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.

公开(公告)号：US20240290014A1

公开(公告)日：2024-08-29

申请号：US18569764

申请日：2022-06-17

Applicant: RENSSELAER POLYTECHNIC INSTITUTE

Inventor： Ge Wang ,  Weiwen Wu ,  Chuang Niu

IPC: G06T11/00 ,  G06T3/4046 ,  G06T3/4053

CPC classification number: G06T11/006 ,  G06T3/4046 ,  G06T3/4053 ,  G06T2211/421 ,  G06T2211/441 ,  G06T2211/444

Abstract: In one embodiment, there is provided an apparatus for ultra-low-dose (ULD) computed tomography (CT) reconstruction. The apparatus includes a low dimensional estimation neural network, and a high dimensional refinement neural network. The low dimensional estimation neural network is configured to receive sparse sinogram data, and to reconstruct a low dimensional estimated image based, at least in part, on the sparse sinogram data. The high dimensional refinement neural network is configured to receive the sparse sinogram data and intermediate image data, and to reconstruct a relatively high resolution CT image data. The intermediate image data is related to the low dimensional estimated image.

公开(公告)号：US20240036135A1

公开(公告)日：2024-02-01

申请号：US18228064

申请日：2023-07-31

Applicant: Rensselaer Polytechnic Institute

Inventor： Xun Jia ,  Ge Wang ,  Mengzhou Li ,  Weiwen Wu ,  Wenxiang Cong ,  Yuting Peng ,  Jace Grandinetti

IPC: G01R33/48 ,  G01R33/385

CPC classification number: G01R33/4812 ,  G01R33/385

Abstract: In one embodiment, there is provided a magnetic resonance (MR) subsystem for magnetic resonance imaging (MRI). The MR subsystem includes a first magnet-coil assembly and a second magnet-coil assembly. The first magnet-coil assembly includes a first magnet structure and a first gradient coil. The second magnet-coil assembly includes a second magnet structure and a second gradient coil. The first magnet-coil assembly and the second magnet-coil assembly are separated by a gap. The gap is configured to facilitate transmission of an x-ray beam from an x-ray source to an x-ray detector. The x-ray source and the x-ray detector are included in a computed tomography (CT) subsystem.