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    FEW-VIEW CT IMAGE RECONSTRUCTION SYSTEM
    3.
    发明公开
    FEW-VIEW CT IMAGE RECONSTRUCTION SYSTEM 审中-公开

    公开(公告)号：US20240041412A1

    公开(公告)日：2024-02-08

    申请号：US18381214

    申请日：2023-10-18

    Applicant: RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Huidong Xie ,  Ge Wang ,  Hongming Shan ,  Wenxiang Cong

    IPC: A61B6/03 ,  G06T11/00 ,  A61B6/00

    CPC classification number: A61B6/032 ,  G06T11/005 ,  G06T11/006 ,  A61B6/5205 ,  G06T2211/421 ,  G06T2211/436

    Abstract: A system for few-view computed tomography (CT) image reconstruction is described. The system includes a preprocessing module, a first generator network, and a discriminator network. The preprocessing module is configured to apply a ramp filter to an input sinogram to yield a filtered sinogram. The first generator network is configured to receive the filtered sinogram, to learn a filtered back-projection operation and to provide a first reconstructed image as output. The first reconstructed image corresponds to the input sinogram. The discriminator network is configured to determine whether a received image corresponds to the first reconstructed image or a corresponding ground truth image. The generator network and the discriminator network correspond to a Wasserstein generative adversarial network (WGAN). The WGAN is optimized using an objective function based, at least in part, on a Wasserstein distance and based, at least in part, on a gradient penalty.

    ROBOTIC ARM-BASED CLINICAL MICRO-CT SYSTEM AND METHOD
    4.
    发明申请
    ROBOTIC ARM-BASED CLINICAL MICRO-CT SYSTEM AND METHOD 有权

    公开(公告)号：US20230133386A1

    公开(公告)日：2023-05-04

    申请号：US17980948

    申请日：2022-11-04

    Applicant: Rensselaer Polytechnic Institute

    Inventor： Ge Wang ,  Mengzhou Li ,  Zheng Fang ,  Wenxiang Cong

    IPC: A61B6/03 ,  A61B6/00 ,  G06T11/00

    Abstract: In one embodiment, there is provided a micro-CT (computed tomography) apparatus. The micro-CT apparatus includes an x-ray source coupled to a source robotic arm, an x-ray detector coupled to a detector robotic arm, and a computing device. The computing device includes a data acquisition module and a reconstruction module. The data acquisition module is configured to acquire local scan data of a volume of interest (VOI) contained in an imaging object. The reconstruction module is configured to reconstruct an image of the VOI based, at least in part, on the local scan data, and based, at least in part, on background compensation data.

    Detection scheme for x-ray small angle scattering
    5.
    发明授权
    Detection scheme for x-ray small angle scattering 有权

    公开(公告)号：US11313814B2

    公开(公告)日：2022-04-26

    申请号：US16955939

    申请日：2018-12-20

    Applicant: RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Ge Wang ,  Guang Li ,  Wenxiang Cong

    IPC: G01N23/20008 ,  G01N23/201 ,  G01N23/046 ,  G21K1/02 ,  A61B6/03

    Abstract: A detection scheme for x-ray small angle scattering is described. An x-ray small angle scattering apparatus may include a first grating and a complementary second grating. The first grating includes a plurality of first grating cells. The complementary second grating includes a plurality of second grating cells. The second grating is positioned relative to the first grating. A configuration of the first grating, a configuration of the second grating and the relative positioning of the grating are configured to pass one or more small angle scattered photons and to block one or more Compton scattered photons and one or more main x-ray photons.

    Systems and methods for integrating tomographic image reconstruction and radiomics using neural networks
    6.
    发明授权
    Systems and methods for integrating tomographic image reconstruction and radiomics using neural networks 有权

    公开(公告)号：US11049244B2

    公开(公告)日：2021-06-29

    申请号：US16621800

    申请日：2018-06-18

    Applicant: Rensselaer Polytechnic Institute

    Inventor： Ge Wang ,  Mannudeep Kalra ,  Juergen Hahn ,  Uwe Kruger ,  Wenxiang Cong ,  Hongming Shan

    IPC: G06T7/00 ,  G06T7/11 ,  G06N3/08

    Abstract: Computed tomography (CT) screening, diagnosis, or another image analysis tasks are performed using one or more networks and/or algorithms to either integrate complementary tomographic image reconstructions and radiomics or map tomographic raw data directly to diagnostic findings in the machine learning framework. One or more reconstruction networks are trained to reconstruct tomographic images from a training set of CT projection data. One or more radiomics networks are trained to extract features from the tomographic images and associated training diagnostic data. The networks/algorithms are integrated into an end-to-end network and trained. A set of tomographic data, e.g., CT projection data, and other relevant information from an individual is input to the end-to-end network, and a potential diagnosis for the individual based on the features extracted by the end-to-end network is produced. The systems and methods can be applied to CT projection data, MRI data, nuclear imaging data, ultrasound signals, optical data, other types of tomographic data, or combinations thereof.

    STATIONARY IN-VIVO GRATING-ENABLED MICRO-CT ARCHITECTURE (SIGMA)
    7.
    发明申请
    STATIONARY IN-VIVO GRATING-ENABLED MICRO-CT ARCHITECTURE (SIGMA) 审中-公开

    公开(公告)号：US20200261030A1

    公开(公告)日：2020-08-20

    申请号：US16761543

    申请日：2018-11-06

    Applicant: RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Ge Wang ,  Wenxiang Cong ,  Qingsong Yang ,  Guang Li

    IPC: A61B6/03 ,  A61B6/00

    Abstract: A stationary in-vivo grating-enabled micro-CT (computed tomography) architecture (SIGMA) system includes CT scanner control circuitry and a number of imaging chains. Each imaging chain includes an x-ray source array, a phase grating, an analyzer grating and a detector array. Each imaging chain is stationary and each x-ray source array includes a plurality of x-ray source elements. Each imaging chain has a centerline, the centerlines of the number of imaging chains intersect at a center point and a first angle between the centerlines of a first adjacent pair of imaging chains equals a second angle between the centerlines of a second adjacent pair of imaging chains. A plurality of selected x-ray source elements of a first x-ray source array is configured to emit a plurality of x-ray beams in a multiplexing fashion.

    CT super-resolution GAN constrained by the identical, residual and cycle learning ensemble (GAN-circle)
    10.
    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.

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