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    SYSTEMS AND METHODS TO REDUCE UNSTRUCTURED AND STRUCTURED NOISE IN IMAGE DATA
    2.
    发明申请
    SYSTEMS AND METHODS TO REDUCE UNSTRUCTURED AND STRUCTURED NOISE IN IMAGE DATA 有权

    公开(公告)号：US20230029188A1

    公开(公告)日：2023-01-26

    申请号：US17385600

    申请日：2021-07-26

    Applicant: GE Precision Healthcare LLC

    Inventor： Rajesh Langoju ,  Utkarsh Agrawal ,  Bhushan Patil ,  Vanika Singhal ,  Bipul Das ,  Jiang Hsieh

    IPC: G06T5/00 ,  G06T7/00 ,  G06N3/08 ,  G06K9/62

    Abstract: The current disclosure provides methods and systems to reduce an amount of structured and unstructured noise in image data. Specifically, a multi-stage deep learning method is provided, comprising training a deep learning network using a set of training pairs interchangeably including input data from a first noisy dataset with a first noise level and target data from a second noisy dataset with a second noise level, and input data from the second noisy dataset and target data from the first noisy dataset; generating an ultra-low noise data equivalent based on a low noise data fed into the trained deep learning network; and retraining the deep learning network on the set of training pairs using the target data of the set of training pairs in a first retraining step, and using the ultra-low noise data equivalent as target data in a second retraining step.

    EXPLAINABLE DEEP INTERPOLATION
    4.
    发明公开
    EXPLAINABLE DEEP INTERPOLATION 审中-公开

    公开(公告)号：US20240160915A1

    公开(公告)日：2024-05-16

    申请号：US18055648

    申请日：2022-11-15

    Applicant: GE Precision Healthcare LLC

    Inventor： Prasad Sudhakara Murthy ,  Utkarsh Agrawal ,  Bipul Das

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: Systems/techniques that facilitate explainable deep interpolation are provided. In various embodiments, a system can access a data candidate, wherein a set of numerical elements of the data candidate are missing. In various aspects, the system can generate, via execution of a deep learning neural network on the data candidate, a set of weight maps for the set of missing numerical elements. In various instances, the system can compute the set of missing numerical elements by respectively combining, according to the set of weight maps, available interpolation neighbors of the set of missing numerical elements.

    METHOD AND SYSTEMS FOR ALIASING ARTIFACT REDUCTION IN COMPUTED TOMOGRAPHY IMAGING
    6.
    发明申请
    METHOD AND SYSTEMS FOR ALIASING ARTIFACT REDUCTION IN COMPUTED TOMOGRAPHY IMAGING 有权

    公开(公告)号：US20230048231A1

    公开(公告)日：2023-02-16

    申请号：US17444881

    申请日：2021-08-11

    Applicant: GE Precision Healthcare LLC

    Inventor： Rajesh Langoju ,  Utkarsh Agrawal ,  Risa Shigemasa ,  Bipul Das ,  Yasuhiro Imai ,  Jiang Hsieh

    IPC: G06T11/00 ,  G06T15/08 ,  G06T3/40 ,  G06N3/08 ,  G06N3/04

    Abstract: Various methods and systems are provided for computed tomography imaging. In one embodiment, a method includes acquiring, with an x-ray detector and an x-ray source coupled to a gantry, a three-dimensional image volume of a subject while the subject moves through a bore of the gantry and the gantry rotates the x-ray detector and the x-ray source around the subject, inputting the three-dimensional image volume to a trained deep neural network to generate a corrected three-dimensional image volume with a reduction in aliasing artifacts present in the three-dimensional image volume, and outputting the corrected three-dimensional image volume. In this way, aliasing artifacts caused by sub-sampling may be removed from computed tomography images while preserving details, texture, and sharpness in the computed tomography images.

    LEARNING LOSS FUNCTIONS USING DEEP LEARNING NETWORKS
    7.
    发明申请
    LEARNING LOSS FUNCTIONS USING DEEP LEARNING NETWORKS 有权

    公开(公告)号：US20210406681A1

    公开(公告)日：2021-12-30

    申请号：US16987449

    申请日：2020-08-07

    Applicant: GE Precision Healthcare LLC

    Inventor： Dattesh Shanbhag ,  Hariharan Ravishankar ,  Utkarsh Agrawal

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Techniques are provided for learning loss functions using DL networks and integrating these loss functions into DL based image transformation architectures. In one embodiment, a method is provided that comprising facilitating training, by a system operatively coupled to a processor, a first deep learning network to predict a loss function metric value of a loss function. The method further comprises employing, by the system, the first deep learning network to predict the loss function metric value in association with training a second deep learning network that to perform a defined deep learning task. In various embodiments, the loss function comprises a computationally complex loss function that is not easily implementable in existing deep learning packages, such as a non-differentiable loss function, a feature similarity index match (FSIM) loss function, a system transfer function, a visual information fidelity (VIF) loss function and the like.

    METHODS AND SYSTEMS FOR GENERATING DUAL-ENERGY IMAGES FROM A SINGLE-ENERGY IMAGING SYSTEM BASED ON ANATOMICAL SEGMENTATION
    8.
    发明申请
    METHODS AND SYSTEMS FOR GENERATING DUAL-ENERGY IMAGES FROM A SINGLE-ENERGY IMAGING SYSTEM BASED ON ANATOMICAL SEGMENTATION 有权

    公开(公告)号：US20250095143A1

    公开(公告)日：2025-03-20

    申请号：US18471188

    申请日：2023-09-20

    Applicant: GE Precision Healthcare LLC

    Inventor： Rajesh Langoju ,  Utkarsh Agrawal ,  Bipul Das ,  Risa Shigemasa ,  Yasuhiro Imai ,  Kok Yen Tham ,  Yuri Teraoka

    IPC: G06T7/00 ,  G06T7/11

    Abstract: Methods and systems are provided for transforming images from one energy level to another. In an example, a method includes obtaining an image at a first energy level, identifying a contrast phase of the image, entering the image as input to a segmentation model trained to output an anatomy mask that identifies each tissue type in the image, generating a guide image from the image and the anatomy mask using a regression model, entering the image and the guide image as input into an energy transformation model trained to output a transformed image at a different, second energy level, the energy transformation model selected from among a plurality of energy transformation models based on the contrast phase, and displaying a final transformed image and/or saving the final transformed image in memory, wherein the final transformed image is the transformed image or is generated based on the transformed image.

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