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    Training a CNN with pseudo ground truth for CT artifact reduction
    11.
    发明授权
    Training a CNN with pseudo ground truth for CT artifact reduction 有权

    公开(公告)号：US11120551B2

    公开(公告)日：2021-09-14

    申请号：US16201186

    申请日：2018-11-27

    Applicant: Rensselaer Polytechnic Institute

    Inventor： Ge Wang ,  Lars Arne Gjesteby ,  Hongming Shan

    IPC: G06T7/00 ,  G06T5/00

    Abstract: Training a CNN with pseudo ground truth for CT artifact reduction is described. An estimated ground truth apparatus is configured to generate an estimated ground truth image based, at least in part, on an initial CT image that includes an artifact. Feature addition circuitry is configured to add a respective feature to each of a number, N, copies of the estimated ground truth image to create the number, N, initial training images. A computed tomography (CT) simulation circuitry is configured to generate a plurality of simulated training CT images based, at least in part, on at least some of the N initial training images. An artifact reduction circuitry is configured to generate a plurality of input training CT images based, at least in part, on the simulated training CT images. A CNN training circuitry is configured to train the CNN based, at least in part, on the input training CT images and based, at least in part, on the initial training images.

    DETECTION SCHEME FOR X-RAY SMALL ANGLE SCATTERING
    12.
    发明申请
    DETECTION SCHEME FOR X-RAY SMALL ANGLE SCATTERING 有权

    公开(公告)号：US20210080409A1

    公开(公告)日：2021-03-18

    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

    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 complementarity 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 gratings 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.

    3-D CONVOLUTIONAL AUTOENCODER FOR LOW-DOSE CT VIA TRANSFER LEARNING FROM A 2-D TRAINED NETWORK
    13.
    发明申请
    3-D CONVOLUTIONAL AUTOENCODER FOR LOW-DOSE CT VIA TRANSFER LEARNING FROM A 2-D TRAINED NETWORK 审中-公开

    公开(公告)号：US20200349449A1

    公开(公告)日：2020-11-05

    申请号：US16964388

    申请日：2019-01-24

    Applicant: RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Ge Wang ,  Hongming Shan ,  Wenxiang Cong

    IPC: G06N3/08 ,  G06T5/00 ,  G06T5/50 ,  G06N3/04

    Abstract: A 3-D convolutional autoencoder for low-dose CT via transfer learning from a 2-D trained network is described, A machine learning method for low dose computed tomography (LDCT) image correction is provided. The method includes training, by a training circuitry, a neural network (NN) based, at least in part, on two-dimensional (2-D) training data. The 2-D training data includes a plurality of 2-D training image pairs. Each 2-D image pair includes one training input image and one corresponding target output image. The training includes adjusting at least one of a plurality of 2-D weights based, at least in part, on an objective function. The method further includes refining, by the training circuitry, the NN based, at least in part, on three-dimensional (3-D) training data. The 3-D training data includes a plurality of 3-D training image pairs. Each 3-D training image pair includes a plurality of adjacent 2-D training input images and at least one corresponding target output image. The refining includes adjusting at least one of a plurality of 3-D weights based, at least in part, on the plurality of 2-D weights and based, at least in part, on the objective function. The plurality of 2-D weights includes the at least one adjusted 2-D weight.

    TRAINING A CNN WITH PSEUDO GROUND TRUTH FOR CT ARTIFACT REDUCTION
    16.
    发明申请
    TRAINING A CNN WITH PSEUDO GROUND TRUTH FOR CT ARTIFACT REDUCTION 审中-公开

    公开(公告)号：US20190164288A1

    公开(公告)日：2019-05-30

    申请号：US16201186

    申请日：2018-11-27

    Applicant: Rensselaer Polytechnic Institute

    Inventor： Ge Wang ,  Lars Arne Gjesteby ,  Hongming Shan

    IPC: G06T7/00 ,  G06T5/00

    Abstract: Training a CNN with pseudo ground truth for CT artifact reduction is described. An estimated ground truth apparatus is configured to generate an estimated ground truth image based, at least in part, on an initial CT image that includes an artifact. Feature addition circuitry is configured to add a respective feature to each of a number, N, copies of the estimated ground truth image to create the number, N, initial training images. A computed tomography (CT) simulation circuitry is configured to generate a plurality of simulated training CT images based, at least in part, on at least some of the N initial training images. An artifact reduction circuitry is configured to generate a plurality of input training CT images based, at least in part, on the simulated training CT images. A CNN training circuitry is configured to train the CNN based, at least in part, on the input training CT images and based, at least in part, on the initial training images.

    X-RAY DISSECTOGRAPHY
    18.
    发明申请
    X-RAY DISSECTOGRAPHY 有权

    公开(公告)号：US20250022210A1

    公开(公告)日：2025-01-16

    申请号：US18714185

    申请日：2022-11-29

    Applicant: RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Ge Wang ,  Chuang Niu

    IPC: G06T15/10 ,  G06T11/00 ,  G06V10/25 ,  G06V10/82

    Abstract: In one embodiment, there is provided a dissectography module for dissecting a two-dimensional (2D) radiograph. The dissectography module includes an input module, an intermediate module, and an output module. The input module is configured to receive a number K of 2D input radiographs, and to generate at least one three-dimensional (3D) input feature set, and K 2D input feature sets based, at least in part, on the K 2D input radiographs. The intermediate module is configured to generate a 3D intermediate feature set based, at least in part, on the at least one 3D input feature set. The output module is configured to generate output image data based, at least in part, on the K 2D input feature sets, and the 3D intermediate feature set. Dissecting corresponds to extracting a region of interest from the 2D input radiographs while suppressing one or more other structure(s).

    X-optogenetics / U-optogenetics
    19.
    发明授权
    X-optogenetics / U-optogenetics 有权

    公开(公告)号：US12172030B2

    公开(公告)日：2024-12-24

    申请号：US16943026

    申请日：2020-07-30

    Applicant: RENSSELAER POLYTECHNIC INSTITUTE

    Inventor： Ge Wang ,  Matthew Getzin ,  Rachel Berry ,  Lars Gjesteby

    IPC: A61N5/06 ,  A61K41/00

    Abstract: Methods and systems for performing optogenetics using X-rays or ultrasound waves are provided. Visible-light-emitting nanophosphors can be provided to a sample, and X-ray stimulation can be used to stimulate the nanophosphors to emit visible light. Alternatively, ultrasonic waves can be provided to the sample to cause sonoluminescence, also resulting in emission of visible light, and this can be aided by the use of a chemiluminescent agent present in the sample. The emitted light can trigger changes in proteins that modulate membrane potentials in neuronal cells.

    FEW-VIEW CT IMAGE RECONSTRUCTION SYSTEM
    20.
    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.

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