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    SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR CONTRAST-ENHANCED RADIOLOGY USING MACHINE LEARNING
    4.
    发明公开
    SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR CONTRAST-ENHANCED RADIOLOGY USING MACHINE LEARNING 审中-公开

    公开(公告)号：US20240050054A1

    公开(公告)日：2024-02-15

    申请号：US18280193

    申请日：2021-11-29

    Applicant: Bayer Aktiengesellschaft

    Inventor： Matthias LENGA ,  Marvin PURTORAB

    IPC: A61B6/00 ,  A61B6/03 ,  A61B8/08

    CPC classification number: A61B6/481 ,  A61B6/032 ,  A61B8/481

    Abstract: A method for providing a prediction of a representation of an examination region that was generated using a medical image technique involving a contrast agent may include receiving a first representation in frequency space of an examination region of an examination object, receiving a second representation in the frequency space of the examination region of the examination object, providing the first representation and the second representation as an input to a predictive machine learning model that is configured to provide, as an output, a prediction of a representation in the frequency space of the examination region with an amount of the contrast agent administered during a medical imaging technique, receiving the output of the predictive machine learning model based on the input, and converting the output of the predictive machine learning model to a representation in real space of the examination region of the examination object.

    IMPLICIT REGISTRATION FOR IMPROVING SYNTHESIZED FULL-CONTRAST IMAGE PREDICTION TOOL
    5.
    发明公开
    IMPLICIT REGISTRATION FOR IMPROVING SYNTHESIZED FULL-CONTRAST IMAGE PREDICTION TOOL 审中-公开

    公开(公告)号：US20240193738A1

    公开(公告)日：2024-06-13

    申请号：US18556528

    申请日：2022-04-13

    Applicant: Bayer Aktiengesellschaft

    Inventor： Veronica CORONA ,  Marvin PURTORAB ,  Sara LORIO ,  Thiago RAMOS DOS SANTOS

    IPC: G06T5/50 ,  G06T5/60

    CPC classification number: G06T5/50 ,  G06T5/60 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: A method of training a prediction tool to generate at least one synthetic full-contrast image from zero-contrast and low-contrast images of a subject may involve receiving a training set a set of images of a set of subjects, the images of each subject comprising a full-contrast image, a low-contrast image, a first zero-contrast image acquired prior to the acquisition of the full-contrast image, and a second zero-contrast image acquired prior to the acquisition of the low-contrast image. An artificial neural network may be trained with the training set by applying the first and second zero-contrast images from the set of images and the low-contrast images from the set of images as input to the artificial neural network and using a cost function to compare the output of the artificial neural network with the full-contrast images from the set of images to train parameters of the artificial neural network using backpropagation.

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