N3/04 ,  G06N3/08

    CPC classification number: G06T7/0012 ,  G06F19/00 ,  G06N3/04 ,  G06N3/08 ,  G06T2207/10004 ,  G06T2207/20081 ,  G06T2207/20084 ,  G16H30/40 ,  G16H40/40

    Abstract: Methods and apparatus for improved deep learning for image acquisition are provided. An imaging system configuration apparatus includes a training learning device including a first processor to implement a first deep learning network (DLN) to learn a first set of imaging system configuration parameters based on a first set of inputs from a plurality of prior image acquisitions to configure at least one imaging system for image acquisition, the training learning device to receive and process feedback including operational data from the plurality of image acquisitions by the at least one imaging system. The example apparatus includes a deployed learning device including a second processor to implement a second DLN, the second DLN generated from the first DLN of the training learning device, the deployed learning device configured to provide a second imaging system configuration parameter to the imaging system in response to receiving a second input for image acquisition.

    SYSTEMS AND METHODS FOR ARTIFACT REMOVAL FOR COMPUTED TOMOGRAPHY IMAGING
    37.
    发明申请
    SYSTEMS AND METHODS FOR ARTIFACT REMOVAL FOR COMPUTED TOMOGRAPHY IMAGING 有权
    用于计算机图像成像的物理去除系统和方法

    公开(公告)号：US20160270754A1

    公开(公告)日：2016-09-22

    申请号：US14663864

    申请日：2015-03-20

    Applicant: General Electric Company ,  Wisconsin Alumni Research Foundation

    Inventor： Guang-Hong Chen ,  Jiang Hsieh

    IPC: A61B6/00 ,  A61B6/03

    CPC classification number: A61B6/5258 ,  A61B6/032 ,  A61B6/4035 ,  A61B6/44 ,  A61B6/4405 ,  A61B6/507 ,  A61B6/508 ,  A61B6/56

    Abstract: An imaging system includes a computed tomography (CT) acquisition unit and at least one processor. The CT acquisition unit includes an X-ray source and a CT detector configured to collect CT imaging data of an object to be imaged. The at least one processor is operably coupled to the CT acquisition unit, and is configured to reconstruct an image using the CT imaging information; extract spatial frequency information from at least a portion of the image, wherein the spatial frequency is defined along a longitudinal direction; and remove a periodically recurring artifact from the at least a portion of the image based on a spatial frequency corresponding to a longitudinal collection periodicity to provide a corrected image.

    Abstract translation: 成像系统包括计算机断层摄影（CT）获取单元和至少一个处理器。 CT采集单元包括：X射线源和CT检测器，被配置为收集待成像对象的CT成像数据。 所述至少一个处理器可操作地耦合到所述CT采集单元，并且被配置为使用所述CT成像信息重建图像; 从所述图像的至少一部分中提取空间频率信息，其中所述空间频率沿着纵向限定; 并且基于对应于纵向收集周期的空间频率从所述图像的所述至少一部分去除周期性重复的伪像，以提供校正图像。

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