公开(公告)号：US20240265591A1

公开(公告)日：2024-08-08

申请号：US18164372

申请日：2023-02-03

Applicant: GE Precision Healthcare LLC

Inventor： Bipul Das ,  Utkarsh Agrawal ,  Prasad Sudhakara Murthy ,  Risa Shigemasa ,  Kentaro Ogata ,  Yasuhiro Imai

IPC: G06T11/00 ,  G06T3/40 ,  G06V10/44 ,  G06V10/80

CPC classification number: G06T11/005 ,  G06T3/4053 ,  G06V10/44 ,  G06V10/806 ,  G06T2207/10081 ,  G06T2207/10116 ,  G06T2211/408

Abstract: Methods and systems are provided for interpolating missing views in dual-energy computed tomography data. In one example, a method includes obtaining a first sinogram missing a plurality of views and a second sinogram missing a different plurality of views, the first sinogram acquired with a first X-ray source energy during a scan and the second sinogram acquired with a second, different X-ray source energy during the scan; initializing each of the first sinogram and the second sinogram to form a first initialized sinogram and a second initialized sinogram; entering the first initialized sinogram and the second initialized sinogram into the same or different interpolation models trained to output a first filled sinogram based on the first initialized sinogram and output a second filled sinogram based on the second initialized sinogram; and reconstructing one or more images from the first filled sinogram and the second filled sinogram.

公开(公告)号：US20240062331A1

公开(公告)日：2024-02-22

申请号：US17821058

申请日：2022-08-19

Applicant: GE Precision Healthcare LLC

Inventor： Rajesh Langoju ,  Prasad Sudhakara Murthy ,  Utkarsh Agrawal ,  Risa Shigemasa ,  Bhushan Patil ,  Bipul Das ,  Yasuhiro Imai

IPC: G06T3/40 ,  G06T7/00 ,  G06T5/00 ,  G06T7/11 ,  G06N3/08

CPC classification number: G06T3/4046 ,  G06T7/0012 ,  G06T5/002 ,  G06T7/11 ,  G06N3/08

Abstract: Systems/techniques that facilitate deep learning robustness against display field of view (DFOV) variations are provided. In various embodiments, a system can access a deep learning neural network and a medical image. In various aspects, a first DFOV, and thus a first spatial resolution, on which the deep learning neural network is trained can fail to match a second DFOV, and thus a second spatial resolution, exhibited by the medical image. In various instances, the system can execute the deep learning neural network on a resampled version of the medical image, where the resampled version of the medical image can exhibit the first DFOV and thus the first spatial resolution. In various cases, the system can generate the resampled version of the medical image by up-sampling or down-sampling the medical image until it exhibits the first DFOV and thus the first spatial resolution.

公开(公告)号：US11823354B2

公开(公告)日：2023-11-21

申请号：US17225395

申请日：2021-04-08

Applicant: GE PRECISION HEALTHCARE LLC

Inventor： Bhushan Dayaram Patil ,  Rajesh Langoju ,  Utkarsh Agrawal ,  Bipul Das ,  Jiang Hsieh

IPC: G06T5/00 ,  G16H30/20 ,  G06N3/08 ,  G06T11/00

CPC classification number: G06T5/002 ,  G06N3/08 ,  G06T11/008 ,  G16H30/20 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2211/408

Abstract: A computer-implemented method for correcting artifacts in computed tomography data is provided. The method includes inputting a sinogram into a trained sinogram correction network, wherein the sinogram is missing a pixel value for at least one pixel. The method also includes processing the sinogram via one or more layers of the trained sinogram correction network, wherein processing the sinogram includes deriving complementary information from the sinogram and estimating the pixel value for the at least one pixel based on the complementary information. The method further includes outputting from the trained sinogram correction network a corrected sinogram having the estimated pixel value.