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    SYSTEMS AND METHODS FOR AUTOMATIC CARDIAC IMAGE ANALYSIS
    24.
    发明公开
    SYSTEMS AND METHODS FOR AUTOMATIC CARDIAC IMAGE ANALYSIS 审中-公开

    公开(公告)号：US20240144469A1

    公开(公告)日：2024-05-02

    申请号：US17973982

    申请日：2022-10-26

    申请人： Shanghai United Imaging Intelligence Co., Ltd.

    发明人： Xiao Chen ,  Shanhui Sun ,  Terrence Chen ,  Arun Innanje

    IPC分类号： G06T7/00 ,  G06T7/11 ,  G06T7/30 ,  G06V10/764

    CPC分类号： G06T7/0012 ,  G06T7/11 ,  G06T7/30 ,  G06V10/764 ,  G06T2207/10088 ,  G06T2207/30048

    摘要： Cardiac images such as cardiac magnetic resonance (CMR) images and tissue characterization maps (e.g., T1/T2 maps) may be analyzed automatically using machine learning (ML) techniques, and reports may be generated to summarize the analysis. The ML techniques may include training one or more of an image classification model, a heart segmentation model, or a cardiac pathology detection model to automate the image analysis and/or reporting process. The image classification model may be capable of grouping the cardiac images into different categories, the heart segmentation model may be capable of delineating different anatomical regions of the heart, and the pathology detection model may be capable of detecting a medical abnormality in one or more of the anatomical regions based on tissue patterns or parameters automatically recognized by the detection model. Image registration that compensates for the impact of motions or movements may also be conducted automatically using ML techniques.

    SYSTEM AND METHOD FOR MAGNIFYING AN IMAGE BASED ON TRAINED NEURAL NETWORKS
    27.
    发明公开
    SYSTEM AND METHOD FOR MAGNIFYING AN IMAGE BASED ON TRAINED NEURAL NETWORKS 审中-公开

    公开(公告)号：US20240087082A1

    公开(公告)日：2024-03-14

    申请号：US17943724

    申请日：2022-09-13

    申请人： Shanghai United Imaging Intelligence Co., LTD.

    发明人： Yikang Liu ,  Shanhui Sun ,  Terrence Chen

    IPC分类号： G06T3/40 ,  G06T7/11

    CPC分类号： G06T3/4046 ,  G06T7/11 ,  G06F3/0482

    摘要： A magnification system for magnifying an image based on trained neural networks is disclosed. The magnification system receives a first user input associated with a selection of a region of interest (ROI) within an input image of a site and a second user input associated with a first magnification factor of the selected ROI. The first magnification factor is associated with a magnification of the ROI in the input image. The ROI is modified based on an application of a first neural network model on the ROI. The modification of the ROI corresponds to a magnified image that is predicted in accordance with the first magnification factor. A display device is controlled to display the modified ROI.

    SYSTEMS AND METHODS FOR MRI DATA PROCESSING
    28.
    发明公开
    SYSTEMS AND METHODS FOR MRI DATA PROCESSING 审中-公开

    公开(公告)号：US20230367850A1

    公开(公告)日：2023-11-16

    申请号：US17741323

    申请日：2022-05-10

    申请人： Shanghai United Imaging Intelligence Co., Ltd.

    发明人： Xiao Chen ,  Yikang Liu ,  Zhang Chen ,  Shanhui Sun ,  Terrence Chen ,  Daniel Hyungseok Pak

    IPC分类号： G06K9/62 ,  G06T11/00 ,  G06N20/00 ,  G01R33/56 ,  A61B5/00

    CPC分类号： G06K9/6256 ,  A61B5/7267 ,  G01R33/5608 ,  G06N20/00 ,  G06T11/005

    摘要： Described herein are systems, methods, and instrumentalities associated with processing complex-valued MRI data using a machine learning (ML) model. The ML model may be learned based on synthetically generated MRI training data and by applying one or more meta-learning techniques. The MRI training data may be generated by adding phase information to real-valued MRI data and/or by converting single-coil MRI data into multi-coil MRI data based on coil sensitivity maps. The meta-learning process may include using portions of the training data to conduct a first round of learning to determine updated model parameters and using remaining portions of the training data to test the updated model parameters. Losses associated with the testing may then be determined and used to refine the model parameters. The ML model learned using these techniques may be adopted for a variety of tasks including, for example, MRI image reconstruction and/or de-noising.