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    STATIONARY MULTI-SOURCE AI-POWERED REAL-TIME TOMOGRAPHY (SMART)
    3.
    发明公开
    STATIONARY MULTI-SOURCE AI-POWERED REAL-TIME TOMOGRAPHY (SMART) 审中-公开

    公开(公告)号：US20240070938A1

    公开(公告)日：2024-02-29

    申请号：US18238605

    申请日：2023-08-28

    Applicant: Rensselaer Polytechnic Institute

    Inventor： Ge Wang ,  Weiwen Wu ,  Yan Xi

    IPC: G06T11/00

    CPC classification number: G06T11/008 ,  G06T11/005 ,  G06T2211/412 ,  G06T2211/428 ,  G06T2211/441

    Abstract: In one embodiment, there is provided a dynamic multi-source image reconstruction apparatus. The apparatus includes a first reconstruction stage, a second reconstruction stage, and a refinement stage. The first reconstruction stage is configured to receive an input data set including a group of data frames. Each data frame corresponds to a respective time step. Each data frame includes a number of projection data sets. Each projection data set corresponds to a respective source-detector pair of a stationary multi-source tomography system. The first reconstruction stage is further configured to reconstruct a first intermediate image based, at least in part, on the group of data frames. The second reconstruction stage is configured to receive a selected data frame and to reconstruct a second intermediate image with a constraint of the first intermediate image as prior. The refinement stage is configured to refine the second intermediate image to produce a three-dimensional output image.

    CT super-resolution GAN constrained by the identical, residual and cycle learning ensemble (GAN-circle)
    5.
    发明授权
    CT super-resolution GAN constrained by the identical, residual and cycle learning ensemble (GAN-circle) 有权

    公开(公告)号：US11854160B2

    公开(公告)日：2023-12-26

    申请号：US17564728

    申请日：2021-12-29

    Applicant: RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Ge Wang ,  Chenyu You ,  Wenxiang Cong ,  Hongming Shan

    IPC: G06T3/40 ,  G06N3/045

    CPC classification number: G06T3/4076 ,  G06N3/045

    Abstract: A system for generating a high resolution (HR) computed tomography (CT) image from a low resolution (LR) CT image is described. The system includes a first generative adversarial network (GAN) and a second GAN. The first GAN includes a first generative neural network (G) configured to receive a training LR image dataset and to generate a corresponding estimated HR image dataset, and a first discriminative neural network (DY) configured to compare a training HR image dataset and the estimated HR image dataset. The second GAN includes a second generative neural network (F) configured to receive the training HR image dataset and to generate a corresponding estimated LR image dataset, and a second discriminative neural network (DX) configured to compare the training LR image dataset and the estimated LR image dataset. The system further includes an optimization module configured to determine an optimization function based, at least in part, on at least one of the estimated HR image dataset and/or the estimated LR image dataset. The optimization function contains at least one loss function. The optimization module is further configured to adjust a plurality of neural network parameters associated with at least one of the first GAN and/or the second GAN, to optimize the optimization function.

    NOISE2SIM - SIMILARITY-BASED SELF-LEARNING FOR IMAGE DENOISING
    6.
    发明公开
    NOISE2SIM - SIMILARITY-BASED SELF-LEARNING FOR IMAGE DENOISING 审中-公开

    公开(公告)号：US20230394631A1

    公开(公告)日：2023-12-07

    申请号：US18035571

    申请日：2021-11-05

    Applicant: Rensselaer Polytechnic Institute

    Inventor： Ge Wang ,  Chuang Niu

    IPC: G06T5/00

    CPC classification number: G06T5/002 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/10056 ,  G06T2207/10081 ,  G06T2207/10076 ,  G06T2207/10088

    Abstract: One embodiment provides a method of training an artificial neural network (ANN) for denoising. The method includes generating, by a similarity module, a respective set of similar elements for each noisy input element of a number of noisy input elements included in a single noisy input data set. Each noisy input element includes information and noise. The method further includes generating, by a sample pair module, a plurality of training sample pairs. Each training sample pair includes a pair of selected similar elements corresponding to a respective noisy input element. The method further includes training, by a training module, an ANN using the plurality of training sample pairs. Each set of similar elements is generated prior to training the ANN. The plurality of training sample pairs is generated during training the ANN. The training is unsupervised.

    Filtration methods for dual-energy X-RAY CT
    7.
    发明授权
    Filtration methods for dual-energy X-RAY CT 有权

    公开(公告)号：US11701073B2

    公开(公告)日：2023-07-18

    申请号：US17354286

    申请日：2021-06-22

    Applicant: Rensselaer Polytechnic Institute

    Inventor： Daniel David Harrison ,  Ge Wang

    IPC: A61B6/00 ,  A61B6/03

    CPC classification number: A61B6/4035 ,  A61B6/032 ,  A61B6/4085 ,  A61B6/5258 ,  A61B6/4007 ,  A61B6/482

    Abstract: Systems and method for performing X-ray computed tomography (CT) that can improve spectral separation and decrease motion artifacts without increasing radiation dose are provided. The systems and method can be used with either a kVp-switching source or a single-kVp source. When used with a kVp-switching source, an absorption grating and a filter grating can be disposed between the X-ray source and the sample to be imaged. Relative motion of the filter and absorption gratings can by synchronized to the kVp switching frequency of the X-ray source. When used with a single-kVp source, a combination of absorption and filter gratings can be used and can be driven in an oscillation movement that is optimized for a single-kVp X-ray source. With a single-kVp source, the absorption grating can also be omitted and the filter grating can remain stationary.

    Image reconstruction method for computed tomography
    8.
    发明授权
    Image reconstruction method for computed tomography 有权

    公开(公告)号：US11423591B2

    公开(公告)日：2022-08-23

    申请号：US16481298

    申请日：2017-05-23

    Applicant: RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Ge Wang ,  Wenxiang Cong ,  Qingsong Yang

    IPC: G06T11/00 ,  A61B6/03

    Abstract: Systems and methods for reconstructing images for computed tomography are provided. Image reconstruction can be based on a realistic polychromatic physical model, and can include use of both an analytical algorithm and a single-variable optimization method. The optimization method can be used to solve the non-linear polychromatic X-ray integral model in the projection domain, resulting in an accurate decomposition for sinograms of two physical basis components.

    RAPID FILTRATION METHODS FOR DUAL-ENERGY X-RAY CT
    9.
    发明申请
    RAPID FILTRATION METHODS FOR DUAL-ENERGY X-RAY CT 有权

    公开(公告)号：US20220257203A1

    公开(公告)日：2022-08-18

    申请号：US17734311

    申请日：2022-05-02

    Applicant: RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Ge Wang ,  Wenxiang Cong ,  Yan Xi

    IPC: A61B6/00 ,  A61B6/03

    Abstract: Systems and method for performing X-ray computed tomography (CT) that can improve spectral separation and decrease motion artifacts without increasing radiation dose are provided. The systems and method can be used with either a kVp-switching source or a single-kVp source. When used with a kVp-switching source, an absorption grating and a filter grating can be disposed between the X-ray source and the sample to be imaged. Relative motion of the filter and absorption gratings can by synchronized to the kVp switching frequency of the X-ray source. When used with a single-kVp source, a combination of absorption and filter gratings can be used and can be driven in an oscillation movement that is optimized for a single-kVp X-ray source. With a single-kVp source, the absorption grating can also be omitted and the filter grating can remain stationary.

    CT SUPER-RESOLUTION GAN CONSTRAINED BY THE IDENTICAL, RESIDUAL AND CYCLE LEARNING ENSEMBLE (GAN-CIRCLE)
    10.
    发明申请
    CT SUPER-RESOLUTION GAN CONSTRAINED BY THE IDENTICAL, RESIDUAL AND CYCLE LEARNING ENSEMBLE (GAN-CIRCLE) 有权

    公开(公告)号：US20220230278A1

    公开(公告)日：2022-07-21

    申请号：US17564728

    申请日：2021-12-29

    Applicant: RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Ge Wang ,  Chenyu You ,  Wenxiang Cong ,  Hongming Shan

    IPC: G06T3/40 ,  G06N3/04

    Abstract: A system for generating a high resolution (HR) computed tomography (CT) image from a low resolution (LR) CT image is described. The system includes a first generative adversarial network (GAN) and a second GAN. The first GAN includes a first generative neural network (G) configured to receive a training LR image dataset and to generate a corresponding estimated HR image dataset, and a first discriminative neural network (DY) configured to compare a training HR image dataset and the estimated HR image dataset. The second GAN includes a second generative neural network (F) configured to receive the training HR image dataset and to generate a corresponding estimated LR image dataset, and a second discriminative neural network (DX) configured to compare the training LR image dataset and the estimated LR image dataset. The system further includes an optimization module configured to determine an optimization function based, at least in part, on at least one of the estimated HR image dataset and/or the estimated LR image dataset. The optimization function contains at least one loss function. The optimization module is further configured to adjust a plurality of neural network parameters associated with at least one of the first GAN and/or the second GAN, to optimize the optimization function.

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