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.

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.

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.

公开(公告)号：US11382574B2

公开(公告)日：2022-07-12

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 ,  G01N23/041

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.

公开(公告)号：US11337663B2

公开(公告)日：2022-05-24

申请号：US16092393

申请日：2017-04-06

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.

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.

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.

公开(公告)号：US20200043205A1

公开(公告)日：2020-02-06

申请号：US16597250

申请日：2019-10-09

Applicant: University of Iowa Research Foundation ,  Shandong University ,  Shandong Provincial Chest Hospital ,  Rensselaer Polytechnic Institute

Inventor： Wenxiang Cong ,  Ye Yangbo ,  Ge Wang ,  Shuwei Mao ,  Yingmei Wang

IPC: G06T11/00 ,  A61B6/03

Abstract: The disclosed apparatus, systems and methods relate to a framelet-based iterative algorithm for polychromatic CT which can reconstruct two components using a single scan. The algorithm can have various steps including a scaled-gradient descent step of constant or variant step sizes; a non-negativity step; a soft thresholding step; and a color reconstruction step.

公开(公告)号：US10285659B2

公开(公告)日：2019-05-14

申请号：US14563727

申请日：2014-12-08

Applicant: Rensselaer Polytechnic Institute

Inventor： Ge Wang ,  Wenxiang Cong ,  Chao Wang ,  Fenglin Liu

IPC: A61B5/00 ,  A61B6/00 ,  A61B6/03 ,  A61B6/06

Abstract: Imaging methods and imaging systems are provided. Methods and systems of the subject invention can include the use of nanoparticles (for example, nanophosphors) within a sample to be imaged. X-ray engraving can be performed and/or X-ray excitation can be used to provide energy to the sample. Stimulation with infrared light, such as near-infrared (NIR) light, and/or optical multiplexing can be used to acquire tomographic data of the sample.

公开(公告)号：US11580410B2

公开(公告)日：2023-02-14

申请号：US16964388

申请日：2019-01-24

Applicant: RENSSELAER POLYTECHNIC INSTITUTE

Inventor： Ge Wang ,  Hongming Shan ,  Wenxiang Cong

IPC: G06T5/00 ,  G06T5/50 ,  G06N3/088 ,  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.