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    MAGNETIC RESONANCE IMAGING DATA CORRECTION METHODS AND SYSTEMS
    1.
    发明申请
    MAGNETIC RESONANCE IMAGING DATA CORRECTION METHODS AND SYSTEMS 有权
    磁共振成像数据校正方法和系统

    公开(公告)号：US20140312897A1

    公开(公告)日：2014-10-23

    申请号：US13866820

    申请日：2013-04-19

    Applicant: GENERAL ELECTRIC COMPANY

    Inventor： Ek Tsoon Tan ,  Christopher Judson Hardy ,  Kevin Franklin King ,  Zachary William Slavens ,  Luca Marinelli ,  Robert Marc Lebel

    IPC: G01R33/565

    CPC classification number: G01R33/56581 ,  G01R33/56341 ,  G01R33/56572

    Abstract: Systems and methods for correcting magnetic resonance (MR) data are provided. One method includes receiving the MR data and correcting errors present in the MR data due to non-uniformities in magnetic field gradients used to generate the diffusion weighted MR signals. The method also includes correcting errors present in the MR data due to concomitant gradient fields present in the magnetic field gradients by using one or more gradient terms. At least one of the gradient terms is corrected based on the correction of errors present in the MR data due to the non-uniformities in the magnetic field gradients.

    Abstract translation: 提供了用于校正磁共振（MR）数据的系统和方法。 一种方法包括由于用于产生扩散加权MR信号的磁场梯度中的不均匀性，接收MR数据并校正MR数据中存在的错误。 该方法还包括通过使用一个或多个梯度项来校正存在于磁场梯度中的伴随的梯度场的MR数据中存在的误差。 由于磁场梯度中的不均匀性，基于MR数据中存在的误差的校正来校正至少一个梯度项。

    Systems and methods for denoising medical images with deep learning network
    2.
    发明授权
    Systems and methods for denoising medical images with deep learning network 有权

    公开(公告)号：US10915990B2

    公开(公告)日：2021-02-09

    申请号：US16164634

    申请日：2018-10-18

    Applicant: GENERAL ELECTRIC COMPANY

    Inventor： Robert Marc Lebel

    IPC: G06T5/00 ,  G06K9/62 ,  G06T7/00 ,  G06K9/46 ,  G06K9/40 ,  G06K9/66 ,  G06K9/52

    Abstract: Methods and systems are provided for selectively denoising medical images. In an exemplary method, one or more deep learning networks are trained to map corrupted images onto a first type and a second type of artifacts present in corresponding corrupted images. Then the one or more trained learning networks are used to single out the first and second types of artifacts from a particular medical image. The first type of artifacts is removed to a first extent and the second type of artifacts is removed to a second extent. The first and second extents may be different. For example, one type of artifacts can be fully suppressed while the other can be partially removed form the medical image.

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